There has never been a more exciting time in technology, with AI creating entirely new ways to solve problems, engage customers, and work more efficiently.
However, most enterprises still struggle with siloed data, which stifles innovation, keeps vital insights locked away, and can reduce the value AI has across the business.
Discover a faster, smarter way to innovate
Google Cloud Cortex Framework accelerates your ability to unify enterprise data for connected insights, and provides new opportunities for AI to transform customer experiences, boost revenue, and reduce costs which can otherwise be hidden in your company’s data and applications.
Built on an AI-ready Data Cloud foundation, Cortex Framework includes what you need to design, build, and deploy solutions for specific business problems and opportunities including endorsed reference architectures and packaged business solution deployment content. In this blog, we provide an overview of Cortex Framework, and highlight some recent enhancements.
Get a connected view of your business with one data foundation
Cortex Framework enables one data foundation for businesses by bridging and enriching private, public, and community insights for deeper analysis.
Our latest release extends Cortex Data Foundation with new data and AI solutions for enterprise data sources including Salesforce Marketing Cloud, Meta, SAP ERP, and Dun & Bradstreet. Together this data unlocks insights across the enterprise and opens up opportunities for optimization and innovation.
New intelligent marketing use cases
Drive more intelligent marketing strategies with one data foundation for your enterprise data, including integrated sources like Google Ads, Campaign Manager 360, TikTok and now — Salesforce Marketing Cloud and Meta connectivity with BigQuery via Cortex Framework, with predefined data ingestion templates, data models and sample dashboards. Together with other business data available like sales and supply chain sources in Cortex Data Foundation, you can accelerate insights and answer questions like: How does my overall campaign and audience performance relate to sales and supply chain?
New sustainability management use cases
Want more timely insights into environment, social and governance (ESG) risks and opportunities? You can now manage ESG performance and goals with new vendor ESG performance insights using Dun & Bradstreet ESGranking data connected with your SAP ERP supplier data. Now with predefined data ingestion templates, data models and a sample dashboard focused on sustainability insights, for informed decision making. Answer questions like: “What is my raw material suppliers’ ESG performance against industry peers?” “What is their ability to measure and manage GHG emissions?” and “What is their adherence and commitment to environmental compliance and corporate governance?”
New simplified finance use cases
Simplify financial insights across the business to make informed decisions about liquidity, solvency, and financial flexibility to feed into strategic growth investment opportunities — now with predefined data ingestion templates, data models and sample dashboards to help you discern new insights with balance sheet and income statement reporting on SAP ERP data.
Accelerate AI innovation with secured data access
To help organizations build out a data mesh for more optimized data discovery, access control and governance when interacting with Cortex Data Foundation, our new solution content offers a metadata framework built on BigQuery and Dataplex that:
Organizes Cortex Data Foundation pre-defined data models into business domains
Augments Cortex Data Foundation tables, views and columns with semantic context to empower search and discovery of data assets
Enables natural language to SQL capabilities by providing logical context for Cortex Data Foundation content to LLMs and gen AI applications
Annotates data access policies to enable consistent enforcement of access controls and masking of sensitive columns
With a data mesh in place, Cortex Data Foundation models can allow for more efficiency in generative AI search and discovery, as well as fine-grained access policies and governance.
Data and AI brings next-level innovation and efficiency
Will your business lead the way? Learn more about our portfolio of solutions by tuning in to our latest Next ‘24 session – ANA107 and checking out our website.
In today’s data-driven world, organizations across industries are seeking ways to gain a deeper understanding of their customers. A Single Customer View (SCV) — also known as a 360-degree customer view — has emerged as a powerful concept in data engineering, enabling companies to consolidate and unify customer data from multiple siloed sources. By integrating various data points into a single, comprehensive view, organizations can unlock valuable insights, drive personalized experiences, and make data-informed decisions. In this blog post, we will take a look at how Telegraph Media Group (TMG)built a SCV using Google Cloud and what we learned from our experience.
TMG is the publisher of The Daily Telegraph, The Sunday Telegraph, The Telegraph Magazine, Telegraph.co.uk, and the Telegraph app. We operate as a subscription-based business, offering news content through a combination of traditional print media and digital channels, including a website and various mobile applications. TMG initially operated a free-to-air, advertising-based revenue model, but over time, this model became increasingly challenging. Like many news media publishers, we saw long-term trends, such as a declining print readership, diminishing ad yields for content publishers, and volatility in ad revenue — all of which make revenue projections uncertain and growth unpredictable.
In 2018, we set out a bold vision to become a subscriber-first business, with quality journalism at our heart, to build deeper connections with our subscribers at scale.By embracing a subscription approach, TMG aimed to establish a more predictable revenue stream and enhance its advertising offerings, which yield higher returns. Our goal was to reach one million subscriptions within five years, and we reached our milestone in August 2023.
The SCV platform we have engineered leverages two primary data resources: a customer’s digital behavior across all digital domains and TMG’s subscription data. Additionally, it integrates data from third-party sources, such as partner shopping websites and engagement products like fantasy football or puzzles. These diverse data sources play a vital role in enriching the platform’s understanding of our audience and delivering a comprehensive news experience.
We can conceptualize the entire process of building the SCV in a few stages:
Data collection: The initial stage involves gathering data from various sources and loading it into BigQuery, which serves as a data lake. Data is extracted from a variety of different sources, using multiple methods, such as databases, APIs, or files, and ingested into BigQuery for centralized storage and future processing.
Data transformation: In this stage, the data retrieved from BigQuery is processed and transformed according to defined business rules. The data is cleansed, standardized, and enriched to ensure its quality and consistency. The data is stored in a new dataset within BigQuery in a structured format known as the SCV data model, where it can be easily accessed and analyzed.
Data presentation: Once the data has been transformed and stored in the BigQuery data lake, it can be organized into smaller, specialized datasets, known as data marts. These data marts serve specific user groups or departments and provide them with tailored and pre-aggregated data for consumption by third-party activation tools, such as email marketing systems, alongside reporting and visualization tools that enable internal decision-making processes.
Stage 1: Data collection
All of TMG’s subscription data is stored in Salesforce. We implemented a streamlined process to gather this data and store it in BigQuery.
First, we utilize a pipeline comprising containerized Python applications that run on Apache Airflow (specifically, Cloud Composer) every minute. This pipeline retrieves updated data from the Salesforce API and transfers it to Pub/Sub, a messaging service within Google Cloud.
Second, we created a real-time pipeline with DataFlow that reads data from Pub/Sub and promptly updates various tables in BigQuery, enabling us to gain real-time insights into the data.
We also perform a daily batch ingestion from Salesforce to ensure data integrity. This practice allows us to have a comprehensive and complete view of the data, compensating for any potential data loss that may occur during real-time ingestion.
We employ a similar approach to ingesting data from both Adobe Analytics, which monitors user behavior on TMG websites and apps, and Adobe Campaign, which tracks user behavior on communication channels. Since real-time availability is not essential for these datasets, batch processing is deemed sufficient for their ingestion and processing. Additionally, similar ingestion methods are applied to other data sources to ensure a consistent and unified data pipeline.
Stage 2: Data transformation
We employ the open-source Data Build Tool (DBT) for transforming our data, utilizing the power of BigQuery through SQL. By leveraging DBT, we translate all of our business rules into SQL for efficient data transformation. The DBT pipelines are containerized applications that run on an hourly basis and are orchestrated using Cloud Composer, which is built on Apache Airflow. As a result, the output of these data pipelines is a relational model that resides in BigQuery, delivering streamlined and organized data for further analysis and processing. During data transformation, we employ several important pipelines, including:
Salesforce Snapshot: This pipeline generates a snapshot of the Salesforce data from both real-time and batch tables. The snapshot reflects the latest available data in Salesforce and serves as a valuable source for other pipelines in the transformation process.
