Data breaches are most widely publicized when they occur at major corporations, such as Target. Unfortunately, these high profile cases take attention away from the need to invest in data security solutions at home. Many hackers try to steal data directly from individual consumers, so they must take all necessary efforts to safeguard it. This can be even more important when working from home, since being online leaves hackers with more opportunities to steal your data.
Windows Users Must Be Diligent About Stopping Hackers from Accessing their Data
Windows is the most popular desktop operating system out there by far, having solely monopolized the OS markets for decades. Since billions of us across the globe use Windows daily and trust the software with our sensitive, confidential, and personal data, it is important not to forget to be vigilant about safety while using Windows. Some practices, like backing up your Windows machine can make or break your life, literally, if you lose your most precious data. You would not drive without a seatbelt, so why would you run your Windows without good knowledge of its security options and settings?
For these reasons, we will look into some industry-standard best practices as well as more advanced ways of improving your Windows cybersecurity by leaps and bounds.
Just like any operating system, no matter if it is a macOS or Android, there are several things that a user has to set up for better performance, better efficiency, and most importantly better security than what comes out of the box. These settings are not set by default because users may prefer different configurations for different purposes, so only basic security settings are activated by the manufacturer on a new machine. The rest is up to you to make an informed decision.
Windows has existed for a long time, and every iteration of Windows has been better and better in terms of several factors including security. The more recent version of Windows, namely Win 10 and Win 11 are security powerhouses, but if you do not know how to interact with these features, you will not reap the full benefits. Windows is usually loaded with a lot more trash than other operating systems (ware) and is also a larger platform that accepts a lot more third-party applications (external) than, say, Apple’s systems. This means that your data can be easily exposed. It is also designed to be backward-compatible with older software, which opens more security holes. However, this does not mean that you cannot adjust Windows to operate at a squeaky clean, bulletproof level. It just takes a bit of time looking through and toggling some Windows Security settings.
Here is a quick list of some settings that we need to talk about that includes both security and privacy-related settings;
Windows FirewallInstallation settingsCortanaAd trackingLocation trackingApp access and permissionsUninstall unnecessary programsSystem ProtectionWindows DefenderDevice SecurityApp & Browser ControlVirus & Threat Protection
First off, in case you are installing a fresh copy of Windows yourself, avoid the ‘Express’ setting at install and opt for a ‘Custom’ set up. When opting for this type of setup, you will be more in control of what settings Windows applies, such as learning more about what Diagnostics Windows records, use of location services, Cortana, etc. as well as a full Privacy Statement for you to read. You can also disable any data collection, diagnostics, and location tracking settings in Settings>Accounts and Settings>Privacy. Cortana can also be, and should be, switched off (like the tracking features) in Settings unless you explicitly need the voice-activated assistant. Keep in mind that Cortana does monitor your system and your activities by default.
Furthermore, make sure that your Windows Firewall (again you can find it via search) is ON. As far as app access and permissions goes, primarily it is important to uninstall (search for ‘Add Remove’ programs) any programs that you do not want on your PC. These programs can contain malware, and unnecessarily hog your PC’s resources in the background. Another thing to keep in mind is which programs and apps you have given your camera, microphone, and other access permissions to (search for App Permissions). Also, if your device supports it, you will be able to access an area of settings called ‘Device Security where you can toggle helpful hardware security features on and off, such as ‘Core Isolation’, ‘Memory Integrity’, and others. Such settings are designed to fight severe cyberattacks,
For optimal protection against dangerous cyberattacks and hackers, it is advisable to ensure that your Windows Defender Smart Screen is enabled, as well as your UAC or User Account Control. There is also the option of enabling Microsoft’s Bitlocker, which is a disc encryption tool (although this is only offered in Win 10 Pro and Enterprise).
Modern Windows versions like 10 and 11 already come with security features enabled by default, such as DEP or Data Execution Prevention for 64-bit applications, ASLR, SEHOP, and more. However, it is up to you to configure options like your Microsoft Defender’s Antivirus and SmartScreen, your Windows Firewall as well as BitLocker encryption. Finally, once you have cleaned up unnecessary programs, and taken the other tips in this article into account, you could also greatly benefit from;
Installing a premium antimalware programUsing a premium VPN when connecting to the internetUsing a security-focused web browser instead of Microsoft EdgePracticing internet browsing best practices like learning about phishing
Data Privacy Must Be a Priority for Windows Users
If you own a device with the Windows operating system, then you have to make sure that data privacy is a top concern. You need to make sure that your settings are adjust to stop hackers from accessing your data.
Cloud Computing is the next big thing that is becoming popular all over the world, especially for the bigger Enterprises. People are now open the cloud computing options because they want to save the data for a longer term to make sure that they don’t lose data in case of any emergency.
Cloud computing has been in the industry for more than two decades now, and it has been continuously providing competitive benefits to everybody in the industry.
Overall, around 69% of the international data has been stored using the cloud computing technique. Over there are around 94% of businesses claim to see an improvement in terms of security with the cloud computing technique and process.
Some of the major benefits of cloud computing are mentioned below.
There are a number of amazing reasons to invest in cloud computing. You want to make sure that you know how to use it effectively, because it can pay huge dividends for your business if you know how to invest in it.
Cloud Computing is cost-saving. You will be saving a good amount of money with efficient Cloud Computing techniques. This is the reason why people no longer invest in other security options for storing and saving the data; instead, they use cloud computing techniques that are a lot more cost-effective and Secure.
A lot of data companies are also working on cloud computing techniques to get excellent flexibility and mobility option. With the cloud computing techniques, the industries have greater insight into the data and can forecast a lot more than they do before the given time period.
Cloud Computing is the future, providing better insight and better collaboration options. With better collaboration options, the industries are increasing their revenue and benefits to a huge level. Quality control is also possible, along with the disaster recovery option.
You no longer have to keep preventing the data from security threats and other issues. Because with the cloud computing option, you will be having a good amount of safety without any difficulty that too with the automatic updates on the software. There is always a competitive edge that you will be getting when you are using cloud computing, and sustainability is always top-notch. There are a lot of companies that are providing Cloud Computing services that you must go for based on your requirement for saving and preventing the data.
These security option will not only significantly save the data, but you will also be moving towards an efficient option in terms of storing your data. You no longer have to go for the conventional techniques of in-house storage of data. Internal data theft is not possible when you are using Cloud Computing. The intelligent Cloud Computing options will provide your disclaimer before the disaster and also offer you disaster recovery options. There are around 9% of the users who have been using cloud computing that claim for better disaster recovery when using Cloud Computing. Moreover, the revenue generation is approximately 53% more as compared to the competitors.
