3D-printed concrete is a revolutionary technology that has been making waves in the construction industry over the last few years. Artificial intelligence has made 3D-printing even more effective. Kishor K talked about the benefits of Generative AI for 3D printing, but we wanted to talk in more detail specifically about 3D-printed concrete.
AI-driven 3D-printing is a process that involves using sophisticated machine learning algorithms to facilitate the printing of concrete structures layer by layer with a 3D printer, making it possible to create intricate details with high accuracy and precision. This technology is transforming the construction sector, making it possible to build faster, cheaper, and more sustainable buildings. Global companies spent over $17 billion on 3D-printing last year and new advances in AI should cause that market to grow even further.
As the world becomes more technologically advanced, 3D printing has emerged as a groundbreaking innovation with limitless possibilities. However, the process of creating intricate designs and models can be time-consuming and complex. This is where AI comes in, revolutionizing the world of 3D printing by providing invaluable assistance and streamlining the entire process.
AI technology has proven to be an indispensable tool for designers and engineers in the realm of 3D printing, including those creating concrete structures. By harnessing the power of artificial intelligence, professionals can now optimize their workflow, enhance precision, and unlock new levels of creativity.
This article will explore 3D-printed concrete, including its advantages, challenges, and potential applications with AI technology. We will also discuss the current state of the technology and what the future may hold for 3D-printed concrete. So, if you are curious about this innovative technology and what it could mean for the future of construction, read on to learn everything you need to know about 3D-printed concrete.
Before we delve into the benefits of using AI for these purposes, we want to provide an overview of 3D-printed concrete in general.
What is 3D Printed Concrete?
It is a 3D printing process that uses a mixture of special concrete and a binding agent to create a three-dimensional model. This model can then be used to construct buildings, bridges, and other structures.
3D-printed concrete is a relatively new technology that is still being developed and improved. It is a form of additive manufacturing, which means that the materials used to create the structure are added layer by layer. This process allows for more precise and intricate designs than traditional construction methods.
Advantages of 3D Printed Concrete:
3D printing of concrete has numerous benefits that make it one of the most sought-after construction techniques in the industry.
1. Cost Savings: One of the most significant advantages of 3D-printed concrete is its cost-efficiency. 3D printing machines require minimal setup and can produce complex shapes with ease. The cost of raw materials used in 3D printing is also relatively low compared to traditional construction methods. This makes 3D-printed concrete an attractive alternative for those who plan to build cost-effective structures.
2. Time Savings: 3D concrete printing is faster than traditional methods. This is because the process does not require molding or cutting of materials. Instead, the 3D printer can print the desired shape with just a few clicks. This eliminates the need for manual labor, which can save time and money.
3. Design Flexibility: 3D printing of concrete also offers a great degree of design flexibility. The 3D printer can be programmed to create any number of shapes, sizes, and textures. This makes it possible to create structures that are customized to the customer’s specifications. This is especially beneficial for architects who want to create unique layouts for their clients.
4. Durability: 3D-printed concrete is also highly durable. The structures created by 3D printers can withstand extreme temperatures and weather conditions. This means that they are not only aesthetically pleasing but also long-lasting.
5. Eco-Friendly: 3D printing of concrete is also a much eco-friendlier option than traditional methods. This is because the process does not require any hazardous chemicals or materials. This reduces the amount of waste created during the construction process, which helps to protect the environment.
Disadvantages of 3D Printed Concrete:
1. Cost: 3D printing with concrete is relatively expensive compared to traditional construction methods. This is due to the costs of the materials, the printer itself, and the technical expertise required for operation.
2. Limited Design Options: 3D printing with concrete can only print in fairly simple shapes due to the limitations of the printer.
3. Limited Materials: Currently, only concrete can be printed with 3D printing, so there are limited options for the material used in the construction.
4. Durability: The printing process itself can create weak points in the structure, as the printer is moving in a single direction and leaving behind layers of concrete that may not be as strong as a monolithic structure.
5. Time: 3D printing with concrete is a slow process, so the construction process takes much longer than traditional methods.
6. Environmental Impact: 3D printing with concrete produces a large amount of waste, as large amounts of concrete are used in the printing process, which can harm the environment.
Application of 3D Printed Concrete:
3D-printed concrete can be used to construct complex building elements that were previously impossible to create with traditional construction technologies. These elements include curved walls, arches, and other shapes that can be printed directly from a computer model.
3D-printed concrete can also be used to create lightweight structural elements that are strong enough to support large loads. This technology can be used to create complex architectural designs that are both aesthetically pleasing and structurally sound.
3D-printed concrete is also used to create customized precast components such as staircases, columns, and beams. 3D printing makes it possible to produce parts with complex shapes and sizes that are impossible with traditional construction methods.
3D-printed concrete can also be used to create custom-made elements for interior design. This technology allows designers to create unique, intricate patterns and shapes that would normally be too difficult or expensive to produce using traditional methods.
Best Concrete 3D Printers:
1. KODAK Portrait 3D Printer
The KODAK Portrait 3D Printer is a great choice for printing concrete 3D models. This printer uses a unique printing process that allows for precise layer-by-layer construction of the model, resulting in very accurate results. It features a large build area, a heated build plate, and a user-friendly interface. The Portrait 3D Printer supports a variety of materials, including concrete, plastic, and metal.
2. Builder Extreme 1500 Pro
The Builder Extreme 1500 Pro is excellent for 3D printing concrete models. This printer features a large build area, a heated build plate, and automated calibration. It is also compatible with a variety of materials, including concrete, plastic, and metal. Its open-source software allows users to customize the printing process for their specific project.
3. BigRep Pro
The BigRep Pro is a professional 3D printer designed for printing large concrete models. It features a large build area, a heated build plate, and automated calibration. The printer supports various materials, including concrete, plastic, and metal. With its open-source software, users can customize the printing process for their specific project.
4. XYZPrinting da Vinci Pro
The XYZPrinting da Vinci Pro is suitable for printing concrete models. This printer features a large build area, a heated build plate, and automated calibration. It is compatible with a variety of materials, including concrete, plastic, and metal. With its open-source software, users can customize the printing process for their specific project.
5. Airwolf 3D AXIOM
The Airwolf 3D AXIOM is a professional 3D printer designed for printing large concrete models. This printer features a large build area, a heated build plate, and automated calibration. It is also compatible with a variety of materials, including concrete, plastic, and metal. Its open-source software allows users to customize the printing process for their specific project.
3D Printed Concrete with the help of SelfCAD:
3D printed concrete is a revolutionary technology that enables the production of complex concrete structures in a fraction of the time it would typically take. This process is made easier with SelfCAD, a powerful 3D modeling and printing platform. The intuitive user interface and automated tools of this software allow users to quickly create 3D models of their desired structures, which can then be 3D printed using reinforced concrete.
SelfCAD’s online slicer also enables users to customize the thickness, layers, and other printing parameters of the concrete being used, thereby allowing for maximum customization and efficiency. Furthermore, the3D printing simulation feature of SelfCAD will enable users to preview and adjust their structures before they are printed. This feature allows users to optimize their print time, thus ensuring they get the most out of their 3D printer.
Finally, SelfCAD’s compatibility with various 3D printers and materials, including reinforced concrete, makes it the perfect tool for 3D printing concrete structures.
Transforming construction, one layer at a time!
3D-printed concrete is a safe and reliable way to build without compromising design or quality. It is an effective way to reduce labor costs, waste, and time. 3D printing technology in the construction industry has made it easier for architects and engineers to create complex designs and structures. With the advancement of 3D printing technology, 3D-printed concrete can be used for various applications, from homes and buildings to bridges and tunnels. 3D-printed concrete is a viable and sustainable solution for the construction industry. It provides an efficient and cost-effective way to build structures while reducing waste and labor costs.
What Are the Benefits of Using AI for 3D-Printing Concrete
One of the key advantages that AI brings to 3D printing is its ability to generate complex designs with remarkable efficiency. With AI algorithms analyzing vast amounts of data and patterns, designers can easily create intricate structures that were once considered impossible or incredibly time-consuming.
Furthermore, AI can assist in automating various aspects of the 3D printing process. From selecting suitable materials to optimizing print settings for maximum efficiency, AI algorithms ensure that every step is executed flawlessly. This not only saves valuable time but also minimizes errors and reduces material wastage.
Additionally, AI-powered software can simulate various scenarios before actual production takes place. By running virtual tests and simulations, designers can identify potential flaws or weaknesses in their designs without having to physically print them first. This not only saves resources but also enables them to iterate quickly and improve their designs before final production.