Source: This pipeline creates a table to store the source data, including source original customer ID and new customer ID. This information plays a crucial role in identifying customers in the original data source.
Customer: This pipeline creates a table that captures and presents detailed information, providing a comprehensive view of their attributes and characteristics.
Contact: This pipeline creates multiple tables that store various contact details of the customers.
Content Interaction: This pipeline generates a table that captures the digital behavior of customers, including their interactions with different content, enabling deeper analysis of customer engagement and preferences.
Subscription Interaction: This pipeline creates a table that tracks and stores subscription-related events and their associated details, providing insights into customer subscription behavior and patterns.
Campaign Interaction: This pipeline creates a table that stores detailed information about events related to communication behavior within different channels, enabling analysis of customer engagement with campaigns and marketing initiatives.
Stage 3: Data presentation
Similar to the transformation layer, DBT pipelines play a crucial role in transforming data from the SCV data model into different data marts. These data marts serve as valuable resources for further analysis and are also consumed by third-party applications. Presently, the three main consumers of the SCV data are Adobe Campaign, Adobe Experience Platform, and Permutive.
Adobe Campaign utilizes the SCV data to effectively target customers by sending relevant campaigns and personalized offers. By leveraging the comprehensive customer insights derived from this data, Adobe Campaign optimizes customer engagement and facilitates targeted marketing efforts.
Adobe Experience Platform leverages the SCV data to deliver tailored experiences to customers visiting the website. By utilizing the rich customer information available, the Adobe Experience Platform customizes the website experience to cater to individual customer preferences, enhancing customer satisfaction and engagement.
Permutive primarily relies on SCV demographic data to target customers with tailored advertisements on the Telegraph website and application. Permutive creates customer segments and integrates with Google Ad Manager to deliver personalized ads.
Prior to the implementation of the SCV, these consumers depended on various data sources, which often resulted in using data that was several days old. This delay imposed limitations on their ability to target customers multiple times within a day. However, with the integration of the SCV, they now have direct access to near real-time data, allowing them to consume and utilize the data as frequently as every 30 minutes. This significant improvement in data freshness empowers TMG to deliver more timely and relevant experiences to our target audiences.
Challenges of creating a SCV
Building a Single Customer View brings forth various challenges, particularly in constructing a data model that meets current requirements while remaining adaptable for future needs. To address this, we prioritize careful extension of the data model, aiming to incorporate new requirements within the existing framework whenever possible. Additionally, determining the appropriate data to include in the SCV is also critical. While businesses may desire to include all customer data, we recognize the importance of avoiding noise and include only relevant and valuable data to maintain a clean SCV.
Managing customer preferences for communication is another significant issue to resolve. Within the SCV, for example, customer preferences dictate how TMG is authorized to engage with them. However, these preferences can vary across different channels, including third-party platforms, potentially conflicting with the preferences stored in our first-party data. To mitigate this, we establish and implement hierarchical rules to carefully define permissions for each communication channel, ensuring compliance and minimizing legal implications.
Efficiently matching customers from third-party data to TMG’s first-party data is also crucial for unifying customers across multiple sources. To tackle this challenge, we employ a combination of exact and fuzzy matching techniques. We implement fuzzy matching using BigQuery User-Defined Functions (UDFs), which allows us to apply various algorithms. However, processing fuzzy matching on large data volumes can be time-consuming. We are actively exploring different approaches to strike a balance between accuracy and processing time, optimizing the matching process and facilitating more efficient customer data integration.
In conclusion, implementing a SCV on Google Cloud empowers TMG to leverage customer data effectively, helping us to drive growth, enhance customer satisfaction, and stay competitive. By harnessing the rich insights derived from a SCV, companies can make data-informed decisions and deliver personalized experiences that resonate with their customers. Overcoming the challenges inherent in building an SCV enables businesses to unlock the full potential of their data and achieve meaningful outcomes.
Organizations are increasingly adopting streaming technologies, and Google Cloud offers a comprehensive solution for streaming ingestion and analytics. Cloud Pub/Sub is Google Cloud’s simple, highly scalable and reliable global messaging service. It serves as the primary entry point for you to ingest your streaming data into Google Cloud and is natively integrated with BigQuery, Google Cloud’s unified, AI-ready data analytics platform. You can then use this data for downstream analytics, visualization, and AI applications. Today, we are excited to announce recent Pub/Sub innovations answering customer needs for simplified streaming data ingestion and analytics.
One-click Streaming Import (GA)
Multi-cloud workloads are becoming a reality for many organizations where customers would like to run certain workloads (e.g., operational) on one public cloud and want to run their analytical workloads on another. However, it can be a challenge to gain a holistic view of their business data. Through data consolidation in one public cloud, you can run analytics across their entire data footprint. For Google Cloud customers it is common to consolidate data in BigQuery, providing a source of truth for the organization.
To ingest streaming data from external sources such as AWS Kinesis Data Streams into Google Cloud, you need to configure, deploy, run, manage and scale a custom connector. You also need to monitor and maintain the connector to ensure the streaming ingestion pipeline is running as expected. Last week, we launched a no-code, one-click capability to ingest streaming data into Pub/Sub topics from external sources, starting with Kinesis Data Streams. The Import Topics capability is now generally available (GA) and offers multiple benefits:
Simplified data pipelines: You can streamline your cross-cloud streaming data ingestion pipelines by using the Import Topics capability. This removes the overhead of running and managing a custom connector.
Auto-scaling: Streaming pipelines created with managed import topics scale up and down based on the incoming throughput.
Out-of-the-box monitoring: Three new Pub/Sub metrics are now available out-of-the-box to monitor your import topics.
Import Topics will support Cloud Storage as another external source later in the year.
Streaming analytics with Pub/Sub Apache Flink connector (GA)
Apache Flink is an open-source stream processing framework with powerful stream and batch processing capabilities, with growing adoption across enterprises. Customers often use Apache Flink with messaging services to power streaming analytics use cases. We are pleased to announce that a new version of the Pub/Sub Flink Connector is now GA with active support from the Google Cloud Pub/Sub team. The connector is fully open source under an Apache 2.0 license and hosted on our GitHub repository. With just a few steps, the connector allows you to connect your existing Apache Flink deployment to Pub/Sub.
The connector allows you to publish an Apache Flink output into Pub/Sub topics or use Pub/Sub subscriptions as a source in Apache Flink applications. The new GA version of the connector comes with multiple enhancements. It now leverages the StreamingPull API to achieve maximum throughput and low latency. We also added support for automatic message lease extensions to enable setting longer checkpointing intervals. Finally, the connector supports the latest Apache Flink source streaming API.
Enhanced Export Subscriptions experience
Pub/Sub has two popular export subscriptions — BigQuery and Cloud Storage. BigQuery subscriptions can now be leveraged as a simple method to ingest streaming data into BigLake Managed Tables, BigQuery’s recently announced capability for building open-format lakehouses on Google Cloud. You can use this method to transform your streaming data into Parquet or Iceberg format files in your Cloud Storage buckets. We also launched a number of enhancements to these export subscriptions.
BigQuery subscriptions support a growing number of ways to move your structured data seamlessly. The biggest change is the ability to write JSON data into columns in BigQuery without defining a schema on the Pub/Sub topic. Previously, the only way to get data into columns was to define a schema on the topic and publish data that matched that schema. Now, with the use table schema feature, Pub/Sub can write JSON messages to the BigQuery table using its schema. Basic types are supported now and support for more advanced types like NUMERIC and DATETIME is coming soon.