Another very important benefit of cloud computing is that it helps with data scalability. You can store far more data on your cloud servers than you could ever hope to store on your internal networks or hard discs. When you combine this with the fact that cloud technology makes it easier to back data up, you will start to see a lot of great benefits of using cloud technology for your company.
An interactive analytics application gives users the ability to run complex queries across complex data landscapes in real-time: thus, the basis of its appeal. The application presents a massive volume of unstructured data through a graphical or programming interface using the analytical abilities of business intelligence technology to provide instant insight. Furthermore, this insight can be modified and recalibrated by changing input variables through the interface. Interactive analytics applications present vast volumes of unstructured data at scale to provide instant insights.
The image above shows a typical example of an interactive analytics application. It shows that someone is interacting with the data changing different inputs to navigate through unstructured data.
Why Use an Interactive Analytics Application?
Every organization needs data to make many decisions. The data is ever-increasing, and getting the deepest analytics about their business activities requires technical tools, analysts, and data scientists to explore and gain insight from large data sets. Interactive analytics applications make it easy to get and build reports from large unstructured data sets fast and at scale.
There are many tools in the market right now to assist with building interactive analytics applications. In this article, we’re going to look at the top 5.
Top 5 Tools for Building an Interactive Analytics App
Firebolt makes engineering a sub-second analytics experience possible by delivering production-grade data applications & analytics. It is built for flexible elasticity: it can easily be scaled up or down in response to the workload of an application with just a click or an execution of a command.
It is scalable because of its decoupled storage and computed architecture. You can use firebolt programmatically through REST API, JDBC, and SDKs — that makes it easy to use. Firebolt is super-fast compared to other popular tools to build interactive analytics apps.
Firebolt also makes common data challenges such as slow queries and frequently changing schema easy to deal with at a reasonable price — $1.54/hour (Engine:1 x c5d.4xlarge).
Snowflake provides the right balance between the cloud and data warehousing, especially when data warehouses like Teradata and Oracle are becoming too expensive for their users. It is also easy to get started with Snowflake as the typical complexity of data warehouses like Teradata and Oracle are hidden from the users.
It is secure, flexible, and requires less management compared to traditional warehouses. Snowflake allows its users to unify, integrate, analyze, and share previously stored data at scale and concurrency through a management platform.
Snowflake offers a “pay for what you use” service but doesn’t state a price; they only highlight the “start for free” button on the website.
3. Google BigQuery
Google BigQuery is a serverless and cost-effective multi-cloud data warehouse. It is designed for business agility, and that is why it is highly scalable. It offers new customers $300 in free credits during the first 90 days. BigQuery also takes it further by giving all of their customers 10 GB storage and up to 1 TB queries/month for free.
Its built-in machine learning makes it possible for users to gain insights predictive and real-time analytics. Accessing data stored on Google BigQuery is secured with default and customer-managed encryption keys, and you can easily share any business intelligence insight derived from such data with teams and members of your organization with a few clicks.
Google BigQuery also claims to provide 99.99% uptime SLA. It offers a “pay for what you” service.
Druid is a real-time analytics database from Apache. It is a high-performing database that is designed to build fast, modern data applications. Druid is specifically designed to support workflows that require fast ad-hoc analytics, concurrency, and instant data visibility are core necessities.
It is easy to integrate with any existing data pipelines, and it can also stream data from the most popular message buses such as Amazon Kinesis and Kafka. It can also batch load files from data lakes such as Amazon S3 and HDFS. Druid is purposefully built to deploy in public, private, and hybrid clouds and use indexing structures, exact and approximate queries to get the most results fast.
Druid has no initial price.
5. Amazon Redshift
Amazon Redshift is a fast and widely used data warehouse. It is a fully managed and scalable data warehouse service that is cost-effective to analyze all your data with existing business intelligence tools efficiently. It is easily integrated with the most popular business intelligence tools like Microsoft PowerBI, Tableau, Amazon QuickSight, etc.
Like other listed data warehouses, it is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more to build insight-driven reports and dashboards at costs less than $1,000 per terabyte per year. That is very cheap compared to traditional data warehouses. In addition, Amazon Redshift ML can automatically create, train, and deploy Amazon SageMaker ML. You can also access real-time operational analytics with the capability of Amazon Redshift.
Building interactive analytics applications are critical for organizations to get quick insight that can help their operations. Interactive analysis applications work best with accessible data centralized in a data warehouse; therefore, there is a need to have analysis tools that make building applications easy, effective and efficient.
For this purpose, this article’s tools such as Firebolt, Snowflake, Amazon Redshift, Google BigQuery, and Apache Druid are very suitable. If you are building an interactive analysis application, pick one of them that is suitable for your needs in terms of efficiency, cost, and scalability and run with it.
2021 has been a year punctuated with new realities. As enterprises now interact mainly online, data and analytics teams need to better understand their data by collaborating across organizational boundaries. Industry research shows 90% of organizations have a multicloud strategy which adds complexity to data integration, orchestration and governance. While building and running enterprise solutions in the cloud, our customers constantly manage analytics across cloud providers. These providers unintentionally create data silos that cause friction for data analysts. This month we announced the availability of BigQuery Omni, a multicloud analytics service that lets data teams break down data silos by using BigQuery to securely and cost effectively analyze data across clouds.
For the first time, customers will be able to perform cross-cloud analytics from a single pane of glass, across Google Cloud, Amazon Web Services (AWS) and Microsoft Azure. BigQuery Omni will be available to all customers on AWS and for select customers on Microsoft Azure during Q4. BigQuery Omni enables secure connections to your S3 data in AWS or Azure Blob Storage data in Azure. Data analysts can query that data directly through the familiar BigQuery user interface, bringing the power of BigQuery to where the data resides.
Here are a few ways BigQuery Omni addresses the new reality customers face with multi cloud environments:
Multicloud is here to stay: Enterprises are not consolidating, they are expanding and proliferating their data stack across clouds. For financial, strategic, and policy reasons customers need data residing in multiple clouds. Data platforms support for multicloud has become table stakes functionality.
Multicloud data platforms provide value across clouds: Almost unanimously, our preview customers echoed that the key to providing game-changing analytics was through providing more functionality and integration across clouds. For instance, customers wanted to join player and ad engagement data to better understand campaign effectiveness. They wanted to join online purchases data with in-store checkouts to understand how to optimize the supply chain. Other scenarios included joining inventory and ad analytics data to drive marketing campaigns, and service and subscription data to understand enterprise efficiency. Data analysts require the ability to join data across clouds, simply and cost-effectively.
Multicloud should work seamlessly: Providing a single-pane-of-glass over all data stores empowers a data analyst to extend their ability to drive business impact without learning new skills and shouldn’t need to worry about where the data is stored. Because BigQuery Omni is built using the same APIs as BigQuery, where data is stored (AWS, Azure, or Google Cloud) becomes an implementation detail.