The role of AI in 3D printing goes beyond just enhancing design capabilities; it also enables collaboration among different stakeholders involved in the process. With cloud-based platforms powered by AI technology, designers from around the world can collaborate seamlessly on projects regardless of their physical location. This fosters a global community where knowledge sharing and innovation thrive.
AI Helps with Creating 3D-Printed Concrete Structures
In conclusion, AI has become an indispensable ally for professionals involved in 3D printing, including those creating concrete structures. Its ability to generate complex designs efficiently, automate processes, simulate scenarios, and facilitate collaboration has revolutionized the industry. As technology continues to advance, the role of AI in 3D printing will undoubtedly grow, opening up new possibilities and pushing the boundaries of what can be achieved in this exciting field.
AI has quickly revolutionized operational processes across a wide variety of industries and organizations. One field that is beginning to take advantage of the many benefits of utilizing AI technology in its operations is healthcare.
Specifically, AI tech is helping nurses thrive in a number of impactful, exciting, and groundbreaking ways. Developing a deeper understanding of how nurses incorporate AI technology into their work is critical to gaining a more thorough perspective on how healthcare is evolving in the modern age.
Here is the powerful collaboration between AI and nurses in our rapidly transforming healthcare landscape.
How Nurses Are Utilizing AI
To truly gauge the impact that AI is having and will have on healthcare, it can help to look at keyways nurses are using it. This is especially important for those who plan on becoming a nurse in the future. Here are some important ways that AI is currently being used by nurses to provide care to patients and optimize their workflows.
Improving and Accelerating Decision Making
For nurses, decision-making is a key part of most, if not all, workdays. From deciding how to respond to various situations to discerning which treatments are best suited for patients, the decisions that nurses make have an incredibly significant impact on the health outcomes of the patients they serve.
Sadly, the decision-making process isn’t always straightforward for nurses. As a result, they often have to take time to weigh different options and look over various healthcare records, which can be incredibly time-consuming.
However, AI technology is quickly changing this paradigm and offering itself as an effective solution to the problem of nurses needing to make consistently rapid and accurate decisions. Rather than nurses having to comb through countless patient records and other supplementary material to make health-related and clinical decisions, AI software has the power to generate accurate solutions virtually immediately.
In essence, these AI-powered software are able to quickly comb through large data sets and make health and treatment-related decisions based on the data it analyzes. While much of these software are currently housed on computers, they are starting to become available on mobile devices. As such, nurses are gaining greater and greater access to AI technology that is able to streamline their decision-making process and make them more efficient and accurate in their roles.
Minimizing Time Spent on Non-Care-Related Tasks
While the main duties of nurses center around treating patients and providing them with care, many other duties and responsibilities typically also fall on the shoulders of nurses. These include tasks such as filling out forms, repeatedly asking patients certain questions, and other menial administrative tasks.
Fortunately, new technology is helping nurses complete these tasks efficiently and with less effort. In particular, automation, robot nurses, and AI technology are speeding up the processes and freeing up more time for nurses.
One of the biggest benefits that comes from a reduction in extra tasks for nurses is more time and energy to focus on patients. In this way, nurses can provide patients with more attentive care and provide better patient experiences. Consequently, new innovations in automation and AI are helping to make healthcare more effective and enjoyable for scores of patients in a variety of medical environments.
Helping Nurses Determine Which Patients Are the Most in Need of Immediate Care
A challenge that has marred healthcare institutions for decades is a shortage of healthcare workers. As a result, many facilities across the country are typically understaffed, and nurses must treat a wide array of patients. Unfortunately, it can be difficult for nurses to effectively determine which patients are in the greatest need of care in these hectic and often high-stakes environments.
Today, however, AI has presented itself as an amazing tool for determining which patients are in the greatest need of immediate care. This process helps ensure that nurses are utilizing their time effectively and giving patients who are most in need of immediate treatment the care they require.
Essentially, AI software is able to determine which patients’ nurses should be treated by engaging in a data-analysis process. These softwares can rapidly compare various patient data to scores of records from previous patients. Ultimately, this allows the software to provide insights into which patients should be treated first.
Though this may seem like a somewhat inconsequential innovation in healthcare, the truth is that the effects have the potential to be profound. In fact, this type of AI-powered analysis can help nurses save lives by pointing out patients with severe health problems that may not be obvious on the surface. As such, AI is aiding nurses in profound ways and helping more patients in need receive the immediate treatment that they require.
AI is Ushering Nursing into a New Age of Efficiency, Accuracy, and Optimization
While there have been numerous innovations in healthcare over the last several decades, AI is proving itself to be one of the most important. From helping nurses become more efficient to aiding in the decision-making process, AI is helping healthcare workers provide patients with better care.
In the near future, it’s more than likely that AI technology will become a normalized part of nursing roles and all nurses will be trained to utilize this incredibly powerful technology.
[Alt Text: Eric Blue, CEO of Nevly, provides access to resources so that others can avoid getting trapped in debt and other common mistakes early on in their financial journey.
We have written extensively about the benefits of using artificial intelligence in the financial sector. Most of our discussions have centered around the use of AI in major financial institutions such as insurance companies, hedge fund management firms and financial advisory groups. This is a major topic of discussion, since the value of the AI in fintech market is estimated to be worth $39 billion .
Embarking on your financial journey can be exciting and full of potential, but it’s not without its pitfalls. It’s crucial to be aware of the common financial mistakes people make early on and learn how to avoid them. Eric Blue, the founder of Nevly, a financial technology company, has shared his insights on the top 10 financial mistakes to avoid in order to achieve a successful and stress-free financial future.
As you begin your financial journey, it’s essential to approach it with a sense of self-awareness and responsibility. Understanding the impact of your financial decisions and being proactive about managing your money can make all the difference in the long run. Once you appreciate the common mistakes people make with personal financial decisions, you will realize the importance of using artificial intelligence to make better decisions.
Managing personal finances is becoming more complex with various investment options, debt strategies, and budgeting tools. AI is now used to assist people in improving their financial literacy and managing their finances better.
This article explores the benefits and limitations of AI in personal finance management and highlights tools and solutions for achieving financial goals. Personal finance management involves tracking income, expenses, and investments.
AI can aid in budgeting, debt management, and retirement planning. Successful personal finance management requires discipline and a deep understanding of financial concepts.
Eric Blue’s experience in the world of finance has given him a unique perspective on the challenges people face and the strategies they can employ to overcome these obstacles. By learning from the experiences of others, you can better navigate your own financial path and lay the groundwork for a stable and prosperous future.
Another crucial aspect of a successful financial journey is having access to the right tools and resources. Nevly, under Eric Blue’s leadership, aims to provide individuals with the tools they need to take control of their financial lives. With a focus on innovation and user-centric design, Nevly’s financial products, such as the ARLO app and Nevly Money, are designed to make managing your finances more accessible, efficient, and enjoyable. By leveraging these resources, you can optimize your financial plan and stay on track toward achieving your goals.
Finally, it’s important to recognize that everyone’s financial journey is unique. What works for one person might not be the best approach for someone else, and your financial priorities will change as you progress through different stages of life.
Eric Blue’s insights on avoiding common financial mistakes early on can help you build a strong foundation for your financial future, but it’s essential to reassess your plan and make adjustments as needed continuously. You can do this better if you take advantage of the right AI tools. By staying adaptable and committed to your economic well-being, you can confidently navigate the twists and turns of your financial journey and ultimately achieve lasting success.
Here are ten of the biggest mistakes that can be corrected with artificial intelligence.
1. Failing to create a budget
One of the most common mistakes people make is not creating a budget. According to a 2019 survey by Debt.com, 33% of Americans don’t maintain a budget, but since the pandemic, that number has decreased.
A budget is the foundation of any sound financial plan, as it helps you understand where your money is going and identify areas where you can save or adjust spending. To avoid this mistake, create a monthly budget that accounts for all your income and expenses, and track your spending to ensure you’re sticking to your plan.
Creating a budget not only helps you manage your money more effectively but it also encourages the development of healthy financial habits. By consistently monitoring your income and expenses, you’ll become more mindful of your spending choices and better equipped to make informed decisions. Furthermore, a well-maintained budget can help you identify and address any financial issues before they escalate, allowing you to adjust and maintain control over your financial well-being.
Technology has made the process of budgeting even more convenient and user-friendly, with various budgeting apps and tools available to help you stay organized and on track. Utilizing these resources can simplify the budgeting process, making it easier to maintain and update your financial plan regularly.
By committing to the practice of budgeting, you’re taking an essential step toward financial success and building a solid foundation for a prosperous future. Remember, a well-structured budget is more than just a way to manage your money; it’s a powerful tool for achieving your financial goals and aspirations.
There are a number of personal financial tools that use AI technology to improve the budgeting process. Cleo, Eva Money and MintZip are three of the best tools that help consumers use AI technology to make better budgets.