Speaking of type support, BigQuery subscriptions now handle most Avro logical types. BigQuery subscriptions now support non-local timestamp types (compatible with the BigQuery TIMESTAMP type) and decimal types (compatible with the BigQuery NUMERIC and BIGNUMERIC types, coming soon). You can use these logical types to preserve the semantic meaning of fields across your pipelines.
Another highly requested feature coming soon to both BigQuery subscriptions and Cloud Storage subscriptions is the ability to specify a custom service account. Currently, only the per-project Pub/Sub service account can be used to write messages to your table or bucket. Therefore, when you grant access, you enable anyone who has permission to use this project-wide service account to write to the destination. You may prefer to limit access to a specific service account via this upcoming feature.
Cloud Storage subscriptions will be enhanced in the coming months with a new batching option allowing you to batch Cloud Storage files based on the number of Pub/Sub messages in each file. You will also be able to specify a custom datetime format in Cloud Storage filenames to support custom downstream data lake analysis pipelines. Finally, you’ll soon be able to use topic schema to write data to your Cloud Storage bucket.
Getting started
We’re excited to introduce a set of new capabilities to help you leverage your streaming data for a variety of use cases. You can now simplify your cross-cloud ingestion pipelines with Managed Import. You can also leverage Apache Flink with Pub/Sub for streaming analytics use cases. Finally, you can now use enhanced Export Subscriptions to seamlessly get data in either BigQuery or Cloud Storage. We are excited to see how you use these Pub/Sub features to solve your business challenges.
BigQuery allows you to analyze your data using a range of large language models (LLMs) hosted in Vertex AI including Gemini 1.0 Pro, Gemini 1.0 Pro Vision and text-bison. These models work well for several tasks such as text summarization, sentiment analysis, etc. using only prompt engineering. However, in some scenarios, additional customization via model fine-tuning is needed, such as when the expected behavior of the model is hard to concisely define in a prompt, or when prompts do not produce expected results consistently enough. Fine-tuning also helps the model learn specific response styles (e.g., terse or verbose), new behaviors (e.g., answering as a specific persona), or to update itself with new information.
Today, we are announcing support for customizing LLMs in BigQuery with supervised fine-tuning. Supervised fine-tuning via BigQuery uses a dataset which has examples of input text (the prompt) and the expected ideal output text (the label), and fine-tunes the model to mimic the behavior or task implied from these examples.Let’s see how this works.
Feature walkthrough
To illustrate model fine-tuning, let’s look at a classification problem using text data. We’ll use a medical transcription dataset and ask our model to classify a given transcript into one of 17 categories, e.g. ‘Allergy/Immunology’, ‘Dentistry’, ‘Cardiovascular/ Pulmonary’, etc.
Dataset
Our dataset is from mtsamples.com as provided on Kaggle. To fine-tune and evaluate our model, we first create an evaluation table and a training table in BigQuery using a subset of this data available in Cloud Storage as follows:
code_block
<ListValue: [StructValue([(‘code’, “– Create a eval tablernrnLOAD DATA INTOrn bqml_tutorial.medical_transcript_evalrnFROM FILES( format=’NEWLINE_DELIMITED_JSON’,rn uris = [‘gs://cloud-samples-data/vertex-ai/model-evaluation/peft_eval_sample.jsonl’] )rnrn– Create a train tablernrnLOAD DATA INTOrn bqml_tutorial.medical_transcript_trainrnFROM FILES( format=’NEWLINE_DELIMITED_JSON’,rn uris = [‘gs://cloud-samples-data/vertex-ai/model-evaluation/peft_train_sample.jsonl’] )”), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e7e321dcc70>)])]>
The training and evaluation dataset has an ‘input_text’ column that contains the transcript, and a ‘output_text’ column that contains the label, or ground truth.
Baseline performance of text-bison model
First, let’s establish a performance baseline for the text-bison model. You can create a remote text-bison model in BigQuery using a SQL statement like the one below. For more details on creating a connection and remote models refer to the documentation (1,2).
For inference on the model, we first construct a prompt by concatenating the task description for our model and the transcript from the tables we created. We then use the ML.GENERATE_TEXT function to get the output. While the model gets many classifications correct out of the box, it classifies some transcripts erroneously. Here’s a sample response where it classifies incorrectly.
code_block
<ListValue: [StructValue([(‘code’, ‘PromptrnrnPlease assign a label for the given medical transcript from among these labels [Allergy / Immunology, Autopsy, Bariatrics, Cardiovascular / Pulmonary, Chiropractic, Consult – History and Phy., Cosmetic / Plastic Surgery, Dentistry, Dermatology, Diets and Nutritions, Discharge Summary, ENT – Otolaryngology, Emergency Room Reports, Endocrinology, Gastroenterology, General Medicine, Hematology – Oncology, Hospice – Palliative Care, IME-QME-Work Comp etc., Lab Medicine – Pathology, Letters, Nephrology, Neurology, Neurosurgery, Obstetrics / Gynecology, Office Notes, Ophthalmology, Orthopedic, Pain Management, Pediatrics – Neonatal, Physical Medicine – Rehab, Podiatry, Psychiatry / Psychology, Radiology, Rheumatology, SOAP / Chart / Progress Notes, Sleep Medicine, Speech – Language, Surgery, Urology]. TRANSCRIPT: rnINDICATIONS FOR PROCEDURE:, The patient has presented with atypical type right arm discomfort and neck discomfort. She had noninvasive vascular imaging demonstrating suspected right subclavian stenosis. Of note, there was bidirectional flow in the right vertebral artery, as well as 250 cm per second velocities in the right subclavian. Duplex ultrasound showed at least a 50% stenosis.,APPROACH:, Right common femoral artery.,ANESTHESIA:, IV sedation with cardiac catheterization protocol. Local infiltration with 1% Xylocaine.,COMPLICATIONS:, None.,ESTIMATED BLOOD LOSS:, Less than 10 ml.,ESTIMATED CONTRAST:, Less than 250 ml.,PROCEDURE PERFORMED:, Right brachiocephalic angiography, right subclavian angiography, selective catheterization of the right subclavian, selective aortic arch angiogram, right iliofemoral angiogram, 6 French Angio-Seal placement.,DESCRIPTION OF PROCEDURE:, The patient was brought to the cardiac catheterization lab in the usual fasting state. She was laid supine on the cardiac catheterization table, and the right groin was prepped and draped in the usual sterile fashion. 1% Xylocaine was infiltrated into the right femoral vessels. Next, a #6 French sheath was introduced into the right femoral artery via the modified Seldinger technique.,AORTIC ARCH ANGIOGRAM:, Next, a pigtail catheter was advanced to the aortic arch. Aortic arch angiogram was then performed with injection of 45 ml of contrast, rate of 20 ml per second, maximum pressure 750 PSI in the 4 degree LAO view.,SELECTIVE SUBCLAVIAN ANGIOGRAPHY:, Next, the right subclavian was selectively cannulated. It was injected in the standard AP, as well as the RAO view. Next pull back pressures were measured across the right subclavian stenosis. No significant gradient was measured.,ANGIOGRAPHIC DETAILS:, The right brachiocephalic artery was patent. The proximal portion of the right carotid was patent. The proximal portion of the right subclavian prior to the origin of the vertebral and the internal mammary showed 50% stenosis.,IMPRESSION:,1. Moderate grade stenosis in the right subclavian artery.,2. Patent proximal edge of the right carotid.rnrnResponsernRadiology’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e7e2d667070>)])]>
In the above case the correct classification should have been ‘Cardiovascular/ Pulmonary’.