Consistent security patterns are crucial for enterprises to scale: As more data assets are created, providing the correct level of access can be challenging. Security teams need control over all data access with as much granularity as possible to ensure trust and data synchronization.
Data quality unlocks innovation: Building a full cross-cloud stack is only valuable if the end user has the right data they need to make a decision. Multiple copies, inconsistent, or out-of-date data all drive poor decisions for analysts. In addition, not every organization has the resources to build and maintain expensive pipelines.
BigQuery customer Johnson & Johnson was an early adopter of BigQuery Omni on AWS; “We found that BigQuery Omni was significantly faster than other similar applications. We could write back the query results to other cloud storages easily and multi-user and parallel queries had no performance issues in Omni. How we see Omni is that it can be a single pane of glass using which we can connect to various clouds and access the data using, SQL like queries,” said Nitin Doeger, Data Engineering and Enablement manager at Johnson and Johnson.
Another early adopter from the media and entertainment industry had data hosted in multiple cloud environments. Using BigQuery Omni they built cross cloud analytics to correlate advertising with in game purchases. Needing to optimize campaign spend and improve targeted ad personalization while lowering the cost per click for ads, their challenge was that campaign data was siloed across cloud environments with AWS, Microsoft Azure, and Google Cloud. In addition to this the data wasn’t synchronized across all environments and moving data introduced complexity, risk and cost. Using BigQuery they were able to analyze CRM data in S3 while keeping the data synchronized. This resulted in a marketing attribution solution to optimize campaign spend and ultimately helped improve campaign efficiency while reducing cost and improving data accessibility across teams.
In 2022, new capabilities will include cross cloud transfer’ and authorized external tables to help data analysts drive governed, cross-cloud scenarios and workflows all from the BigQuery interface. Cross cloud transfer helps move the data you need to finish your analysis in Google Cloud and find insights leveraging unique capabilities of BigQuery ML, Looker and Dataflow. Authorized external tables will provide consistent and fine grained governance with row-level and column-level security for your data. Together these capabilities will unlock simplified and secure access across clouds for all your analytics needs. Below is a quick demo of those features relevant to multicloud data analysts and scientists.
To get started with BigQuery Omni, simply create a connection to your data stores, and start running queries against your existing data, wherever it resides. Watch the multicloud session at Next 21 for more details.
BigQuery Omni makes cross cloud analytics possible! We are excited with what the future holds and look forward to hearing about your cross cloud data analytics scenarios. Share your questions with us on the Google Cloud Community, we look forward to hearing from you.
October 23rd (this past Saturday!) was my 4th Googlevarsery and we are wrapping an incredible Google Next 2021!
When I started in 2017, we had a dream of making BigQuery Intelligent Data Warehouse that would power every organization’s data driven digital transformation.
This year at Next, It was amazing to see Google Cloud’s CEO, Thomas Kurian, kick off his keynote with CTO of WalMart, Suresh Kumar , talking about how his organization is giving its data the “BigQuery treatment”.
AS I recap Next 2021 and reflect on our amazing journey over the past 4 years, I’m so proud of the opportunity I’ve had to work with some of the world’s most innovative companies from Twitter to Walmart to Home Depot, Snap, Paypal and many others.
So much of what we announced at Next is the result of years of hard work, persistence and commitment to delivering the best analytics experience for customers.
I believe that one of the reasons why customers choose Google for data is because we have shown a strong alignment between our strategy and theirs and because we’ve been relentlessly delivering innovation at the speed they require.
Unified Smart Analytics Platform
Over the past 4 years our focus has been to build industries leading unified smart analytics platforms. BigQuery is at the heart of this vision and seamlessly integrates with all our other services. Customers can use BigQuery to query data in BigQuery Storage, Google Cloud Storage, AWS S3, Azure Blobstore, various databases like BigTable, Spanner, Cloud SQL etc. They can also use any engine like Spark, Dataflow, Vertex AI with BigQuery. BigQuery automatically syncs all its metadata with Data Catalog and users can then run a Data Loss Prevention service to identify sensitive data and tag it. These tags can then be used to create access policies.
In addition to Google services, all our partner products also integrate with BigQuery seamlessly. Some of the key partners highlighted at Next 21 included Data Ingestion (Fivetran, Informatica & Confluent), Data preparation (Trifacta, DBT), Data Governance (Colibra), Data Science (Databricks, Dataiku) and BI (Tableau, PowerBI, Qlik etc).
Planet Scale analytics with BigQuery
BigQuery is an amazing platform and over the past 11 years we have continued to innovate in various aspects. Scalability has always been a huge differentiator for BigQuery. BigQuery has many customers with more than 100 petabytes of data and our largest customer is now approaching an exabyte of data. Our large customers have run queries over trillions of rows.
But scale for us is not just about storing or processing a lot of data. Scale is also how we can reach every organization in the world. This is the reason we launched BigQuery Sandbox which enables organizations to get started with BigQuery without a credit card. This has enabled us to reach tens of thousands of customers. Additionally to make it easy to get started with BigQuery we have built integrations with various Google tools like Firebase, Google Ads, Google Analytics 360, etc.
Finally, to simplify adoption we now provide options for customers to choose whether they would like to pay per query, buy flat rate subscriptions or buy per second capacity. With our autoscaling capabilities we can provide customers best value by mixing flat rate subscription discounts with auto scaling with flex slots.
Intelligent Data Warehouse to empower every data analyst to become a data scientist
BigQuery ML is one of the biggest innovations that we have brought to market over the past few years. Our vision is to make every data analyst a data scientist by democratizing Machine learning. 80% of time is spent in moving, prepping and transforming data for the ML platform. This also causes a huge data governance problem as now every data scientist has a copy of your most valuable data. Our approach was very simple. We asked:”what if we could bring ML to data rather than taking data to an ML engine?”
That is how BigQuery ML was born. Simply write 2 lines of SQL code and create ML models.
Over the past 4 years we have launched many models like regression, matrix factorization, anomaly detection, time series, XGboost, DNN etc. These models are used by customers to solve complex business problems simply from segmentation, recommendations, time series forecasting, package delivery estimation etc. The service is very popular: 80%+ of our top customers are using BigQueryML today. When you consider that the average adoption rate of ML/AI is in the low 30%, 80% is a pretty good result!
We announced tighter integration of BQML with Vertex AI. Model explainability will provide the ability to explain the results of predictive ML classification and regression models by understanding how each feature contributes to the predicted result. Also users will be able to manage, compare and deploy BigQuery ML models in Vertex; leverage Vertex Pipelines to train and predict BigQuery ML models.