These tools use a variety of AI algorithms to help families set realistic expectations when it comes to budgeting for major expenses. These algorithms are able to account for inflation, changes caused by cost of living differences after moving and other variables. The machine learning algorithms provide much more nuanced financial insights and most people could ever make on their own.
2. Not establishing an emergency fund
An emergency fund is a financial safety net that can help you cover unexpected expenses, such as car repairs, medical bills, or job loss, without resorting to high-interest debt. Unfortunately, a 2023 Bankrate survey found that 36% of Americans have more credit card debt than emergency savings. To avoid this mistake, Eric Blue and other finance experts say to aim to save at least three to six months’ worth of living expenses in a separate, easily accessible savings account.
Establishing an emergency fund not only provides you with financial security during unforeseen circumstances but also contributes to your overall peace of mind. Knowing that you have a safety net in place can alleviate stress and enable you to focus on other aspects of your financial journey, such as paying off debt, investing, or saving for long-term goals.
Moreover, having an emergency fund in place can help you avoid the trap of relying on credit cards or loans to cover unexpected expenses, which can lead to a vicious cycle of debt. By prioritizing creating and maintaining an emergency fund, you’re investing in your financial stability and taking a proactive step toward ensuring your long-term financial success.
Again, AI-driven budgeting tools help people better prepare for these kinds of emergencies. They Use predictive analytics technology to better anticipate possible emergencies and the expected costs associated with them. This helps people make more informed financial planning decisions then they could make simply relying on general rules like saving three months worth of income.
3. Overusing credit cards
[Alt Text: Eric Blue advises consumers to avoid overusing credit cards early on in their financial journey, as it is one of the common financial pitfalls of many Americans.]
Credit cards can be a helpful financial tool when used responsibly, but they can also lead to a cycle of debt if not managed properly. According to the Federal Reserve’s latest quarterly report on household debt and credit, total household debt rose by $148 billion, or 0.9 percent, to $17.05 trillion in the first quarter of 2023. To avoid this mistake, only use credit cards for planned purchases, and always pay off your balance in full each month to avoid interest charges.
Credit cards offer various benefits, such as building credit history, earning rewards, and providing a convenient payment method for everyday transactions. However, it’s essential to use them wisely to avoid falling into the trap of accumulating high-interest debt. By using credit cards for planned purchases and budgeting appropriately, you can take advantage of these benefits without compromising your financial health.
Paying off your credit card balance in full each month is a crucial habit to develop, as it not only saves you from paying interest charges but also helps maintain and improve your credit score. A lower credit utilization ratio can positively impact your credit score, making it easier for you to qualify for loans and secure favorable interest rates in the future.
Lastly, it’s essential to be selective when choosing a credit card that aligns with your financial goals and spending habits. Carefully consider factors such as interest rates, fees, and rewards programs before committing to a specific card. By being strategic with your credit card usage and diligently managing your payments, you can successfully leverage credit cards as a valuable financial tool while avoiding the pitfalls of debt.
AI technology can also help people get out of credit card debt. NewsWeek has an article titled How to pay off Credit Card Debt Faster Using Artificial Intelligence that provides an overview of some AI-driven apps that can help consumers trying to get out of credit card debt.
One of the AI tools that the article talks about is called Tally. NewsWeek writes that “Tally uses it’s computing power to analyze APRs, credit card balances to offer a tentative debt-free date, provided that you make the indicated monthly payments.”
You will also want to use AI to help choose the right credit cards. We have an article on this topic. This can help you avoid using a card that is likely to get you into debt due to high interest rates or onerous late fees.
4. Ignoring your credit score
Your credit score plays a crucial role in your financial life, as it affects your ability to obtain loans, credit cards, and even housing. Yet, a 2020 Consumer Federation of America survey found that only 57% of respondents knew their credit score. To avoid this mistake, Eric Blue suggests you regularly monitor your credit score and take steps to improve it, such as paying bills on time and keeping your credit utilization low.
Staying informed about your credit score is an essential aspect of financial literacy, as it allows you to understand your current credit standing better and identify areas for improvement. Several free online services offer access to your credit score, making it easy to monitor any changes or discrepancies. By keeping a close eye on your credit score, you can proactively address any issues that might arise and maintain a healthy credit profile.
Improving your credit score involves a combination of responsible financial habits and sound credit management strategies. Consistently paying your bills on time, maintaining a low credit utilization ratio, and avoiding excessive applications for new credit are all effective ways to boost your credit score; Newly’s ARLO app can help you do this and more.
Additionally, ensuring that your credit report is accurate by regularly reviewing it and disputing any errors can further contribute to a strong credit profile. By taking these steps and staying committed to nurturing your credit score, you’ll be better positioned to take advantage of financial opportunities and secure a brighter financial future.
AI technology can also help people that are trying to improve their credit scores. A number of personal financial apps like Mint and Credit Sesame have a variety of artificial intelligence algorithms that help people make a number of better financial decisions.
These algorithms can do a lot to help people trying to get out of debt, which is one of the reasons people have poor credit scores. They can also help people better track payment deadlines so they can avoid hurting their credit score by making missed payments.
5. Failing to save for retirement
Saving for retirement is essential for securing your financial future, but many people neglect this aspect of their financial plan. A 2020 survey by the Transamerica Center for Retirement Studies revealed that 45% of American workers have no retirement savings. To avoid this mistake, start saving for retirement as early as possible and take advantage of employer-sponsored retirement plans and tax-advantaged accounts, such as a 401(k) or IRA.
Starting to save for retirement early in your career allows you to maximize the power of compounding interest, which can significantly increase your retirement savings over time. Even modest contributions to your retirement account can add up and grow exponentially as the years go by.
By participating in employer-sponsored retirement plans, you can also benefit from employer matching contributions, which essentially equates to free money towards your retirement savings. Utilizing tax-advantaged accounts like a 401(k) or IRA can further optimize your retirement savings strategy by providing tax benefits that encourage long-term saving. By making retirement savings a priority and leveraging the available tools and resources, you’ll be well on your way to securing a comfortable and financially stable retirement.
There are many ways that artificial intelligence can help you better prepare for retirement. AI technology can use predictive analytics algorithms to anticipate future financial needs.
Some of these tools use very advanced algorithms that account for a variety of different variables, including expected life expectancy based on health conditions, cost of living in your area and anticipated lifestyle after retiring.
6. Not having financial goals
Without clear financial goals, it’s difficult to stay motivated and focused on your financial journey. A 2018 study by Charles Schwab found that individuals with a written financial plan were more likely to save, invest, and feel confident about their financial future. To avoid this mistake, set short-term and long-term financial goals, such as paying off debt, saving for a down payment on a home, or funding your child’s education, and create a plan to achieve them.
Setting clear financial goals is a critical component of any successful financial plan, as it provides direction and a sense of purpose. By establishing both short-term and long-term objectives, you can create a roadmap that guides your financial decisions and helps you stay focused on your priorities. This, in turn, enables you to allocate your resources more effectively and make progress towards your goals at a steady pace.
Eric Blue knows the importance of this step in developing a secure financial future, that’s why he developed his business, Nevly, to help others achieve successful financial planning. Developing a plan to achieve your financial goals involves breaking them down into actionable steps and identifying the necessary strategies and tools to reach them.
By creating a detailed plan, you’re more likely to stay on track and maintain the discipline required to follow through with your financial commitments. This sense of organization and structure can significantly contribute to your overall financial success.
Monitoring and adjusting your financial goals as needed is equally important, as your circumstances and priorities may change over time. Regularly reviewing your progress and updating your plan accordingly ensures that your goals remain relevant and attainable. By setting clear financial goals and diligently working towards them, you can cultivate a sense of accomplishment and confidence in your financial future.
7. Taking on too much debt
Debt can quickly spiral out of control if not managed carefully. According to the Federal Reserve, in a fourth-quarter report of 2022, the total U.S. household debt was $16.90 trillion. To avoid this mistake, Eric Blue encourages consumers only to take on debt for essential purchases, such as a home or education, and prioritize paying on your loans as quickly as possible. Additionally, avoid high-interest debt, such as payday loans or credit card debt, which can be difficult to pay off and can negatively impact your financial health.
Eric Blue, the founder of Nevly, emphasizes the importance of being mindful when taking on debt and making informed decisions about the types of debt you’re willing to incur. By focusing on essential purchases and avoiding high-interest debt, you can minimize the potential burden on your financial health and maintain a more sustainable debt-to-income ratio. Eric Blue’s advice on managing debt responsibly can help you better navigate your financial journey and avoid the pitfalls hindering your progress.