Metrics-based evaluation for base modelTo perform a more robust evaluation of the model’s performance, you can use BigQuery’s ML.EVALUATE function to compute metrics on how the model responses compare against the ideal responses from a test/eval dataset. You can do so as follows:
code_block
<ListValue: [StructValue([(‘code’, ‘– Evaluate base modelrnrnSELECTrn *rnFROMrn ml.evaluate(MODEL bqml_tutorial.text_bison_001,rn (rn SELECTrn CONCAT(“Please assign a label for the given medical transcript from among these labels [Allergy / Immunology, Autopsy, Bariatrics, Cardiovascular / Pulmonary, Chiropractic, Consult – History and Phy., Cosmetic / Plastic Surgery, Dentistry, Dermatology, Diets and Nutritions, Discharge Summary, ENT – Otolaryngology, Emergency Room Reports, Endocrinology, Gastroenterology, General Medicine, Hematology – Oncology, Hospice – Palliative Care, IME-QME-Work Comp etc., Lab Medicine – Pathology, Letters, Nephrology, Neurology, Neurosurgery, Obstetrics / Gynecology, Office Notes, Ophthalmology, Orthopedic, Pain Management, Pediatrics – Neonatal, Physical Medicine – Rehab, Podiatry, Psychiatry / Psychology, Radiology, Rheumatology, SOAP / Chart / Progress Notes, Sleep Medicine, Speech – Language, Surgery, Urology]. “, input_text) AS input_text,rn output_textrn FROMrn `bqml_tutorial.medical_transcript_eval` ),rn STRUCT(“classification” AS task_type))’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e7e2d667490>)])]>
In the above code we provided an evaluation table as input and chose ‘classification‘ as the task type on which we evaluate the model. We left other inference parameters at their defaults but they can be modified for the evaluation.
The evaluation metrics that are returned are computed for each class (label). The results look like following:
Focusing on the F1 score (harmonic mean of precision and recall), you can see that the model performance varies between classes. For example, the baseline model performs well for ‘Autopsy’, ‘Diets and Nutritions’, and ‘Dentistry’, but performs poorly for ‘Consult – History and Phy.’, ‘Chiropractic’, and ‘Cardiovascular / Pulmonary’ classes.
Now let’s fine-tune our model and see if we can improve on this baseline performance.
Creating a fine-tuned model
Creating a fine-tuned model in BigQuery is simple. You can perform fine-tuning by specifying the training data with ‘prompt’ and ‘label’ columns in it in the Create Model statement. We use the same prompt for fine-tuning that we used in the evaluation earlier. Create a fine-tuned model as follows:
code_block
<ListValue: [StructValue([(‘code’, ‘– Fine tune a textbison modelrnrnCREATE OR REPLACE MODELrn `bqml_tutorial.text_bison_001_medical_transcript_finetuned` REMOTErnWITH CONNECTION `LOCATION. ConnectionID`rnOPTIONS (endpoint=”text-bison@001″,rn max_iterations=300,rn data_split_method=”no_split”) ASrnSELECTrn CONCAT(“Please assign a label for the given medical transcript from among these labels [Allergy / Immunology, Autopsy, Bariatrics, Cardiovascular / Pulmonary, Chiropractic, Consult – History and Phy., Cosmetic / Plastic Surgery, Dentistry, Dermatology, Diets and Nutritions, Discharge Summary, ENT – Otolaryngology, Emergency Room Reports, Endocrinology, Gastroenterology, General Medicine, Hematology – Oncology, Hospice – Palliative Care, IME-QME-Work Comp etc., Lab Medicine – Pathology, Letters, Nephrology, Neurology, Neurosurgery, Obstetrics / Gynecology, Office Notes, Ophthalmology, Orthopedic, Pain Management, Pediatrics – Neonatal, Physical Medicine – Rehab, Podiatry, Psychiatry / Psychology, Radiology, Rheumatology, SOAP / Chart / Progress Notes, Sleep Medicine, Speech – Language, Surgery, Urology]. “, input_text) AS prompt,rn output_text AS labelrnFROMrn `bqml_tutorial.medical_transcript_train`’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e7e2d667520>)])]>
The CONNECTION you use to create the fine-tuned model should have (a) Storage Object User and (b) Vertex AI Service Agent roles attached. In addition, your Compute Engine (GCE) default service account should have an editor access to the project. Refer to the documentation for guidance on working with BigQuery connections.
BigQuery performs model fine-tuning using a technique known as Low-Rank Adaptation (LoRA. LoRA tuning is a parameter efficient tuning (PET) method that freezes the pretrained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture to reduce the number of trainable parameters. The model fine-tuning itself happens on a Vertex AI compute and you have the option to choose GPUs or TPUs as accelerators. You are billed by BigQuery for the data scanned or slots used, as well as by Vertex AI for the Vertex AI resources consumed. The fine-tuning job creates a new model endpoint that represents the learned weights. The Vertex AI inference charges you incur when querying the fine-tuned model are the same as for the baseline model.
This fine-tuning job may take a couple of hours to complete, varying based on training options such as ‘max_iterations’. Once completed, you can find the details of your fine-tuned model in the BigQuery UI, where you will see a different remote endpoint for the fine-tuned model.
Endpoint for the baseline model vs a fine tuned model.
Currently, BigQuery supports fine-tuning of text-bison-001 and text-bison-002 models.
Evaluating performance of fine-tuned model
You can now generate predictions from the fine-tuned model using code such as following:
code_block
<ListValue: [StructValue([(‘code’, ‘SELECTrn ml_generate_text_llm_result,rn label,rn promptrnFROMrn ml.generate_text(MODEL bqml_tutorial.text_bison_001_medical_transcript_finetuned,rn (rn SELECTrn CONCAT(“Please assign a label for the given medical transcript from among these labels [Allergy / Immunology, Autopsy, Bariatrics, Cardiovascular / Pulmonary, Chiropractic, Consult – History and Phy., Cosmetic / Plastic Surgery, Dentistry, Dermatology, Diets and Nutritions, Discharge Summary, ENT – Otolaryngology, Emergency Room Reports, Endocrinology, Gastroenterology, General Medicine, Hematology – Oncology, Hospice – Palliative Care, IME-QME-Work Comp etc., Lab Medicine – Pathology, Letters, Nephrology, Neurology, Neurosurgery, Obstetrics / Gynecology, Office Notes, Ophthalmology, Orthopedic, Pain Management, Pediatrics – Neonatal, Physical Medicine – Rehab, Podiatry, Psychiatry / Psychology, Radiology, Rheumatology, SOAP / Chart / Progress Notes, Sleep Medicine, Speech – Language, Surgery, Urology]. “, input_text) AS prompt,rn output_text as labelrn FROMrn `bqml_tutorial.medical_transcript_eval`rn ),rn STRUCT(TRUE AS flatten_json_output))’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e7e2d667400>)])]>
Let us look at the response to the sample prompt we evaluated earlier. Using the same prompt, the model now classifies the transcript as ‘Cardiovascular / Pulmonary’ — the correct response.