The BigQuery’s storage engine is optimized for real-time streaming. BigQuery supports streaming ingestion of 10s of millions of events in real-time and there is no impact on query performance. Additionally customers can use materialized views and BI Engine (which is now GA) on top of streaming data. We guarantee always fast, always fresh data. Our system automatically updates MVs and BI Engine.
Many customers also use our PubSub service to collect real-time events and process these through Dataflow prior to ingesting into BigQuery. This is a streaming ETL pattern which is very popular. Last year,we announced PubSub Lite to provide customers with a 90% lower price point and aTCO that is lower than any DIY Kafka deployment.
We also announced Dataflow Prime, it is our next generation platform for Dataflow. Big Data processing platforms have only focused on horizontal scaling to optimize workloads. But we have seen new patterns and use cases like streaming AI where you may have a few steps in pipelines that perform data prep and then customers have to run a GPU based model. Customers want to use different sizes and shapes of machines to run these pipelines in the most optimum manner. This is exactly what Dataflow Prime does. It delivers vertical auto scaling with the right fitting for your pipelines. We believe this should lower costs for pipelines significantly.
With Datastream as our change data capture service (built on Alooma technology), we have solved the last key problem space for customers. We can automatically detect changes in your operational databases like MySQL, Postgres, Oracle etc and sync them in BigQuery.
Most importantly, all these products work seamlessly with each other through a set of templates. Our goal is to make this even more seamless over next year.
Open Data Analytics with BigQuery
Google has always been a big believer in Open Source initiatives. Our customers love using various open source offerings like Spark, Flink, Presto, Airflow etc. With Dataproc & Composer our customers have been able to run various of these open source frameworks on GCP and leverage our scale, speed and security. Dataproc is a great service and delivers massive savings to customers moving from on-prem Hadoop environments. But customers want to focus on jobs and not clusters.
That’s why we launched Dataproc Serverless Spark (GA) offering at Next 2021. This new service adheres to one of our key design principles we started with: make data simple.
Just like with BigQuery, you can simply RUN QUERY. With Spark on Google Cloud, you simply RUN JOB. ZDNet did a great piece on this. I invite you to check it out!
Many of our customers are moving to Kubernetes and wanted to use that as the platform for Spark. Our upcoming Spark on GKE offering will give the ability to deploy spark workloads on existing Kubernetes clusters.
But for me the most exciting capability we have is, the ability to run Spark directly on BigQuery Storage. BigQuery storage is highly optimized analytical storage. By running Spark directly on it, we again bring compute to data and avoid moving data to compute.
BigSearch to power Log Analytics
We are bringing the power of Search to BigQuery. Customers already ingest massive amounts of log data into BigQuery and perform analytics on it. Our customers have been asking us for better support for native JSON and Search. At Next 21 we announced the upcoming availability of both these capabilities.
Fast cross column search will provide efficient indexing of structured, semi-structured and unstructured data. User friendly SQL functions let customers rapidly find data points without having to scan all the text in your table or even know which column the data resides in.
This will be tightly integrated with native JSON, allowing customers to get BigQuery performance and storage optimizations on JSON as well as search on unstructured or constantly changing data structures.
Multi & Cross Cloud Analytics
Research on multi cloud adoption is unequivocal — 92% of businesses in 2021 report having a multi cloud strategy. We have always believed in providing customers choice to our customers and meeting them where they are. It was clear that all our customers wanted us to take our gems like BigQuery to other clouds as their data was distributed on different clouds.
Additionally it was clear that customers wanted cross cloud analytics not multi-cloud solutions that can just run in different clouds. In short, see all their data with a single pane of glass, perform analysis on top of any data without worrying about where it is located, avoid egress costs and finally perform cross cloud analysis across datasets on different clouds.
With BigQuery Omni, we deliver on this vision, with a new way of analyzing data stored in multiple public clouds. Unlike competitors, BigQuery Omni does not create silos across different clouds. BigQUery provides a single control plane that shows an analyst all data they have access to across all clouds. Analyst just writes the query and we send it to the right cloud across AWS, Azure or GCP to execute it locally. Hence no egress costs are incurred.
Geospatial Analytics with BigQuery and Earth Engine
We have partnered with our Google Geospatial team to deliver GIS functionality inside BigQuery over the years. At Next we announced that customers will be able to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and Google Maps Platform.
Think about all the scenarios and use-cases your team’s going to be able to enable sustainable sourcing, saving energy or understanding business risks.
We’re integrating the best of Google and Google Cloud together to – again – make it easier to work with data to create a sustainable future for our planet.
BigQuery as a Data Exchange & Sharing Platform
BigQuery was built to be a sharing platform. Today we have 3000+ organizations sharing more than 250 petabytes of data across organizations. Google also brings more than 150 public datasets to be used across various use cases. In addition to this, we are also bringing some of the most unique datasets like Google Trends to BigQuery. This will enable organizations to understand in real-time trends and apply to their business problems.
I am super excited about the Analytics Hub Preview announcement. Analytics Hub will provide the ability for organizations to build private and public analytics exchanges. This will include data, insights, ML Models and visualizations. This is built on top of the industry leading security capabilities of BigQuery.
Breaking Data Silos
Data is distributed across various systems in the organization and making it easy to break the data silo and make all this data accessible to all is critical. I’m also particularly excited about the Migration Factory we’re building with Informatica and the work we are doing for data movement, intelligent data wrangling with players like Trifacta and FiveTran, with whom we share over 1,000 customers (and growing!). Additionally we continue to deliver native Google service to help our customers.
We acquired Cask in 2018 and launched our self service Data Integration service in Data Fusion. Now Fusion allows customers to create complex pipelines with just simple drag and drop. This year we focused on unlocking SAP data for our customers. We have launched various SAP connectors and accelerators to achieve this.
At GCP Next we also announced our BigQuery Migration service in preview. Many of our customers are migrating their legacy data warehouses and data lakes to BigQuery. BigQuery Migration Service provides end-to-end tools to simplify migrations for these customers.
And today, to make migrations to BigQuery easier for even more customers, I am super excited to announce the acquisition of CompilerWorks. CompilerWorks’ Transpiler is designed from the ground up to facilitate SQL migration in the real world and will help our customers accelerate their migrations. It supports migrations from over 10 legacy enterprises data warehouses and we will be making it available as part of our BigQuery Migration service in the coming months.
Data Democratization with BigQuery
Over the past 4 years we have focused a lot on making it very easy to derive actionable insights from data in BigQuery. Our priority has been to provide a strong ecosystem of partners that can provide you with great tools to achieve this but also deliver native Google capabilities.