Creating a strategic debt repayment plan is another key component in managing debt effectively. Prioritize paying off high-interest debt first, as this can save you money in the long run and help you become debt-free more quickly. Consider implementing debt repayment strategies, such as the debt snowball or debt avalanche methods, to streamline the process and stay motivated.
By following Eric Blue’s guidance and using resources like Nevly Money and the ARLO app, taking a proactive approach to debt management, you can achieve greater financial stability and work towards a debt-free future with confidence.
This is another area where AI can help you make better financial decisions. You can use a variety of AI apps that can recommend certain financial goals that you may not have otherwise considered.
8. Neglecting to invest
Investing is a powerful tool for growing your wealth over time, but many people shy away from it due to a lack of knowledge or fear of risk. According to a 2020 Gallup poll, only 55% of Americans reported owning stocks. To avoid this mistake, educate yourself about investing basics and consider working with a financial advisor or using the ARLO app to help you build a diversified investment portfolio that aligns with your goals and risk tolerance.
Eric Blue, the founder of Nevly, recognizes the importance of investing as a means of securing long-term financial growth and stability. He encourages individuals to overcome their apprehensions and gain a solid understanding of investing fundamentals. By doing so, you’ll be better equipped to make informed decisions that can contribute to your overall financial well-being. Eric Blue’s insights on the value of investing emphasize the need for individuals to embrace this essential aspect of personal finance and leverage its potential for wealth creation.
For those who may feel overwhelmed or uncertain about where to begin, working with a financial advisor or utilizing a robo-advisor can be an excellent starting point. These professionals and platforms can provide personalized guidance and help you develop a diversified investment portfolio tailored to your financial goals and risk tolerance. With the expertise of a financial advisor or the convenience of a robo-advisor, you can benefit from professional insights and support as you embark on your investing journey.
Eric Blue’s emphasis on the importance of investing serves as a reminder that taking calculated risks and diversifying your financial portfolio can lead to significant long-term benefits. By educating yourself, seeking professional guidance, and being proactive in your investment strategy, you can work towards achieving your financial goals and setting yourself up for a secure and prosperous future.
Of course, It is important to make sure that you invest in the right assets. Some People make the mistake of investing in assets that have a lot of glamor attached to them, even though they are not likely to be profitable long term.
AI technology can be very beneficial for people that want to make better investing decisions. These tools can conduct the best valuation calculations to determine The long term profitability of various assets.
9. Not having insurance
Insurance is essential for protecting your financial well-being in the event of unexpected events, such as accidents, illness, or natural disasters. However, a 2021 Policygenius survey found that 33% of Americans don’t have life insurance, and 16% don’t have health insurance. To avoid this mistake, ensure you have adequate insurance coverage for your needs, including health, life, disability, and property insurance.
Eric Blue underscores the significance of insurance as a critical component of a comprehensive financial plan. He stresses the importance of safeguarding your financial well-being and that of your loved ones by obtaining sufficient insurance coverage in various areas, such as health, life, disability, and property insurance. By following Eric Blue’s advice and proactively securing appropriate insurance policies, you can effectively mitigate potential risks, navigate unforeseen events more efficiently, and maintain financial stability for yourself and your family.
AI can also help you anticipate potential problems that you may need to use insurance to prevent. This can help you choose the best insurance plan to cover unexpected problems.
10. Failing to review and adjust your financial plan
Your financial situation and goals can change over time, so it’s important to regularly review and adjust your financial plan accordingly. Neglecting to do so can lead to missed opportunities or setbacks on your financial journey. To avoid this mistake, schedule regular financial check-ins, at least annually, to assess your progress, update your goals, and make any necessary adjustments to your plan.
Eric Blue, the founder of Nevly, emphasizes the importance of adaptability and regular reassessment in managing your personal finances. He acknowledges that life is dynamic, and your financial situation and objectives are bound to evolve over time.
By staying attuned to these changes and adjusting your financial plan accordingly, you can better align your strategies with your current circumstances and aspirations. Eric Blue’s guidance on the necessity of periodic financial check-ins can help you stay on track, seize new opportunities, and avoid potential setbacks as you progress along your financial journey.
Scheduling regular financial check-ins, as suggested by Eric Blue, allows you to take stock of your current financial standing and identify areas that may need reevaluation. These check-ins can help you determine whether your existing budget still meets your needs, whether your investments continue to align with your risk tolerance, or if your insurance coverage remains adequate. By making a conscious effort to reassess your financial plan, you can ensure that you are consistently working towards your goals and making the most of your resources.
In addition to monitoring your financial progress, Eric Blue also highlights the value of being proactive in seeking out new information and resources. This can involve staying up-to-date on changes in tax laws, exploring new investment opportunities, or taking advantage of advancements in financial technology, such as Nevly’s products. By remaining engaged and informed, you can make educated decisions and take advantage of emerging opportunities to enhance your financial success and stability.
AI Technology Can Help People Avoid Common Financial Mistakes
In conclusion, avoiding these common financial mistakes early in your financial journey can set you on the path to success and help you achieve your goals. By learning from the expertise of industry leaders like Eric Blue and taking advantage of innovative financial tools like those offered by Nevly, you can navigate your financial journey with confidence and create a strong foundation for a secure and prosperous future.
About Eric Blue:
Eric Blue, the founder of Nevly, is a seasoned entrepreneur and fintech expert with a passion for helping others achieve financial success. Drawing from his own experiences and struggles with finances as a child, Eric has dedicated his career to developing innovative financial solutions that empower individuals to take control of their money and build a secure financial future.
Through Nevly and its cutting-edge products, Blue is committed to democratizing access to financial planning tools and resources, ensuring that everyone has the opportunity to achieve their financial goals.
You can now query Firestore using the OR operator. Your application may often need to compare one or more fields with two or more values and select the documents that match at least one of the values. This is an extension to the IN operator which could be used to compare multiple values with the same field.
For example, if you’re searching for information about cats and dogs, you could use the OR operator to combine the two search terms into a single query:
‘cats = black OR dogs = brown’
This would return results that include information about both black cats and brown dogs.
Firestore now supports OR queries via server and client SDKs, with streaming and offline compatibility.
Use Cases
Support for OR operator now enables you to utilize Firestore for building applications which need to filter one or more fields with two or more values using any of our existing query constructs.
Examples
Let’s say you are running an employee management portal on Firestore and you want the ability to filter on a number of criteria like Title, Organization, Location, Tenure etc., you can now easily utilize OR to be able to do it.
Now, say you want to query for performance reviews for a given employee that occurred within the last two years, or where the given rating was higher than 4.
You can nest multiple ORs to accomplish such queries as follows :
Nested ORs can be useful when you need to check multiple conditions, but they can also be difficult to read and understand. It is important to use them sparingly and to only use them when necessary.
OR operator can also be used on all our existing query constructs like querying across Collection Groups, or in combination with existing operators like COUNT().
Let’s take an example of Collection Groups called Landmarks, you can now use OR operator to filter collections within the group
In our work on the Big Data solutions architecture, BigQuery, and Dataproc teams, we talk to a lot of customers who are interested in migrating all or part of their data warehouse from Apache Hive to BigQuery, but who have hit some speed bumps along the way. To help, we are proud to announce the public GA release of the Hive-BigQuery Connector.
With this open-source connector, you now can let Apache Hive workloads read and write to BigQuery and BigLake tables. The underlying data can be stored either in BigQuery native storage or in open-source data formats on Cloud Storage. Whether you’re fully migrating from Apache Hive to BigQuery or you want both systems to coexist and interact together, this new connector covers a wide range of use cases.
What is the Hive-BigQuery Connector?
If you’ve run Hadoop or Spark workloads on Google Cloud, you should already be familiar with the Cloud Storage Connector and the Apache Spark SQL connector for BigQuery. The former implements the Hadoop Compatible File System (HCFS) API that lets you store and access data files in Cloud Storage, Google Cloud’s highly-scalable and highly-available object storage service. The latter implements the Spark SQL Data Source API to allow reading BigQuery tables into Spark’s dataframes and writing DataFrames back into BigQuery.
Similarly, the Hive-BigQuery connector implements the Hive StorageHandler API to allow Hive workloads to integrate with BigQuery and BigLake tables. While Hive’s execution engine still handles all compute operations such as aggregates and joins, the connector manages all interactions with the data layer in BigQuery, whether the underlying data is stored in BigQuery native storage or in Cloud Storage buckets via a BigLake connection.
The following diagram illustrates how the Hive-BigQuery connector fits in the architecture:
For its part, Apache Hive is one of the most popular open-source data warehouses, and provides an SQL-like interface to query data stored in various databases and file systems that integrate with Apache Hadoop. Over time Hive evolved from using HDFS on-premises as its exclusive data storage layer, to using cloud storage services. And now, thanks to this new connector, Hive integrates with native storage solutions like BigQuery, simplifying migration.