Metrics based evaluation for fine tuned model
Now, we will compute metrics on the fine-tuned model using the same evaluation data and the same prompt we previously used for evaluating the base model.
code_block
<ListValue: [StructValue([(‘code’, ‘– Evaluate fine tuned modelrnrnrnSELECTrn *rnFROMrn ml.evaluate(MODEL bqml_tutorial.text_bison_001_medical_transcript_finetuned,rn (rn SELECTrn CONCAT(“Please assign a label for the given medical transcript from among these labels [Allergy / Immunology, Autopsy, Bariatrics, Cardiovascular / Pulmonary, Chiropractic, Consult – History and Phy., Cosmetic / Plastic Surgery, Dentistry, Dermatology, Diets and Nutritions, Discharge Summary, ENT – Otolaryngology, Emergency Room Reports, Endocrinology, Gastroenterology, General Medicine, Hematology – Oncology, Hospice – Palliative Care, IME-QME-Work Comp etc., Lab Medicine – Pathology, Letters, Nephrology, Neurology, Neurosurgery, Obstetrics / Gynecology, Office Notes, Ophthalmology, Orthopedic, Pain Management, Pediatrics – Neonatal, Physical Medicine – Rehab, Podiatry, Psychiatry / Psychology, Radiology, Rheumatology, SOAP / Chart / Progress Notes, Sleep Medicine, Speech – Language, Surgery, Urology]. “, input_text) AS prompt,rn output_text as labelrn FROMrn `bqml_tutorial.medical_transcript_eval`), STRUCT(“classification” AS task_type))’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e7e2d667c70>)])]>
The metrics from the fine-tuned model are below. Even though the fine-tuning (training) dataset we used for this blog contained only 519 examples, we already see a marked improvement in performance. F1 scores on the labels, where the model had performed poorly earlier, have improved, with the “macro” F1 score (a simple average of F1 score across all labels) jumping from 0.54 to 0.66.
Ready for inference
The fine-tuned model can now be used for inference using the ML.GENERATE_TEXT function, which we used in the previous steps to get the sample responses. You don’t need to manage any additional infrastructure for your fine-tuned model and you are charged the same inference price as you would have incurred for the base model.
To try fine-tuning for text-bison models in BigQuery, check out the documentation. Have feedback or need fine-tuning support for additional models? Let us know at bqml-feedback@google.com>.
Special thanks to Tianxiang Gao for his contributions to this blog.
Google Cloud is on a mission to help everyone build the skills they need for in-demand cloud jobs. Today, we’re excited to announce new learning opportunities that will help you gain these in-demand skills through new courses and certificates in AI, data analytics, and cybersecurity. Even better, we’re hearing from Google Cloud customers that they are eager to consider certificate completers for roles they’re actively hiring for, so don’t delay and start your learning today.
Google Cloud offers new generative AI courses
Introduction to Generative AI
Demand for AI skills is exploding in the market. There has been a staggering 21x increase in job postings that include AI technologies in 2023.1 To help prepare you for these roles, we’re announcing new generative AI courses on YouTube and Google Cloud Skills Boost, from introductory level to advanced. Once you complete the hands-on training, you can show off your new skill badges to employers.
Introductory (no cost!): This training will get you started with the basics of generative AI and responsible AI.
Intermediate: For Application Developers, and you will learn how to use Gemini for Google Cloud to work faster across networking, security, and infrastructure.
Advanced:For AI/ ML Engineers, and you will learn how to integrate multimodal prompts in Gemini into your workflow.
New AI-powered, employer-recognized Google Cloud Certificates
Gen AI has triggered massive demand for skilling, especially in the areas of cybersecurity and data analytics,2 where there are significant employment opportunities. In the U.S. alone:
There are over 505,000 open entry-level roles3 related to a Cloud Cybersecurity Analyst, with a median annual salary of $135,000.4
There are more than 725,000 open entry-level roles5 related to a Cloud Data Analyst, with a median annual salary of $85,700.6
Building on the success of the Grow with Google Career Certificates, our new Google Cloud Certificates in Data Analytics and Cybersecurity can help prepare you for these high-growth, entry-level cloud jobs.
A gen AI-powered learning journey
What better way to understand just how much AI can do for you than integrating it into your learning journey? You’ll get no-cost access to generative AI tools throughout your learning experience. For example, you can put your skills to use and rock your interviews with Interview Warmup, Google’s gen AI-powered interview prep tool.
Talent acquisition, reimagined
And while we’re at it, we’ll help connect you to jobs. Our new Google Cloud Affiliate Employer programunlocks access for certificate completers to apply for jobs with some top cloud employers, like the U.S. Department of the Treasury, Rackspace, and Jack Henry.
We’re also taking it one step further. Together, with the employers in the affiliate program, we’re helping reimagine talent acquisition through a new skills-based hiring effort. This new initiative uses Google Cloud technology to help move certificate completers through the hiring process. Here’s how it works: Certificate completers in select hiring locations will have the chance to take custom labs that represent on-the-job scenarios, specific to each employer partner. These labs will be considered the first stage in their hiring process. By matching candidates with the right skills to the right jobs, this initiative marks a major step forward in creating more access to job opportunities for cloud employers.
The U.S. Department of the Treasury will start using these new Google Cloud Certificates and labs for cyber and data analytics talent identification across the federal agency, per President Biden’s Executive Order on AI.
“In an age of rapid innovation and adoption of new technology offering the promise of improved productivity, it is imperative that we equip every worker with accessible training and development opportunities to understand and apply this new technology. We are partnering with Google to provide the new Cloud Certificates training for our current and future employees to accelerate their careers in cybersecurity and data analytics.” – Todd Conklin, Chief AI Officer and Deputy Assistant Secretary of Cybersecurity and Critical Infrastructure Protection, U.S. Department of the Treasury
No-cost access for higher education institutions worldwide
To expand access to these programs, educational institutions, as well as government and nonprofit workforce development programs across the globe, can offer these new certificates and gen AI courses at no cost. Learn more and apply today.
And in the U.S., learners who successfully complete a Google Cloud Certificate can apply for college credit,7 to have a faster and more affordable pathway to a degree.
“Purdue Global’s students have benefited greatly from the strong working relationship between Purdue Global and Google. Together, they were the pioneers in stacking Grow with Google certificates into four types of degree-earning credit certificates over the past two years. We believe these new Google Cloud Cybersecurity and Data Analytics Certificates will equip our working adult learners with the essential skills to move forward and succeed in today’s cloud-driven market.” – Frank Dooley, Chancellor of Purdue Global
Take the next steps to upskill and identify cloud-skilled talent
We’re helping to prepare new-to-industry talent for the most in-demand cloud jobs, expanding access to these opportunities globally, and pioneering a skills-based hiring effort with employers eager to hire them. Here’s how you can get started:
Learners: Preview the courses and certificates on Google Cloud YouTube and earn the full credential on Google Cloud Skills Boost to give yourself a headstart in the race to hire AI talent.
Higher education institutions and government / nonprofit workforce programs: Apply today to skill up your workforce at no cost.
Employers: Express interest to become a Google Cloud Affiliate Employer and be considered for our skills-based hiring pilot to connect with cloud-skilled talent.
Embeddings represent real-world objects, like entities, text, images, or videos as an array of numbers (a.k.a vectors) that machine learning models can easily process. Embeddings are the building blocks of many ML applications such as semantic search, recommendations, clustering, outlier detection, named entity extraction, and more. Last year, we introduced support for text embeddings in BigQuery, allowing machine learning models to understand real-world data domains more effectively and earlier this year we introduced vector search, which lets you index and work with billions of embeddings and build generative AI applications on BigQuery.
At Next ’24, we announced further enhancement of embedding generation capabilities in BigQuery with support for:
Multimodal embeddings generation in BigQuery via Vertex AI’s multimodalembedding model, which lets you embed text and image data in the same semantic space
Embedding generation for structured data using PCA, Autoencoder or Matrix Factorization models that you train on your data in BigQuery
Multimodal embeddings
Multimodal embedding generates embedding vectors for text and image data in the same semantic space (vectors of items similar in meaning are closer together) and the generated embeddings have the same dimensionality (text and image embeddings are the same size). This enables a rich array of use cases such as embedding and indexing your images and then searching for them via text.