BigQuery + Data Studio are like peanut butter and Jelly. They just work well together. We launched BI Engine first with Data Studio and scaled it to all the users. More than 40% of our BigQuery customers use Data Studio. Once we knew BI Engine works extremely well we now have made it an integral part of BigQuery API and launched it for all our internal and partner BI tools.
We announced GA for BI Engine at Next 2021 but we were already GA with Data Studio for the past 2 years. We recently moved the Data Studio team back into Google Cloud making the partnership even stronger. If you have not used Data Studio, I encourage you to take a look and get started for free today here!!
Connected Sheets for BigQuery is one of my favorite combinations. You can give every business user in your organization the ability to analyze billions of records using standard Google Sheets experience. I personally use it everyday to analyze all our product data.
We acquired Looker in Feb 2020 with a vision of providing a semantic modeling layer to our customers with a governed BI solution. Looker is tightly integrated with BigQuery including BigQuery ML. Our latest partnership with Tableau where Tableau customers will soon be able to leverage Looker’s semantic model, enabling new levels of data governance while democratizing access to data.
Finally, I have a dream that one day we will bring Google Assistant to your enterprise data. This is the vision of Data QnA. We are in early innings on this and we will continue to work hard to make this vision a reality.
Everything I’ve learned from customers over my years in this field is that they don’t just need a data catalog or a set of data quality and governance tools, they need an intelligent data fabric. That is why we created Dataplex, whose general availability we announced at Next.
Dataplex enables customers to centrally manage, monitor, and govern data across data lakes, data warehouses, and data marts, while also ensuring data is securely accessible to a variety of analytics and data science tools. It lets customers organize and manage data in a way that makes sense for their business, without data movement or duplication. It provides logical constructs – lakes, data zones, and assets – which enable customers to abstract away the underlying storage systems to build a foundation for setting policies around data access, security, lifecycle management, and so on. Check out Prajakta Damle’s session and learn from Deutsche Bank how they are thinking about a unified data mesh across distributed data.
The Google Cloud Data Analytics portfolio has become a leading force in the industry and I couldn’t be more excited to have been part of it. I do miss you, my customers and partners, and I’m frankly bummed that we didn’t get to meet in person like we’ve done so many times before (see a photo of my last in-person talk before the pandemic), but this Google Next was extra special, so let’s dive into the product innovation and their themes.
I hope that I will get to see you in person next time we run Google Next!
Organizations that collect geospatial data can use that information to understand their operations, help make better business decisions, and power innovation. Traditionally, organizations have required deep GIS expertise and tooling in order to deliver geospatial insights. In this post, we outline some ways that geospatial data can be used in various business applications.
Assessing environmental risk
Governments and businesses involved in insurance underwriting, property management, agriculture technology, and related areas are increasingly concerned with risks posed by environmental conditions. Historical models that predict natural disasters like pollution, flooding, and wildfires are becoming less accurate as real-world conditions change. Therefore, organizations are incorporating real-time and historical data into a geospatial analytics platform and using predictive modeling to more effectively plan for risk and to forecast weather.
Selecting sites and planning expansion
Businesses that have storefronts, such as retailers and restaurants, can find the best locations for their stores by using geospatial data like population density to simulate new locations and to predict financial outcomes. Telecom providers can use geospatial data in a similar way to determine the optimal locations for cell towers. A site selection solution can combine proprietary site metrics with publicly-available data like traffic patterns and geographic mobility to help organizations make better decisions about site selection, site rationalization, and expansion strategy.
Planning logistics and transport
For freight companies, courier services, ride-hailing services, and other companies that manage fleets, it’s critical to incorporate geospatial context into business decision-making. Fleet management operations include optimizing last-mile logistics, analyzing telematics data from vehicles for self-driving cars, managing precision railroading, and improving mobility planning. Managing all of these operations relies extensively on geospatial context. Organizations can create a digital twin of their supply chain that includes geospatial data to mitigate supply chain risk, design for sustainability, and minimize their carbon footprint.
Understanding and improving soil health and yield
AgTech companies and other organizations that practice precision agriculture can use a scalable analytics platform to analyze millions of acres of land. These insights help organizations understand soil characteristics and help them analyze the interactions among variables that affect crop production. Companies can load topography data, climate data, soil biomass data, and other contextual data from public data sources. They can then combine this information with data about local conditions to make better planting and land-management decisions. Mapping this information using geospatial analytics not only lets organizations actively monitor crop health and manage crops, but it can help farmers determine the most suitable land for a given crop and to assess risk from weather conditions.
Managing sustainable development
Geospatial data can help organizations map economic, environmental, and social conditions to better understand the geographies in which they conduct business. By taking into account environmental and socio-economic phenomena like poverty, pollution, and vulnerable populations, organizations can determine focus areas for protecting and preserving the environment, such as reducing deforestation and soil erosion. Similarly, geospatial data can help organizations design data-driven health and safety interventions. Geospatial analytics can also help an organization meet its commitments to sustainability standards through sustainable and ethical sourcing. Using geospatial analytics, organizations can track, monitor, and optimize the end-to-end supply chain from the source of raw materials to the destination of the final product.
Google Cloud provides a full suite of geospatial analytics and machine learning capabilities that can help you make more accurate and sustainable business decisions without the complexity and expense of managing traditional GIS infrastructure. Get started today by learning how you can use Google Cloud features to get insights from your geospatial data, see Geospatial analytics architecture.
Acknowledgements: We’d like to thank Chad Jennings, Lak Lakshmanan, Kannappan Sirchabesan, Mike Pope, and Michael Hao for their contributions to this blog post and the Geospatial Analytics architecture.
Businesses around the globe are realizing the benefits of replacing legacy data silos with cloud-based enterprise data warehouses, including easier collaboration across business units and access to insights within their data that were previously unseen. However, bringing data from numerous disparate data sources into a single data warehouse requires you to develop pipelines that ingest data from these various sources into your enterprise data warehouse. Historically, this has meant that data engineering teams across the organization procure and implement various tools to do so. But this adds significant complexity to managing and maintaining all these pipelines and makes it much harder to effectively scale these efforts across the organization. Developing enterprise-grade, cloud-native pipelines to bring data into your data warehouse can alleviate many of these challenges. But, if done incorrectly, these pipelines can present new challenges that your teams will have to spend their time and energy addressing.
Developing cloud-based data ingestion pipelines that replicate data from various sources into your cloud data warehouse can be a massive undertaking that requires significant investment of staffing resources. Such a large project can seem overwhelming and it can be difficult to identify where to begin planning such a project. We have defined the following principles for data pipeline planning to begin the process. These principles are intended to help you answer key business questions about your effort and begin to build data pipelines that address your business and technical needs. Each section below details a principle of data pipelines and certain factors your teams should consider as they begin developing their pipelines.