Migrating a data warehouse to the cloud is a complex process, but it can offer significant benefits, including:
Reduced costs: pay only for the resources you use
Increased scalability: easily scale up or down to meet your evolving needs
Improved reliability: leverage redundant, highly-available systems
Enhanced security: encrypt data in transit and at rest and implement granular access control
Expanded capabilities: integrate directly or indirectly with a vast array of Google Cloud native tools and solutions such as:
Dataflow to process data at scale in batch or streaming mode
Vertex AI to build, deploy, and scale machine learning models
and many, many more
Google Cloud offers the BigQuery Migration Service as a comprehensive solution to accelerate migration from your Hive data warehouse to BigQuery. It includes free-to-use tools that help you with each phase of migration, including assessment and planning, data transfer, and data validation. Two of those tools, the BigQuery batch SQL translator and interactive SQL translator, enable you to translate your Hive queries to BigQuery’s own ANSI-compliant SQL syntax so you can then run those queries natively with BigQuery execution engine.
The new Hive-BigQuery connector offers one additional option: You can keep your original queries in their HiveQL dialect, continue to run those queries with the Hive execution engine on your cluster, but let those queries access data migrated to BigQuery and BigLake tables.
Here’s how the Connector assisted Flipkart’s data lake migration to Google Cloud:
“Flipkart places great importance on interoperability with open source technologies as a part of their reliance on and contribution to the open source community. The Hive-BigQuery Connector has played a crucial role in enabling queries on BigQuery data from Hive, as Hive is the primary query engine on our data lake. This integration has provided Flipkart the flexibility to utilize fast query engines like BigQuery without the need for data duplication or silos across various data stores.” – Venkata Ramana Gollamudi, Principal Architect, Flipkart; Apache Committer
Use cases
The Hive-BigQuery Connector can help you in at least the following core use cases:
Ensure continuity of operations during a wholesale migration. Imagine you decide to move your entire Hive data warehouse to BigQuery and you intend to eventually translate all of your existing Hive queries to BigQuery’s SQL dialect. You expect that the migration will take a significant amount of time due to the sheer size of your data warehouse and the large number of connected applications. You need to ensure smooth continuity of operations during the migration period. To do this, you can first move your data to BigQuery then let your original Hive queries access that data through the Connector while you gradually translate them to BigQuery’s own ANSI-compliant SQL dialect. Once the migration is complete, you can use BigQuery exclusively and retire Hive altogether.
Use BigQuery for some, but not all, data warehouse needs. With the Hive-BigQuery Connector, you can choose to continue using Hive for most workloads and only use BigQuery for certain workloads that you think would benefit from specific BigQuery features like, for example, BI Engine or BigQuery ML. For this use case, you can use the Connector to unify the two environments by letting Hive join its own tables with the tables managed by BigQuery.
Maintain a full open-source software (OSS) stack. Let’s say you want to avoid any potential vendor lock-in and decide to continue using a full OSS stack for your data warehouse. You migrate your data in its original OSS format (e.g. Avro, Parquet or ORC) to Cloud Storage and continue using Hive to execute and process your queries in Hive’s own SQL dialect. For this use case, you can use the Connector to augment your OSS stack foundation by leveraging some BigLake and BigQuery features such as metadata caching for query performance, or Data Loss Prevention, column-level access control, and dynamic data masking for security and governance at scale.
Features
The Connector’s public preview release already ships with many features, including:
Running queries with MapReduce and Tez execution engines
Creating and deleting BigQuery tables from Hive
Joining BigQuery and BigLake tables with Hive tables
Direct writes using the BigQuery Storage Write API in pending mode. Use this method for workload that requires low write latency like near real-time dashboards with short refresh time windows.
Indirect writes by staging temporary Avro files to Cloud Storage then loading those files into the destination table using the Load Job API. This method is cheaper than the direct method, since BigQuery load jobs are free. However, it is slower, so it should only be used for workloads that aren’t time critical.
Accessing BigQuery time-partitioned and clustered tables. Here’s an example defining the relation between a Hive table and a table that is natively partitioned and clustered in BigQuery:
code_block[StructValue([(u’code’, u”CREATE TABLE my_hive_table (int_val BIGINT, text STRING, ts TIMESTAMP)rnSTORED BY ‘com.google.cloud.hive.bigquery.connector.BigQueryStorageHandler’rnTBLPROPERTIES (rn ‘bq.table’=’myproject.mydataset.mytable’,rn ‘bq.time.partition.field’=’ts’,rn ‘bq.time.partition.type’=’MONTH’,rn ‘bq.clustered.fields’=’int_val,text’rn);”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e48dc4cc3d0>)])]
Column pruning to avoid retrieving unnecessary columns from the data layer
Predicate pushdowns to pre-filter data rows at the BigQuery storage layer. This can drastically reduce the amount of data traversing the network and therefore improve overall query performance.
Automatic conversion of Hive data types to BigQuery data types
To get started, see the documentation on how to install and configure the connector on your Hive cluster.
We’re continuously adding new features and making improvements, and your feedback is welcome! Send us your thoughts and feature requests either by contacting your Google Cloud sales representative or opening a Github issue. We are excited to see all the innovative ways you will use this new Connector.
Thanks to contributors to the design and development of this connector, in no particular order: David Rabinowitz, Yi Zhang, Dagang Wei, Vinay Londhe, Roderick Yao, Kartik Malik, Guruprasad S, Nikunj Bhartia, Gaurav Khandelwal, Vijay Dulipala, Savitha Sethuram, Palak Mishra, Akanksha Singh, Prathap Reddy.
Scheduling reports in Looker Studio helps teams predictably and reliably distribute insights to business users in the flow of work and workflows where they spend their time. This helps to foster a data culture, making information more actionable and contextual, and enabling cross-team collaboration. Today, we are introducing two new capabilities in Looker Studio scheduling: the ability to apply distinct filter conditions to reports, and to create and manage multiple schedules within a report. These enhancements will help data teams distribute insights efficiently and make them even more targeted.
Schedule Filters lets you apply specific filter values while creating or editing a schedule, ensuring recipients see the intended filtered view in their delivered report. The Filters section of the Schedule delivery window shows all filters applied to the report along with their default values. You can now modify the filter values for a schedule and they will be applied to the schedule delivery. For example, you might schedule a report to multiple regional teams and apply location- or region-based filters for different sets of recipients.
Schedule Filters is now generally available to all Looker Studio users.
Multiple schedules lets you create multiple schedules within a Looker Studio report and manage them in a single view. You can now schedule and deliver the same report to different recipients, based on a variety of criteria such as delivery cadence, filter values and more. For example, a retail store manager might want to create different email schedules for a category performance report to different suppliers filtered based on their product category.
For an already created schedule, you can also now send a one-time instant delivery using the Send now option from the list view of schedules. You can also toggle the state of a schedule between active and inactive if you wish to temporarily pause delivery.
Multiple schedules feature is exclusively available for Looker Studio Pro users
Multiple schedules is now available for all Looker Studio Pro users
Transform how your team effectively distributes insights and makes it actionable with Looker Studio Pro. For more information, including features, pricing and trial options, learn more.
Data and AI provide organizations with endless opportunities to personalize customer experiences, improve supply chain efficiency, reduce operating costs, and drive incremental revenue. However, growing volumes and types of data, disparate analytical tools, limited machine learning skilled resources, and several other challenges all increase the time it takes to see value from data and AI investments.
Google’s Data and AI Cloud is well positioned to help customers navigate these challenges with a unified analytics offering, an open data ecosystem, and built-in machine learning intelligence. Today, we are excited to announce that Forrester Research has recognized Google as a Leader in The Forrester Wave™: Cloud Data Warehouses, Q2 2023. We believe this is a testament to our vision and strong track record of delivering continuous product innovation.
Download the complimentary report: The Forrester Wave™: Cloud Data Warehouses, Q2 2023.
In this report, Forrester evaluated 15 cloud data warehouse (CDW) providers against pre-defined criteria, evaluating them on their current offerings and strategy. In addition to being named a Leader, Google received the highest possible score for 10 different evaluation criteria, including roadmap, partner ecosystem, machine learning (ML) and data science optimization, performance optimization, built-in streaming, in-database analytics and data lake integration.
Google has a superior roadmap emphasizing a more open and intelligent CDW, broader data connectivity and streaming, data quality, distributed data governance, and multi-cloud. …Google is an excellent fit for enterprises that need a CDW with high-end performance and scale to support large, complex analytical workloads, including real-time analytics, AI/ML-driven, and data collaboration workloads. The Forrester Wave™: Cloud Data Warehouses, Q2 2023
Tens of thousands of enterprise customers across industries accelerate digital transformation with BigQuery, our cloud data warehouse. ANZ bank uses it to provide high-quality data across the organization to reduce financial crime. BBC relies on BigQuery to provide a reliable and stable service without worrying about scale. And BigCommerce gathers, analyzes and acts on retail data with BigQuery’s help.