You can start using multimodal embedding in BigQuery using the following simple flow. If you like, you can take a look at our overview video which walks through a similar example.
Step 0: Create an object table which points to your unstructured data You can work with unstructured data in BigQuery via object tables. For example, if you have your images stored in a Google Cloud Storage bucket on which you want to generate embeddings, you can create a BigQuery object table that points to this data without needing to move it.
To follow along the steps in this blog you will need to reuse an existing BigQuery CONNECTION or create a new one following instruction here. Ensure that the principal of the connection used has the ‘Vertex AI User’ role and that the Vertex AI API is enabled for your project. Once the connection is created you can create an object table as follows:
In this example, we are creating an object table that contains public domain art images from The Metropolitan Museum of Art (a.k.a. “The Met”) using a public Cloud Storage bucket that contains this data. The resulting object table has the following schema:
Let’s look at a sample of these images. You can do this using a BigQuery Studio Colab notebook by following instructions in this tutorial. As you can see, the images represent a wide range of objects and art pieces.
Image source: The Metropolitan Museum of Art
Now that we have the object table with images, let’s create embeddings for them.
Step 1: Create model To generate embeddings, first create a BigQuery model that uses the Vertex AI hosted ‘multimodalembedding@001’ endpoint.
Note that while the multimodalembedding model supports embedding generation for text, it is specifically designed for cross-modal semantic search scenarios, for example, searching images given text. For text-only use cases, we recommend using the textembedding-gecko@ model instead.
Step 2: Generate embeddings You can generate multimodal embeddings in BigQuery via the ML.GENERATE_EMBEDDING function. This function also works for generating text embeddings (via textembedding-gecko model) and structured data embeddings (via PCA, AutoEncoder and Matrix Factorization models). To generate embeddings, simply pass in the embedding model and the object table you created in previous steps to the ML.GENERATE_EMBEDDING function.
code_block
<ListValue: [StructValue([(‘code’, “CREATE OR REPLACE TABLE `bqml_tutorial.met_image_embeddings`rnASrnSELECT * FROM ML.GENERATE_EMBEDDING(rn MODEL `bqml_tutorial.multimodal_embedding_model`,rn TABLE `bqml_tutorial.met_images`)rnWHERE content_type = ‘image/jpeg’rnLimit 10000″), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e9804081430>)])]>
To reduce the tutorial’s runtime, we limit embedding generation to 10,000 images. This query will take 30 minutes to 2 hours to run. Once this step is completed you can see a preview of the output in BigQuery Studio. The generated embeddings have a dimension of 1408.
Step 3 (optional): Create a vector index on generated embeddings While the embeddings generated in the previous step can be persisted and used directly in downstream models and applications, we recommend creating a vector index for improving embedding search performance and enabling the nearest-neighbor query pattern. You can learn more about vector search in BigQuery here.
code_block
<ListValue: [StructValue([(‘code’, “– Create a vector index on the embeddingsrnrnCREATE OR REPLACE VECTOR INDEX `met_images_index`rnON bqml_tutorial.met_image_embeddings(ml_generate_embedding_result)rnOPTIONS(index_type = ‘IVF’,rn distance_type = ‘COSINE’)”), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e9803cc6fa0>)])]>
Step 4: Use embeddings for text-to-image (cross-modality) search You can now use these embeddings in your applications. For example, to search for “pictures of white or cream colored dress from victorian era” you first embed the search string like so:
code_block
<ListValue: [StructValue([(‘code’, ‘– embed search stringrnrnCREATE OR REPLACE TABLE `bqml_tutorial.search_embedding`rnASrnSELECT * FROM ML.GENERATE_EMBEDDING(rn MODEL `bqml_tutorial.multimodal_embedding_model`,rn (rn SELECT “pictures of white or cream colored dress from victorian era” AS contentrn )rn)’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e9803cc6bb0>)])]>
You can now use the embedded search string to find similar (nearest) image embeddings as follows:
code_block
<ListValue: [StructValue([(‘code’, ‘– use the embedded search string to search for imagesrnrnCREATE OR REPLACE TABLErn `bqml_tutorial.vector_search_results` ASrnSELECTrn base.uri AS gcs_uri,rn distancernFROMrn VECTOR_SEARCH( TABLE `bqml_tutorial.met_image_embeddings`,rn “ml_generate_embedding_result”,rn TABLE `bqml_tutorial.search_embedding`,rn “ml_generate_embedding_result”,rn top_k => 5)’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e9807772760>)])]>
Step 5: Visualize results Now let’s visualize the results along with the computed distance and see how we performed on the search query “pictures of white or cream colored dress from victorian era”. Refer the accompanying tutorial on how to render this output using a BigQuery notebook.
Image source: The Metropolitan Museum of Art
The results look quite good!
Wrapping up
In this blog, we demonstrated a common vector search usage pattern but there are many other use cases for embeddings. For example, with multimodal embeddings you can perform zero-shot classification of images by converting a table of images and a separate table containing sentence-like labels to embeddings. You can then classify images by computing distance between images and each descriptive label’s embedding. You can also use these embeddings as input for training other ML models, such as clustering models in BigQuery to help you discover hidden groupings in your data. Embeddings are also useful wherever you have free text input as a feature, for example, embeddings of user reviews or call transcripts can be used in a churn prediction model, embeddings of images of a house can be used as input features in a price prediction model etc. You can even use embeddings instead of categorical text data when such categories have semantic meaning, for example, product categories in a deep-learning recommendation model.
In addition to multimodal and text embeddings, BigQuery also supports generating embeddings on structured data using PCA, AUTOENCODER and Matrix Factorization models that have been trained on your data in BigQuery. These embeddings have a wide range of use cases. For example, embeddings from PCA and AUTOENCODER models can be used for anomaly detection (embeddings further away from other embeddings are deemed anomalies) and as input features to other models, for example, a sentiment classification model trained on embeddings from an autoencoder. Matrix Factorization models are classically used for recommendation problems, and you can use them to generate user and item embeddings. Then, given a user embedding you can find the nearest item embeddings and recommend these items, or cluster users so that they can be targeted with specific promotions.
To generate such embeddings, first use the CREATE MODEL function to create a PCA, AutoEncoder or Matrix Factorization model and pass in your data as input, and then use ML.GENERATE_EMBEDDING function providing the model, and a table input to generate embeddings on this data.
Getting started
Support for multimodal embeddings and support for embeddings on structured data in BigQuery is now available in preview. Get started by following our documentation and tutorials. Have feedback? Let us know what you think at bqml-feedback@google.com.
Delta Lake is an open-source optimized storage layer that provides a foundation for tables in lake houses and brings reliability and performance improvements to existing data lakes. It sits on top of your data lake storage (like cloud object stores) and provides a performant and scalable metadata layer on top of data stored in the Parquet format.
Organizations use BigQuery to manage and analyze all data types, structured and unstructured, with fine-grained access controls. In the past year, customer use of BigQuery to process multiformat, multicloud, and multimodal data using BigLake has grown over 60x. Support for open table formats gives you the flexibility to use existing open source and legacy tools while getting the benefits of an integrated data platform.This is enabled via BigLake — a storage engine that allows you to store data in open file formats on cloud object stores such as Google Cloud Storage, and run Google-Cloud-native and open-source query engines on it in a secure, governed, and performant manner. BigLake unifies data warehouses and lakes by providing an advanced, uniform data governance model.