Principle 1: Clarify your objectives
The first principle to consider for pipeline development is clarify your objectives. This can be broadly defined as taking a holistic approach to pipeline development that encompasses requirements from several perspectives: technical teams, regulatory or policy requirements, desired outcomes, business goals, key timelines, available teams and their skill sets, and downstream data users. Clarifying your objectives clearly identifies and defines requirements from each key stakeholder at the beginning of the process and continually checks development against these requirements to ensure the pipelines built will meet these requirements.
This is done by first clearly defining the desired end state for each project in a way that addresses a demonstrated business need of downstream data users. Remember that data pipelines are almost always the means to accomplish your end state, rather than the end state itself. An example of an effectively defined end-state is “enabling teams to gain a better understanding of our customers by providing access to our CRM data within our cloud data warehouse” rather than “move data from our CRM to our cloud data warehouse”. This may seem like a merely semantic difference, but framing the problem in terms of business needs helps your teams make technical decisions that will best meet these needs.
After clearly defining the business problem you are trying to solve, you should facilitate requirement gathering from each stakeholder and use these requirements to guide the technical development and implementation of your ingestion pipelines. We recommend gathering stakeholders from each team, including downstream data users, prior to development to gather requirements for the technical implementation of the data pipeline. These will include critical timelines, uptime requirements, data update frequency, data transformation, DevOps needs, and security, policy, or regulatory requirements by which a data pipeline must meet.
Principle 2: Build your team
The second principle to consider for pipeline development is build your team. This means ensuring you have the right people with the right skills available in the right places to develop, deploy, and maintain your data pipelines. After you have gathered your pipeline requirements, you can begin to develop a summary architecture that will be used to build and deploy your data pipelines. This will help you identify the human talent you will need to successfully build, deploy, and manage these data pipelines and identify any potential shortfalls that would require additional support from either third-party partners or new team members.
Not only do you need to ensure you have the right people and skill sets available in aggregate, but these individuals need to be effectively structured to empower them to maximize their abilities. This means developing team structures that are optimized for each team’s responsibilities and their ability to support adjacent teams as needed.
This also means developing processes that prevent blockers to technical development whenever possible, such as ensuring that teams have all of the appropriate permissions they need to move data from the original source to your cloud data warehouse without violating the concept of least privilege. Developers need access to the original data source (depending on your requirements and architecture) in addition to the destination data warehouse. Examples of this are ensuring that developers have access to develop and/or connect to a Salesforce Connected App or read access to specific Search Ads 360 data fields.
Principle 3: Minimize time to value
The third principle to consider for pipeline development is minimize time to value. This means considering the long-term maintenance burden of a data pipeline prior to developing and deploying it in addition to being able to deploy a minimum viable pipeline as quickly as possible. Generally speaking, we recommend the following approach to building data pipelines to minimize their maintenance burden: Write as little code as possible. Functionally, this can be implemented by:
1. Leveraging interface-based data ingestion products whenever possible. These products minimize the amount of code that requires ongoing maintenance and empower users who aren’t software developers to build data pipelines. They can also reduce development time for data pipelines, allowing them to be deployed and updated more quickly.
Products like Google Data Transfer Service and Fivetran allow for managed data ingestion pipelines by any user to centralize data from SaaS applications, databases, file systems, and other tooling. With little to no code required, these managed services enable you to connect your data warehouse to your sources quickly and easily.For workloads managed by ETL developers and data engineers, tools like Google Cloud’s Data Fusionprovide an easy-to-use visual interface for designing, managing and monitoring advanced pipelines with complex transformations.
2. Whenever interface-based products or data connectors are insufficient, use pre-existing code templates. Examples of this include templates available for Dataflow that allow users to define variables and run pipelines for common data ingestion use cases, and the Public Datasets pipeline architecture that our Datasets team uses for onboarding.
3. If neither of these options are sufficient, utilize managed services to deploy code for your pipelines. Managed services, such as Dataflow or Dataproc, eliminate the operational overhead of managing pipeline configuration by automatically scaling pipeline instances within predefined parameters.
Principle 4: Increase data trust and transparency
The fourth principle to consider for pipeline development is increase data trust and transparency. For the purposes of this document, we define this as the process of overseeing and managing data pipelines across all tools. Numerous data ingestion pipelines that each leverage different tools or are not developed under a coordinated management plan can result in “tech sprawl”, which significantly increases the management overhead of data ingestion pipelines as the quantity of data pipelines increases. This becomes especially cumbersome if you are subject to service-level agreements, or legal, regulatory, or policy requirements for overseeing data pipelines. Preventing tech sprawl is, by far, the best strategy for dealing with it by developing streamlined pipeline management processes that automate reporting. Although this can theoretically be achieved by building all of your data pipelines using a single cloud-based product, we do not recommend doing so because it prevents you from taking advantage of features and cost optimizations that come with choosing the best product for your use case.
A monitoring service such as Google Cloud Monitoring Service or Splunk that automates metrics, events, and metadata collection from various products, including those hosted in on-premise and hybrid computing environments, can help you centralize reporting and monitoring of your data pipelines. A metadata management tool such as Google Cloud’s Data Catalog or Informatica’s Enterprise Data Catalog can help you better communicate the nuances of your data so users better understand which data resources are best fit for a given use case. This significantly reduces your pipeline’s governance burden by eliminating manual reporting processes that often result in inaccuracies or lagging updates.
Principle 5: Manage costs
The fifth principle to consider for pipeline development is manage costs. This encompasses both the cost of cloud resources and the staffing costs necessary to design, develop, deploy, and maintain your cloud resources. We believe that your goal should not necessarily be to minimize cost, but rather maximizing the value of your investment. This means maximizing the impact of every dollar spent by minimizing waste in cloud resource utilization and human time. There are several factors to consider when it comes to managing costs:
Use the right tool for the job – Different data ingestion pipelines will have different requirements for latency, uptime, transformations, etc. Similarly, different data pipeline tools have different strengths and weaknesses. Choosing the right tool for each data pipeline can help your pipelines operate significantly more efficiently. This can reduce your overall cost, free up staffing time to focus on the most impactful projects, and make your pipelines much more efficient.
Standardize resource labeling – Implement and utilize a consistent labeling schema across all tools and platforms to have the most comprehensive view of your organization’s spending. One example is requiring all resources to be labeled by the cost center or team at time of creation. Consistent labeling allows you to monitor your spend across different teams and calculate the overall value of your cloud spending.
Implement cost controls – If available, leverage cost controls to prevent errors that result in unexpectedly large bills.