Unified data management
Google Cloud provides a unified data platform that allows organizations to manage every stage of the data lifecycle — from running operational databases for applications, to managing analytical workloads across data warehouses and data lakes, to data-driven decision-making, and to AI and ML. How we’ve architected our platform is unique and enables customers to bring together their data, people, and workloads.
“BigQuery integrates with Cloud BigTable, Google Cloud Storage, Cloud AI Notebooks, Dataplex, Looker and Google Sheets, allowing users to join data across various systems.”
To unify data pipeline management, we recently announced the general availability of Dataform, which helps data engineers and data analysts of all skill levels build production-grade SQL pipelines in BigQuery while following software engineering best practices such as version control with Git, CI/CD, and code lifecycle management.
These integrations increase productivity and bring down costs. To help you further manage data cloud costs, we launched BigQuery editions with three pricing tiers — Standard, Enterprise and Enterprise Plus — for you to choose from, with the ability to mix and match for the right price-performance based on your individual workload needs.
BigQuery’s flexible new pricing options and autoscaling capabilities are driving results for customers. For example, Lytics, a leading customer data platform (CDP), has seen 15% operational improvement and a 20% decrease in costs with BigQuery.
BigQuery editions feature two innovations. First, compute capacity autoscaling adds fine-grained compute resources in real-time to match the needs of your workload demands, and help you only pay for the compute capacity you use. Second, physical bytes billing pricing allows you to only pay for data storage after it’s been highly compressed. With compressed storage pricing, you can reduce your storage costs even while increasing your data footprint.
The backbone of these innovations is BigQuery’s unique architecture, which separates storage and compute, allowing BigQuery to scale both storage and compute independently, based on demand.
Built-in intelligence
BigQuery is more than just a traditional data warehouse. BigQuery ML empowers data analysts to use machine learning through existing SQL tools and skills. It saw over 200% growth in usage in 2022 as customers ran hundreds of millions of prediction and training queries.
“Reference customers like Google’s serverless architecture, high-end scale and performance, geospatial and robust AI/ML capabilities, and support for broad analytical use cases.“
We continue to bring the latest advancements in AI technology to make our data cloud services even more intelligent. The new BigQuery ML inference engine, for example, allows you to run predictions not only with popular models formats directly in BigQuery, but also using remotely hosted models and Google’s state-of-the-art pretrained models.
Then there’s BigLake, which lets you work with data of any type, in any location. This allowed us to deliver object tables, a new table type that provides a structured interface for unstructured data. Object tables let you natively run analytics and ML on images, audio, and documents, changing the game for data teams worldwide, who can now innovate without limits with all their data in one unified environment. Additionally, support for Apache Iceberg through BigLake is now generally available, so you can build a unified data and AI platform where multiple engines spanning analytics and ML/AI runtimes work seamlessly over a single copy of data.
And now, the models you create in BigQuery using BigQuery ML are now instantly visible in Vertex AI model registry. Once in the model registry, you can then deploy them to Vertex AI endpoints for real-time serving, use Vertex AI pipelines to monitor and train models, and view detailed explanations for your predictions through BigQuery ML and Vertex AI integration.
Priceline plans to deploy Google Cloud’s generative AI technologies to enable customers to engage with a new, generative AI-powered chatbot and to receive personalized offerings when searching for hotels worldwide. By leveraging BigQuery, Looker, and Recommendations AI, TIME has gone from zero to two million connected users on time.com, enabling a 360-degree view of their audiences to create deeper relationships with them.
An open data ecosystem
Google Cloud provides industry-leading integration with open-source and open APIs, providing portability, flexibility, and reducing the risk of vendor lock-in. At the same time, we significantly expanded our partner ecosystem and are increasing investments across many new areas.
“Google’s partner ecosystem stands out; GCP Marketplace has over 500 partner solutions and offers analytics hub integration for data sharing with thousands of commercial/public datasets.”
Today, more than 900 software partners are building their products using Google’s Data Cloud, and more than 50 data platform partners offer validated integrations through our Google Cloud Ready – BigQuery initiative. Learn how Airports of Thailand , SKY and EVme are adopting Google Cloud’s open data cloud to deliver sustainable, digital-first travel experiences.
We also introduced BigQuery Partner Center, a new user interface in the Google Cloud console that lets you easily discover, try, purchase and use a diverse range of partner products that have been validated through the Google Cloud Ready – BigQuery program.
We look forward to continuing to innovate and partner with you on your digital transformation journey and are honored to be a Leader in The Forrester Wave™: Cloud Data Warehouses, Q2 2023.
“CPG manufacturers and retailers are dependent on product data to understand their markets, inspire innovation, and serve customers, but this is a challenge with the common data sources across the industry,” says Cem Kent, CEO of Harmonya. “Data sets are siloed, products are categorized differently across sources, and the descriptive attributes and characteristics about products are not evolving to reflect industry or consumer perspectives. That’s where Harmonya comes in.”
Harmonya is an all-in-one, AI-powered, product data enrichment, categorization, and insights platform. The company enriches its customers’ product data with deeper attributes and characteristics to power more impactful analytics and decision-making. Harmonya is committed to empowering its customers with greater control over their product analysis and categorization while maintaining a fresh, consistent view of the categories in which they operate. With Harmonya, customers can unlock a wide range of use cases, including:
category management
merchandising
innovation
e-commerce content, search and recommendation applications
Solution approach
Harmonya’s proprietary technology enriches product data by ingesting information from millions of online product listings and tags products with unique concepts informed by titles, descriptions, structured attributes, consumer reviews, and more. This harmonized data asset empowers brand and retail teams that use product data to unlock new opportunities for their business through a better understanding of what matters most to consumers. We’ll discuss several use cases of this enrichment in detail later in this article.
On top of this enrichment, Harmonya builds robust analytical tools to help uncover insights about the consumer and marketing drivers of in-market performance, improve assortment and merchandising, guide product innovation, engage target audiences more effectively, and categorize products. Fortune 500s and other industry leading CPG manufacturers and retailers rely on Harmonya to enrich their product data and help them compete in a fast-changing marketplace.
Solution details
Harmonya builds and maintains data pipelines that process massive amounts of data, training and serving machine learning models on top of the BigQuery data warehouse used throughout the organization. BigQuery’s integration with other Google Cloud components and pay-per-use model enables near-limitless scalability for data processing, providing significant value that allows Harmonya to focus on bringing value to its customers. Below is an example illustrating the data access model and deployment model between Harmonya’s internal environment and the customer-facing multi-tenant environment on the right side of the diagram.
The above diagram shows that Harmonya’s stack is split into two separate environments. The first is an internal environment (left side, yellow background) independent of Harmonya’s customers and their data. There, the Harmonya Product Language is created, starting (from left to right) with scheduling data acquisition tasks, querying the current state of the normalized product data vs. the scrape-state DB and deciding which new scrape tasks should be performed.
Then, Cloud Functions are triggered to gather the relevant data from the web and store the raw results in Cloud Storage. From there, the process of the Harmonya Graph creation takes place, where products are clustered into a consistent view, and relations between products are discovered. Following that process, a set of NLP models are used to extract any meaningful concepts related to the products forming a detailed taxonomy.
The second environment (right side, red background) is a multi-tenant environment where each customer has their own complete separation of resources, ensuring nothing is being shared between any two customers of Harmonya.
The processing starts with a customer sharing raw point-of-sale data point with Harmonya. This data is processed using BigQuery in a streamlined and scalable way and merged with a snapshot of the Harmonya Language, relying on BigQuery’s capability to join data between separate projects. The merged dataset is then processed in Harmonya’s data pipelines, running ML processing to generate customer-specific insights, stored in Cloud SQL for real-time serving in Harmonya’s SaaS based application, running on Node.js and accessed by customers online at https://app.harmonya.com.
BigQuery is an essential tool for Harmonya when working with product data for several reasons:
Scalability: BigQuery is a cloud-based data warehouse that can scale automatically to handle large and complex data sets. This makes it an ideal solution for Harmonya, which needs to manage growing amounts of data without the need for expensive infrastructure investments.
Cost-effective: BigQuery operates on a pay-as-you-go model, which means Harmonya only pays for the resources we use. This makes it a cost-effective solution for startups with limited budgets.
Speed: BigQuery’s high-speed processing of large data sets enables Harmonya to analyze data and make decisions in real-time. This provides a competitive advantage to customers that need to react quickly to market changes.