This week at Google Cloud Next ’24, we announced that this support now extends to the Delta Lake format, enabling you to query Delta Lake tables stored in Cloud Storage or Amazon Web Services S3 directly from BigQuery, without having to export, copy, nor use manifest files to query the data.
Why is this important?
If you have existing dependencies on Delta Lake and prefer to continue utilizing Delta Lake, you can now leverage BigQuery native support. Google Cloud provides an integrated and price-performant experience for Delta Lake workloads, encompassing unified data management, centralized security, and robust governance. Many customers already harness the capabilities of Dataproc or Serverless Spark to manage Delta Lake tables on Cloud Storage. Now, BigQuery’s native Delta Lake support enables seamless delivery of data for downstream applications such as business intelligence, reporting, as well as integration with Vertex AI. This lets you do a number of things, including:
Build a secure and governed lakehouse with BigLake’s fine-grained security model
Securely exchange Delta Lake data using Analytics Hub
Run data science workloads on Delta Lake using BigQuery ML and Vertex AI
How to use Delta Lake with BigQuery
Delta Lake tables follow the same table creation process as BigLake tables.
Required roles
To create a BigLake table, you need the following BigQuery identity and access management (IAM) permissions:
Here is the DDL statement to create a Delta lake Table
code_block
<ListValue: [StructValue([(‘code’, ‘CREATE EXTERNAL TABLE `PROJECT_ID.DATASET.DELTALAKE_TABLE_NAME`rnWITH CONNECTION `PROJECT_ID.REGION.CONNECTION_ID`rnOPTIONS (rn format =”DELTA_LAKE”,rn uris=[‘DELTA_TABLE_GCS_BASE_PATH’]);’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e476f76ed00>)])]>
Querying Delta Lake tables
After creating a Delta Lake BigLake table, you can query it using GoogleSQL syntax, the same as you would a standard BigQuery table. For example:
code_block
<ListValue: [StructValue([(‘code’, ‘SELECT FIELD1, FIELD2 FROM `PROJECT_ID.DATASET.DELTALAKE_TABLE_NAME`’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e476f76e610>)])]>
You can also enforce fine-grained security at the table level, including row-level and column-level security. For Delta Lake tables based on Cloud Storage, you can also use dynamic data masking.
Conclusion
We believe that BigQuery’s support for Delta Lake is a major step forward for customers building lakehouses using Delta Lake. This integration will make it easier for you to get insights from your data and make data-driven decisions. We are excited to see how you use Delta Lake and BigQuery together to solve their business challenges. For more information on how to use Delta Lake with BigQuery, please refer to the documentation.
Acknowledgments: Mahesh Bogadi, Garrett Casto, Yuri Volobuev, Justin Levandoski, Gaurav Saxena, Manoj Gunti, Sami Akbay, Nic Smith and the rest of the BigQuery Engineering team.
Navigating the complexities of the data-to-insights journey can be frustrating. Data professionals spend valuable time sifting through data sources, reinventing the wheel with each new question that comes their way. They juggle multiple tools, hop between coding languages, and collaborate with a wide array of teams across their organizations. This fragmented approach is riddled with bottlenecks, preventing analysts from generating insights and doing high-impact work as quickly as they should.
Yesterday at Google Cloud Next ‘24, we introduced BigQuery data canvas, which reimagines how data professionals work with data. This novel user experience helps customers create graphical data workflows that map to their mental model while AI innovations accelerate finding, preparing, analyzing, visualizing and sharing data and insights.
Watch this video for a quick overview of BigQuery data canvas.
BigQuery data canvas: a NL-driven analytics experience
BigQuery data canvas makes data analytics faster and easier with a unified, natural language-driven experience that centralizes data discovery, preparation, querying, and visualization. Rather than toggling between multiple tools, you can now use data canvas to focus on the insights that matter most to your business. Data canvas addresses the challenges of traditional data analysis workflow in two areas:
Natural language-centric experience: Instead of writing code, you can now speak directly to your data. Ask questions, direct tasks, and let the AI guide you through various analytics tasks.
Reimagined user experience: Data canvas rethinks the notebook concept. Its expansive canvas workspace fosters iteration and easy collaboration, allowing you to refine your work, chain results, and share workspaces with colleagues.
For example, to analyze a recent marketing campaign with BigQuery data canvas, you could use natural language prompts to discover campaign data sources, integrate them with existing customer data, derive insights, collaborate with teammates and share visual reports with executives — all within a single canvas experience.
Natural language-based visual workflow with BigQuery data canvas
Do more with BigQuery data canvas
BigQuery provides a variety of features that can help analysts accelerate their analytics tasks:
Search and discover: Easily find the specific data asset visualization table or view that you need to work with. Or search for the most relevant data assets. Data canvas works with all data that can be managed with BigQuery, including BigQuery managed storage, BigLake, Google Cloud Storage objects, and BigQuery Omni tables. For example, you could use either of the follow inputs to pull data with data canvas:
Specific table: project_name.dataset_name.table_name
Search: “customer transaction data” or “projectid:my-project-name winter jacket sales Atlanta”
Explore data assets: Review the table schema, review their details or preview data and compare it side by side.
Generate SQL queries: Iterate with NL inputs to generate the exact SQL query you need to accomplish the analytics task at hand. You can also edit the SQL before executing it.
Combine results: Define joins with plain language instructions and refine the generated SQL as needed. Use query results as a starting point for further analysis with prompts like “Join this data with our customer demographics on order id.”
Visualize: Use natural language prompts to easily create and customize charts and graphs to visualize your data, e.g., “create a bar chart with gradient” Then, seamlessly share your findings by exporting your results to Looker Studio or Google Sheets.
Automated insights: Data canvas can interpret query results and chart data and generate automated insights from them. For example, it can look at the query results of sales deal sizes and automatically provide the insight “the median deal size is $73,500.”
Share to collaborate: Data analytics projects are often a team effort. You can simply save your canvas and share it with others using a link.
Popular use cases
While BigQuery data canvas can accelerate many analytics tasks, it’s particularly helpful for:
Ad hoc analysis: When working on a tight deadline, data canvas makes it easy to pull data from various sources.
Exploratory data analysis (EDA): This critical early step in the data analysis process focuses on summarizing the main characteristics of a dataset, often visually. Data canvas helps find data sources and then presents the results visually.
Collaboration: Data canvas makes it easy to share an analytics project with multiple people.
What our customers are saying
Companies large and small have been experimenting with BigQuery data canvas for their day-to-day analytics tasks and their feedback has been very positive.
Wunderkind, a performance marketing channel that powers one-to-one customer interactions, has been using BigQuery data canvas across their analytics team for several weeks and is experiencing significant time savings.
“For any sort of investigation or exploratory exercise resulting in multiple queries there really is no replacement [for data canvas]. [It] Saves us so much time and mental capacity!” – Scott Schaen, VP of Data & Analytics, Wunderkind
How Wunderkind accelerates time to insights with BigQuery data canvas
Veo, a micro mobility company that operates in 50+ locations across the USA, is seeing immediate benefits from the AI capabilities in data canvas.
“I think it’s been great in terms of being able to turn ideas in the form of NL to SQL to derive insights. And the best part is that I can review and edit the query before running it – that’s a very smart and responsible design. It gives me the space to confirm it and ensure accuracy as well as reliability!” – Tim Velasquez, Head of Analytics, Veo
Give BigQuery data canvas a try
To learn more, watch this video and check out the documentation. BigQuery data canvas is launching in preview and will be rolled out to all users starting on April 15th. Submit this form to get early access.