Capture cloud spend – Capture your spend on all cloud resource utilization for internal analysis using a cloud data warehouse and a data visualization tool. Without it, you won’t understand the context of changes in cloud spend and how they correlate with changes in business.
Make cost management everyone’s job – Managing costs should be part of the responsibilities of everyone who can create or utilize cloud resources. To do this well, we recommend making cloud spend reporting more transparent internally and/or implementing chargebacks to internal cost centers based on utilization.
Long-term, the increased granularity in cost reporting available within Google Cloud can help you better measure your key performance indicators. You can shift from cost-based reporting (i.e. – “We spent $X on BigQuery storage last month”) to value-based reporting (i.e. – “It costs $X to serve customers who bring in $Y revenue”).
The sixth principle is leverage continually improving services. Cloud services are consistently improving their performance and stability, even if some of these improvements are not obvious to users. These improvements can help your pipelines run faster, cheaper, and more consistently over time. You can take advantage of the benefits of these improvements by:
Automating both your pipelines and pipeline management: Not only should data pipelines be automated, but almost all aspects of managing your pipelines can also be automated. This includes pipeline/data lineage tracking, monitoring, cost management, scheduling, access management and more. This helps reduce long-term operational costs of each data pipeline that can significantly alter your value proposition and prevent any manual configurations from negating the benefits of later product improvements.
Minimizing pipeline complexity whenever possible: While ingestion pipelines are relatively easy to develop using UI-based or managed services, they also require continued maintenance as long as they are in use. The most easily maintained data ingestion pipelines are typically the ones that minimize complexity and leverage automatic optimization capabilities. Any transformation in a data ingestion pipeline is a manual optimization of the pipeline that may struggle to adapt or scale as the underlying services improve. You can minimize the need for such transformations by building ELT (extract, load, transform) pipelines rather than ETL (extract, transform, load) pipelines. This pushes transformations down to the data warehouse that is use a specifically optimized query engine to transform your data rather than manually configured pipelines.
Data analytics has become a major gamechanger for the cryptocurrency industry. Traders and miners have discovered a number of advantages of using big data and AI tools to improve their profitability.
One of the newest applications of data analytics in cryptocurrency mining is with yield farming. Many crypto enthusiasts have found that it can help them lift their ROI considerably and address many of the problems that they face.
Data Analytics Helps Set the Future of Yield Farming for Cryptocurrency Traders
Decentralized finance (DeFi) has lately risen due to new developments like liquidity mining, which is both creative and dangerous. Stick or lending crypto assets to produce significant returns or rewards in different cryptocurrencies is yield farming. As a result of yield farming, the DeFi industry, which is still in its infancy, expects its market valuation to rise to $10 billion by 2020. New developments in data analytics and machine learning have helped accelerate this growth.
To put it simply, yield farming protocols encourage liquidity providers (LP) to stake or lock up their crypto assets in a pool of liquidity based on smart contracts. A part of the transaction costs, interest from the lenders, or a governance token can be used as incentives (see liquidity mining below). These profits are shown as a yearly percentage return (APY). The right data analytics tools can help investors identify the best trading opportunities and solidify their ROI. Investors’ returns fall in value when more money is added to the linked liquidity pool.
Stablecoins like USDT, DAI, and USDC were the most popular choices for early-yield investors. These investors will have an even better chance of achieving profitability if they use data-driven predictive analytics models to properly forecast asset prices. DeFi protocols that are now popular functions on the Ethereum network provide governance tokens for so-called liquidity mining. For the exchange of supplying liquidity to decentralized exchanges, tokens are farmed in these liquidity pools (DEXs).
Data analytics can help with the mining process as well. They can solve the puzzles needed to earn cryptocurrencies a lot more quickly. Big data technology can also help streamline computing resources to minimize energy use and therefore reduce the costs of the mining process.
Yield farming participants that gain token incentives as an additional remuneration are said to be engaged in liquidity mining, which became popular after Compound began releasing its rapidly rising governance token, COMP, to its platform users. This has been one of the biggest breakthroughs in the use data technology in cryptocurrency mining.
Liquidity providers in yield farming protocols are now typically rewarded with governance tokens, which can be exchanged on centralized exchanges like Binance and decentralized exchanges like Uniswap.
With 123yield, you’ll be the first. By participating in staking, users may take data-driven approaches to mining and earn incentives as they utilize the network. An increasing range of PoS and DPoS assets may be safely staked by users while also receiving 123 tokens as additional incentives. Many cross-chain assets may be earned by staking the users’ 123 tokens. The platform aggregates user stake interests, which then delegates assets to approved validators on their behalf.
It’s a peer-to-peer crypto asset exchange platform that works seamlessly. 123swap uses the collective expertise of the crowd to build a web 3.0 economy that is transparent, community-governed, and decentralized. Because of this, it is based on the Binance Smart Chain.
The 123swap platform is self-funded to a large extent and requires oversight to proceed. It means that the pre-sale will be the primary selling period for tokens. Both the general public and venture capitalists will have equal rights. To save and build money jointly, 123swap offers individuals financial power back.
123yield is easy to use, safe, and devoid of complications. No action is required on behalf of the users, who can stake their 123tokens every day and earn USDT incentives in return. Because of this, the platform keeps a tiny portion of the profit to meet the considerable operational, technological, and legal expenses.
Developments in Big Data Technology Have Helped Improve Cryptocurrency Mining
There is no question that big data has had a huge influence on the direction of the cryptocurrency industry. The DeFi industry, which is still in its infancy, expects its market valuation to rise to $10 billion by 2020. The evolution of data analytics technology has played a huge role in this process.
123swap is a peer-to-peer crypto asset exchange platform that works seamlessly. 123swap uses the collective expertise of the crowd to build a web 3.0 economy that is transparent, community-governed, and decentralized. Yield farming protocols encourage liquidity providers (LP) to stake or lock up their crypto assets in a liquidity pool based on smart contracts. By participating in staking, users may earn incentives as they utilize the network, which is one of the biggest opportunities for data-driven cryptocurrency miners and traders.
The pre-sale will be the primary selling period for tokens. For every stake a user makes with the help of another user’s link, another user receives 10% of the staked money. Because of this, the platform keeps a tiny portion of the profit to meet the considerable operational, technological, and legal expenses.
Data science careers used to be extremely selective and only those with certain types of traditional credentials were ever considered. While some might suggest that this discouraged those with hands-on experience from ever breaking into the field, it did at least help some companies glean a bit of information about potential hires. Now, however, an increasingly large number of people breaking into the field of data sciences actually aren’t themselves scientists.
Many come from a business or technical background that has very little to do with traditional academic pursuits. What these prospects lack in classroom education they more than make up for with hands-on experience, which has put them in heavy demand when it comes to hire people for firms that need to tackle data analysis tasks on a regular basis. With 89 percent of recruiters saying that they need specialists who also have plenty of soft skills, it’s likely that a greater percentage of outside hires may make it into the data sciences field as a whole.