Accessibility: BigQuery is accessible through a web-based interface, as well as through a range of programming languages, including SQL and Python. This means that Harmonya’s team, with different levels of technical expertise, can use the tool to analyze and visualize their data integration: BigQuery can integrate with a range of other tools, including data visualization and business intelligence tools, as well as with other Google services. This makes it a versatile tool for Harmonya, which needs to work with data from multiple sources.
Simple data ingestion: BigQuery can ingest data from a variety of sources, including Cloud Storage, Cloud Pub/Sub, Cloud SQL, and more. Harmonya uses these integrations to seamlessly move data from their existing data sources into BigQuery.
On top of that, BigQuery’s flexible scheme allows it to store various data types and query them in a dynamic fashion. Harmonya stores a mixture of structured and semi-structured json files within the same tables in BigQuery, simplifying data ingestion and allowing for a wide variety of use-cases with less data duplication.
Creating meaningful selling stories and trends
Enriching product data unlocks a wide variety of commercial and operational use cases on the brand and retail sides of the commerce chain. A popular use for Harmonya’s enrichment is in creating more impactful and dynamic selling stories.
Manufacturers rely on retailers to sell their products, so it’s crucial for manufacturers to create unique selling stories that resonate with retailers to stand out in the highly competitive marketplace. Enriching product data with unique attributes and characteristics with Harmonya can help manufacturers tell better selling stories to retailers in several ways:
Deeper understanding of performance drivers: When product data is enriched with unique attributes and characteristics, brands and retailers have a differentiated understanding of in-market dynamics. This helps them make better decisions, identify the true drivers of brand and category performance, and develop more successful strategies to drive growth.
Improved product descriptions: Manufacturers can provide more detailed and accurate product descriptions to retailers when they have a more holistic understanding of how owned and competitive portfolios resonate with consumers. This helps brands and retailers create more compelling product descriptions and marketing materials that drive sales.
Better targeting: Enriched product data can help manufacturers target specific customer segments more effectively based on the combination of first party data and enriched transactional data. By understanding the unique attributes and characteristics of a product and the demographics and behaviors of purchasers, manufacturers and retailers can tailor their outreach and marketing messages to specific customer needs and preferences with unprecedented precision.
Differentiation: Retailers carry many products from various manufacturers, and it’s important for manufacturers to create a unique selling story that sets their product apart from the competition. A unique selling story can make the difference between a single or multiple facings and preferential shelf placement, especially when both the brand and the retailer understand the unique attributes that set those products apart.
Harmonya’s collaborative approach to data enrichment is brought to life via its suite of applications that allow customers to explore and analyze their enhanced datasets.
Detecting trends in product data is challenging for brands and retailers because of the vast amount of information generated by multiple sources, such as sales data, customer feedback, social media, and industry reports. Extracting insights from this data requires powerful analytics tools, expertise in data analysis, and a deep understanding of the market.
This is where Harmonya comes in. Their proprietary algorithms can analyze sales data at the attribute and characteristic level of products, providing granular insights into consumer preferences and trends. Harmonya’s technology can also identify emerging trends and changes in consumer behavior, allowing brands and retailers to adapt their product strategies in real-time.
By leveraging Harmonya’s technology, brands and retailers can gain a competitive edge by staying ahead of the curve in product innovation and positioning. They can also optimize their product portfolios and pricing strategies, improve customer engagement, and ultimately drive revenue growth.
In addition to analyzing sales data at the attribute and characteristic level of products, Harmonya also provides an intuitive, user-friendly interface that allows brands and retailers to visualize their data and trends in a way they haven’t been able to before. Their platform displays data in interactive dashboards and charts, making it simple for users to identify patterns and correlations that may be difficult to spot with traditional analysis methods.
Furthermore, Harmonya’s app simplifies the process of detecting trends by automating the analysis and reporting process, eliminating the need for manual data processing and freeing up valuable time for teams to focus on other strategic initiatives. By leveraging machine learning algorithms, Harmonya’s platform can quickly identify and report on trends, providing brands and retailers with timely insights that enable them to make informed decisions about product development and marketing campaigns.
Overall, Harmonya’s technology and app enable brands and retailers to gain a deeper understanding of their customers’ preferences and behaviors, leading to better product development, pricing strategies, and customer engagement. By providing powerful insights in an easy-to-use interface, Harmonya is helping companies stay ahead of the curve in a constantly evolving market.
Real-world impact
According to a Fortune 50 multi-category manufacturer, “Harmonya achieved 98% accuracy of UPC coding and classification during their engagement. This has enabled us to enrich and automate core data processes around how we manage our product catalog and harmonize external data structures. We are really impressed with the accuracy and quality of their outputs, and we are accelerating the expansion of our partnership to take full advantage of Harmonya’s strategic capabilities more broadly.”
Conclusion
Google’s data cloud provides a complete platform for building data-driven applications from simplified data ingestion, processing, and storage to powerful analytics, AI, ML, and data sharing capabilities — all integrated with the open, secure, and sustainable Google Cloud platform. With a diverse partner ecosystem, open-source tools, and APIs, Google Cloud can provide technology companies the portability and differentiators they need to serve the next generation of customers.
To learn more about Harmonya on Google Cloud, visit Harmonya. Click here to learn more about Google Cloud’s Built with BigQuery initiative.
We thank the Google Cloud team members who co-authored the blog: Banruo Yu, Technical Account Manager, Google Cloud, and Christian Williams, Principal Architect, Google Cloud
Editor’s note: Today we hear from PLAID, the company behind KARTE, a customer experience platform (CxP) that helps businesses provide real-time personalized and seamless experiences to their users. PLAID recently re-architected its real-time user analytics engine using Cloud Bigtable, achieving latencies within 10 milliseconds. Read on to learn how they did it.
Here at PLAID, we rely on many Google Cloud products to manage our data in a wide variety of use cases: AlloyDB for PostgreSQL for relational workloads, BigQuery for enterprise data warehousing, and Bigtable, an enterprise-grade NoSQL database service. Recently, we turned to Bigtable again to help us re-architect our core customer experience platform — a real-time user analytics engine we call “Blitz.”
To say we ask a lot of Blitz is understatement: our high-traffic environment receives over 100,000 events per second, which Blitz needs to process within a few hundred milliseconds end-to-end. In this blog post, we’ll share how we re-architected Blitz with Bigtable and achieved truly real-time analytics under heavy write traffic. We’ll delve into the architectural choices and implementation techniques that allowed us to accomplish this feat, namely, implementing a highly scalable, low-latency distributed queue.
What we mean by real-time user analytics
But first, let’s discuss what we mean when we say ‘real-time user analytics’. In a real-time user analytics engine, when an event occurs for a user, different actions can be performed based on event history and user-specific statistics. Below is an example of an event data and a rule definition that filters to a specific user for personalized action.
This is pseudo-code for a rule to verify whether “userId-xxx” is a user who is a “member”, has an average purchase price of 10,000 yen or more in a year, had a session count of 10 or more last week, but who purchased little to nothing.
When people talk about real-time analytics, they usually mean near-real-time, where statistics can be seconds or even minutes out-of-date. However, being truly real-time requires that user statistics are always up-to-date, with all past event histories reflected in the results available to the downstream services. The goal is very simple, but it’s technically difficult to keep user statistics up-to-date — especially in a high-traffic environment with over 100,000 events per second and required latency within a few hundred milliseconds.
Our previous architecture
Our previous analytics engine consisted of two components: a real-time analytics component (Track) and a component that updates user statistics asynchronously (Analyze).
Figure 3: Architecture of the previous analytics engine
The key points of this architecture are:
In the real-time component (Track), the user statistics generated in advance from the key-value store are read-only, and no writing is performed to it.
In Analyze, streaming jobs roll up events over specific time windows.
However, we wanted to meet the following strict performance requirements for our distributed queue:
High scalability – The queue must be able to scale with high-traffic event numbers. During peak daytime hours, the reference value for the write requests is about 30,000 operations per second, with a write data volume of 300 MiB/s.
Low latency that achieves both fast writes and reads within 10 milliseconds.
But the existing messaging services could not meet both high-scalability and low-latency requirements simultaneously — see Figure 4 below.
Figure 4: Comparison between existing messaging services
Our new real-time analytics architecture
Based on the technical challenges explained above, Figure 5 below shows the architecture after the revamp. Changes from the existing architecture include:
We divided the real-time server into two parts. Initially, the frontend server is responsible for writing events to the distributed queue.
The real-time backend server reads events from the distributed queue and performs analytics.
Figure 5: Architecture of the revamped analytics engine
In order to meet both our scalability and latency goals, we decided to use Bigtable, which we already used as our low-latency key-value store, to implement our distributed queue. Here’s the specific method we used.
Bigtable is mainly known as a key-value store that can achieve latencies in the single-digit milliseconds and be scaled horizontally. What caught our attention is the fact that performing range scans in Bigtable that specify the beginning and end of row keys is also fast.
The specific schema for the distributed queue can be described as follows:
The key point is the event timestamp added at the end. This allows us to perform range scans by specifying the start and end of the event timestamps.
Furthermore, we were able to easily implement a feature to delete old data from the queue by setting a time-to-live (TTL) using Bigtablegarbage collection feature.
By implementing these changes, the real-time analytics backend server was able to ensure that the user’s statistics remain up-to-date, regardless of any unreflected events.
Additional benefits of Bigtable
Scalability and low latency weren’t the only things that implementing a distributed queue with Bigtable brought to our architecture. It was also cost-effective and easy to manage.
Cost efficiency
Thanks to the excellent throughput of the SSD storage type and a garbage collection feature that keeps the amount of data constant by deleting old data, we can operate our real-time distributed queue at a much lower cost than we initially anticipated. In comparison to running the same workload on Pub/Sub, our calculations show that we operate at less than half the cost.
Less management overhead
From an infrastructure operation perspective, using the Bigtable auto-scaling feature reduces our operational cost. In case of a sudden increase in requests to the real-time queue, the Bigtable cluster can automatically scale out based on CPU usage. We have been operating this real-time distributed queue for over a year reliably with minimal effort.
Supercharging our real-time analytics engine
In this blog post, we shared our experience in revamping our core real-time user analytics engine, Blitz, using Bigtable. We successfully achieved a consistent view of the user in our real-time analysis engine under high traffic conditions. The key to our success was the innovative use of Bigtable to implement a distributed queue that met both our high scalability and low latency requirements. By leveraging the power of Bigtable low-latency key-value store and its range scan capabilities, we were able to create a horizontally scalable distributed queue with latencies within 10ms.
We hope that our experience and the architectural choices we made can serve as a valuable reference for global engineers looking to enhance their real-time analytics systems. By leveraging the power of Bigtable, we believe that businesses can unlock new levels of performance and consistency in their real-time analytics engines, ultimately leading to better user experiences and more insightful decision-making.
Artificial intelligence has changed marketing in extraordinary ways, which is why the market for AI in the marketing profession is booming. Marketing companies spent over $35 billion on AI in marketing research alone last year, but they are likely to spend as much on AI-driven SEO.
Search engine optimization (or SEO) is a broad and highly complicated area and if you are a new business owner discovering it for the first time, it can feel terribly overwhelming. The good news? You don’t need to be an expert in SEO in order to benefit from it. However, it certainly does help to have some basic understanding of it. In fact, new AI tools have made SEO more advanced than ever!
Users get custom search results based on their past behaviors, devices, and locations. Search engines know what users want. This shift drives change in SEO. AI builds customized optimization strategies. SEO techniques serve as a baseline. The competitive landscape prioritizes page one results. AI looks at the relationship between search intent and content. Data paints a picture customized to specific content landscapes. Optimization strategies that work for one group of keywords might not work for another.
In this article, we’re going to share a brief layman’s guide to understand SEO in 2023 and the role that AI is playing.
What is SEO?
Before you can learn how to apply AI in SEO, you need to understand what SEO is in the first place. SEO, in the simplest terms, is the process of making your website easier to find! With some technical and onsite optimizations, you essentially help Google identify who you are and what you do – and then work towards attracting as many potential customers to your website as possible.
But it’s not just about getting more eyes on your website; it’s about ensuring that you convert as many of those website visitors into paying customers as possible.
Make your website easy to find.
Help Google rank you accordingly in a SERP (search engine results page).
Ensure the “ideal” customers are clicking on your website.
Creating a positive on-page experience in order to convert those visitors into customers.
SEO has existed long before the newest advances in AI. However, AI has helped improve the process considerably.
How can AI help with SEO?
SEO has gone through many iterations over the years as Google frequently updates its algorithm. In any case, there are four fundamental aspects of SEO that remain as important today as they have ever been. AI can help SEO professionals and marketers with all of these steps. They include the following:
Keyword research.
Onsite optimization.
Content creation.
Link-building.
SEO is arguably the process of pleasing Google (though it is much more nuanced than that). Follow the rules and you will be rewarded accordingly. Keep reading to learn some ways that artificial intelligence can help with SEO. This article provides a brief layman’s guide to understanding SEO in 2023 for your convenience.
AI helps with keyword research
You know when you type something into Google and then click on the most relevant web page in order to find the information / products that you need?
That’s keyword research in a nutshell.
Those high-ranking websites will have done their research in order to determine the kind of phrases and search terms that their ideal customers type into Google – including the intention behind those searches – and then they position their website and content accordingly.
AI technology can help with this essential stage of the SEO process. Search has been permanently changed by AI. Custom search results are provided to users based on their past behaviors, devices, and locations. Search engines have the ability to understand what users want. This shift has led to changes in SEO. AI creates personalized optimization strategies, while SEO techniques serve as a baseline. Page one results are prioritized in the competitive landscape. AI analyzes the relationship between search intent and content. Data is used to create a customized picture of specific content landscapes. Optimization strategies that work for one group of keywords may not work for another.
SEMRush, Keyword Finder and Keyword Chef are some of the top tools that you can use to find new keywords with AI.
Machine learning can help with onsite optimization
There are many, many aspects to proper optimization so we won’t bog you down with too many details. Instead, let’s focus on the fundamentals:
Improve website performance: the faster it loads and the easier your website is to navigate, the better. It’s all about the UX (user experience). There are many differenttechnical SEO tasks that occur in the back-end of the website that are critical for improving the overall health and performance of your website – all of which contribute to your SEO.
Focus keywords in Titles and Meta: you need to identify your primary focus keywords and then carefully place them in your Titles and Meta descriptions – including the general content (without stuffing too many in unnaturally).
Create awesome content: you also need to populate your website with awesome, SEO-optimized content with consumer psychology in mind. What are their biggest problems and how can you provide them with the perfect solution?
There are a lot of AI tools that can help with onsite SEO. These tools can help you identify broken links, make sure the keyword density on your posts isn’t too high and identify other onsite SEO problems.
AI Can help with content creation
Everyblog post, every social media reel, and every video that you engage with online is classed as ‘content’.
Each piece of content that you associate with your brand and business must be created with a clear intention: to serve your audience and make their lives easier.
The more high-value content you create, the more opportunities you have to rank higher in Google. This is because it builds your relevancy in your industry – while also establishing your authority.
There are also a number of benefits of using AI to help with content generation. Tools like Ryte.me, ChatGPT and Copy.AI can help considerably.
“To be eligible to appear in Google web search results (web pages, images, videos, news content, or other material that Google finds from across the web), content shouldn’t violate Google Search’s overall policies or the spam policies listed on this page.”
In other words, it is okay to use AI-created content. However, it shouldn’t look like spam.
AI can also help with link building
For every link that points to your website from other reputable, industry-relevant websites, it essentially counts as a ‘vote of confidence’.
The more links you have, clearly the more authoritative and trustworthy your website is.
For example, if you write a blog post in so much depth that other marketers and websites invariably share and link to the post, Google is going to assume that it must be very interesting. As a result, they will reward you with more visibility.
Links = votes.
There are also many ways that AI can help with linkbuilding. You can use AI tools like the SEMRush backlink finder to identify links to relevant sites and use them for your own prospecting. You can also use AI to generate automated email outreach campaigns to ask other bloggers to link to your content or offer you guest blogging opportunities.
Artificial Intelligence Can Help with All Four Aspects of SEO
Let’s have a quick recap of the ways that you can use AI to help with SEO:
Your keywords help Google (and your customers) identify what your website is all about. AI tools can help you find new keywords.
Onsite optimization tidies up your website, makes it easier to find, and helps your customers navigate their way to a positive conclusion (money in your pocket). AI can help you deal with broken links and keyword density issues.
Content creation is all about establishing yourself as an authority by sharing high-value information that your audience can engage with. ChatGPT and other AI tools make writing content easier than ever.
Link building is all about getting as many ‘votes of confidence’ from other reputable websites as possible. AI can help you find new linkbuilding opportunities and automate outreach email campaigns to get those links.
As mentioned earlier: you don’t need to master SEO to benefit from it. For the best results it may be worth hiring a digital marketing agency to handle these requirements on your behalf. That way, you won’t have to try and master a broad and complicated field and simply enjoy the benefits instead: a high and sustainable ROI.
In summary, there are huge benefits of using AI for SEO. You should use it to your advantage.