Moving From One Career to Another
The business and legal fields increasingly require employees to have strong mathematical skills, which has encouraged people to learn various types of skills that they might not otherwise have had. Potential hires who are constantly adding new skills to their personal set and practicing them are among those who are most likely to be able to land a new job in the field of data sciences in spite of the fact that they don’t normally have much in the way of tech industry experience.
This is especially true of anyone who needs to perform analytic work in a very specific field. Law offices who want to apply analytic technology to injury claims would more than likely want to work with someone who has a background in these claims because they would be most capable with the unique challenges posed by accident suits. The same would go for those in healthcare.
Providers have often expressed an interest in finding data analysis specialists who also understand the challenges associated with prescription side-effect reporting systems and patient confidentiality laws. By hiring someone who has worked in a medical office, organizations that are concerned with these rather unique problems posed by these issues. The same is probably true of those who work in precision manufacturing and even food services.
By offering jobs to those who previously handled other unrelated responsibilities in these industries, some firms now say that they’re hiring well-rounded individuals who know about customer interactions as well as how to draw conclusions from visualizations. Perhaps most importantly, though, they’re putting themselves in a better position to survive any labor shortages that the data science field might be experiencing.
Weathering Changes in the Labor Market
While countless individuals naturally always struggle to find their dream job, the market currently seems to be in favor of those who want to transition into a more technically-oriented position. Firms that have to enlarge their IT departments might be feeling the crunch, so creating a resume might be all it takes for someone to land a new job. Since companies and NGOs have to compete for a relatively small number of prospects, it’s making sense for them to hire those who might not have otherwise even thought about working in the tech industry.
Firms that find themselves in this position might not have been able to get anyone to fill these jobs if they didn’t do so. That’s also creating room for something of a cottage industry of data scientists.
The Growth of Non-traditional Data Science Firms
Companies that perform analytics on behalf of someone else are starting to become rather popular. Considering the rise of tracking-related laws, small business owners might look to them as a way to ensure compliance. Anything that they do on behalf of someone else usually has to be compliant with all of these rules per the terms of the agreed upon contract. This takes at least some of the burden off of companies that have little to no experience at all with monetizing their data and avoiding any legal troubles associated with doing so.
While it’s likely that many of these smaller analysis offices will eventually merge together, the fact of the matter remains that they’re growing for the time being. As they do, they’ll probably create any number of additional positions for those looking to break into the data science field regardless of just how far their old careers were from the tech industry.
The global cryptocurrency market is growing rapidly. It is projected to be worth nearly $5 billion by 2030. A lot of factors are playing a role in this growth, including new advances in blockchain and the introduction of new blockchain ETFs.
Blockchain ETFs are among the most popular new ways for everyday investors to diversify their portfolios with blockchain and cryptocurrency investments. However, many are left asking what exactly are blockchain ETFs, and why should I be buying them?
Many Different Ways Now Available to Invest in Blockchain Technology
Today, there are many different ways that investors can incorporate cryptocurrencies into their portfolios. The traditional method has always been the direct buying and selling of specific cryptocurrencies like Bitcoin and Ethereum.
This direct exposure to cryptocurrencies is a simple matter of buy low and sell high. However, this opens traders up to significant risk, given how turbulent individual cryptocurrencies can be. It also requires at least a little technical know-how to safely and securely purchase, hold, and sell cryptocurrencies directly.
Buying cryptocurrencies as a speculative investment is just one of the ways that traders can invest in blockchain technology. Alternatively, they can purchase stocks in crypto and blockchain companies. The April 2021 public listing of cryptocurrency exchange Coinbase on the Nasdaq Stock Market is one major example of investing in blockchain technology through a company’s stock.
Investors can also incorporate blockchain exposure into their portfolio in smaller degrees by investing in companies that are only partially or tangentially tied to blockchain technology. Major stocks like Microsoft, Visa, PayPal, and Square all have various blockchain involvement that provides exposure while maintaining security through their strong core business operations.
Of course, picking stocks isn’t for everyone. In fact, it’s not a recommended way to invest for anyone but professional traders. Most everyday investors instead rely on various types of funds that group investments based on a number of factors. Among these are blockchain ETFs.
ETFs Provide Attractive Investment Opportunities Across All Industries
Exchange-traded funds (ETFs) aren’t anything exclusive to blockchain technology. They’re just another type of financial instrument that investors can purchase, designed to provide both growth and security. Blockchain or otherwise, they’re some of the most popular investment assets available today.
An ETF will be made up of investments in a variety of stocks and other assets. The specific assets are chosen to represent some commodity, technology, or strategy. One might imagine investing in every company across a specific industry in order to capture the growth of that industry but remain secure against the failings of individual companies.
ETFs are in some ways similar to investing in traditional mutual funds or other combined assets but generally offer reduced expense ratios. They also offer more variety and versatility, allowing investors to implement more diverse investment strategies.
Blockchain ETFs Provide Excellent Growth While Mitigating Crypto Risks
A blockchain ETF is, quite simply, an ETF that tries to capture the growth of blockchain technology. These instruments accomplish this by selecting a variety of companies with varying levels of blockchain involvement.
This can include both large-cap stocks like Microsoft and Visa, which deliver security and steady growth, and smaller stocks that are more directly tied to blockchain technology. With this strategy, investors can profit as blockchain technology grows without taking on the excessive risk of any individual blockchain venture failing.
There are many different blockchain ETFs available today, with varying levels of blockchain exposure. While some take more high-risk and potentially high-reward strategies than others, they are all relatively secure compared to investing directly in cryptocurrencies or even individual stocks.
The highest performing ETFs in this category have done remarkably well, with 3-year returns ranging from 50 to 150%. This is performance far beyond what most investments could possibly claim to offer. On average, a typical ETF might have been expected to deliver a 3-year return of 30 percent.
A key point to remember here is that these ETFs provide excellent security. While it’s true that an expertly timed investment in Bitcoin could have far overshadowed these gains, there are many investors out there who have incurred major losses trying to time the market. In many cases these losses are incurred when uneducated investors fall prey to various schemes like “Bitcoin Prime” which was recently labeled as a “blacklisted scam” by review website ScamCryptoRobots.com
Choosing the Right Way to Handle Your Blockchain Investments
Blockchain ETFs are just one more way that you can choose to invest in blockchain technology. Like most ETFs, they could find a place in essentially any portfolio. Those considering whether to buy cryptocurrencies directly or mitigate risks with an ETF are going to have to make their decision based on their own investment goals and risk tolerance.