For any bugs and feedback, please reach out to the product and engineering team at datacanvas-feedback@google.com. We’re looking forward to hearing how you use the new data canvas!
How do you store the entire internet? That’s the problem our engineering team set out to solve in 2004 when it launched Bigtable, one of Google’s longest serving and largest data storage systems. As the internet — and Google — grew, we needed a new breed of storage solution to reliably handle millions of requests a second to store the ever-changing internet. And when we revealed its design to the world in a 2006 research paper, Bigtable kicked-off the Big Data revolution, inspiring the database architectures for NoSQL systems such as Apache HBase and Cassandra.
Twenty years later, Bigtable doesn’t just support Google Search, but also latency-sensitive workloads across Google such as Ads, Drive, Analytics, Maps, and YouTube. On Google Cloud, big names like Snap, Spotify and Shopify rely on Bigtable, now serving a whopping 7 billion queries per second at peak. On any given day, it is nearly impossible to use the internet without interacting with a Bigtable database.
Bigtable isn’t just for Big Tech, though. This year, our goal is to bring Bigtable to a much broader developer audience and range of use cases, starting with a number of capabilities that we announced this week at Google Cloud Next.
Introducing Bigtable Data Boost and Authorized Views
For one, Bigtable now supports Data Boost, a serverless way for users to perform analytical queries on their transactional data without impacting their operational workloads. Currently in preview, Data Boost makes managing multiple copies of data for serving and analytics a thing of the past. Further, Data Boost supports a requester-pays model, billing data consumers directly for their usage — a unique capability for an operational database.
Then, new Bigtable Authorized Views enable many data sharing and collaboration scenarios. For example, retailers can securely share sales or inventory data with each of their vendors, so they can more accurately forecast demand and resupply the shelves — without worrying about how much server capacity to provision. This type of use case is quite common for organizations with multiple business units, but sharing this level of data has traditionally required keeping copies of data in multiple databases, building custom application layers and billing components. Instead, with Bigtable Authorized Views and Data Boost, each vendor will get their own bill for the amount of data they process, with no negative impact on retailer’s operations. Bigtable Authorized Views make it easier to serve data from a single source of truth, with improved data governance and quality.
These features, along with the existing Request Priorities, stand to transform Bigtable into an all-purpose data fabric, or a Digital Integration Hub. Many Google Cloud customers already use Bigtable for their data fabrics, where its strong write performance, horizontal scalability and flexible schema make it an ideal platform for projects that ingest large amounts of data in batch from multiple sources or collate real-time streaming events. But businesses and their data evolve over time. New data sources are added through acquisitions, partnerships, new product launches, additional business metrics and ML features. To get the value out of data, you need to combine all the pieces and see the big picture — and do it in real-time. Bigtable has already solved the latency and database scaling problems, but features like Authorized Views and Data Boost help to solve data and resource governance issues.
During the preview, Data Boost is offered at no cost.
Boosting Bigtable performance for next-gen workloads
At Next, we also announced several Bigtable price-performance improvements. Bigtable now offers a new aggregate data type optimized for increment operations, which delivers significantly higher throughput and can be used to implement distributed counters and simplify Lambda architectures. You can also choose large nodes that offer more performance stability at higher server utilization rates, to better support spiky workloads. This is the first of workload-optimized node shapes that Bigtable will offer. All of these changes come on the heels of an increase in point-read throughput from 10K to 14K reads per second per node just a few months ago. Overall, these improvements mean lower TCO for a database already known for its price-performance.
These improvements could help power your modern analytics and machine learning (ML) workloads: ML is going real-time, and models are getting larger, with more and more variables that require flexible schemas and wide data structures. Analytics workloads are also moving towards wide-table designs with the so-called one big table (OBT) data model. Whether you need its flexible data model for very wide, gradually evolving tables; its scalable counters’ ability to provide real-time metrics at scale, or features like Data Boost and Request Priorities that allow seamless backfills and frequent model training (thereby combining real-time serving and batch processing into a single database), Bigtable simplifies the ML stack and reduces concept and data drift, uplifting ML model performance.
Eighty percent of data leaders believe that the lines between data and AI are blurring. Using large language models (LLMs) with your business data can give you a competitive advantage, but to realize this advantage, how you structure, prepare, govern, model, and scale your data matters.
Tens of thousands of organizations already choose BigQuery and its integrated AI capabilities to power their data clouds. But in a data-driven AI era, organizations need a simple way to manage all of their data workloads. Today, we’re going a step further and unifying key data Google Cloud analytics capabilities under BigQuery, which is now the single, AI-ready data analytics platform.BigQuery incorporates key capabilities from multiple Google Cloud analytics services into a single product experience that offers the simplicity and scale you need to manage structured data in BigQuery tables, unstructured data like images, audience and documents, and streaming workloads, all with the best price-performance.
BigQuery helps you:
Scale your data and AI foundation with support for all data types and open formats
Eliminate the need for upfront sizing and just simply bring your data, at any scale, with a fully managed, serverless workload management model and universal metastore
Increase flexibility and agility for data teams to collaborate by bringing multiple languages and engines (SQL, Spark, Python) to a single copy of data
Support the end-to-end data to AI lifecycle with built-in high availability, data governance, and enterprise security features
Simplify analytics with a unified product experience designed for all data users and AI-powered assistive and collaboration features
With your data in BigQuery, you can quickly and efficiently bring gen AI to your data and take advantage of LLMs. BigQuery simplifies multimodal generative AI for the enterprise by making Gemini models available through BigQuery ML and BigQuery DataFrames. It helps you unlock value from your unstructured data, with its expanded integration with Vertex AI’s document processing and speech-to-text APIs, and its vector capabilities to enable AI-powered search for your business data. The insights from combining your structured and unstructured data can be used to further fine-tune your LLMs.
Support for all data types and open formats
Customers use BigQuery to manage all data types, structured and unstructured, with fine-grained access controls and integrated governance. BigLake, BigQuery’s unified storage engine, supports open table formats which let you use existing open-source and legacy tools to access structured and unstructured data while benefiting from an integrated data platform. BigLake supports all major open table formats, including Apache Iceberg, Apache Hudi and now Delta Lake natively integrated with BigQuery. It provides a fully managed experience for Iceberg, including DDL, DML and streaming support.
Your data teams need access to a universal definition of data, whether in structured, unstructured or open formats. To support this, we are launching BigQuery metastore, a managed, scalable runtime metadata service that provides universal table definitions and enforces fine-grained access control policies for analytics and AI runtimes. Supported runtimes include Google Cloud, open source engines (through connectors), and 3rd party partner engines.
Use multiple languages and serverless engines on a single copy of data
Customers increasingly want to run multiple languages and engines on a single copy of their data, but the fragmented nature of today’s analytics and AI systems makes this challenging. You can now bring the programmatic power of Python and PySpark right to your data without having to leave BigQuery.
BigQuery DataFrames brings the power of Python together with the scale and ease of BigQuery with a minimum learning curve. It implements over 400 common APIs from pandas and scikit-learn by transparently and optimally converting methods to BigQuery SQL and BigQuery ML SQL. This breaks the barriers of client side capabilities, allowing data scientists to explore, transform and train on terabytes of data and processing horsepower of BigQuery.
Apache Spark has become a popular data processing runtime, especially for data engineering tasks. In fact, customers’ use of serverless Apache Spark in Google Cloud increased by over 500% in the past year.1BigQuery’s newly integrated Spark engine lets you process data using PySpark as you do with SQL. Like the rest of BigQuery, the Spark engine is completely serverless — no need to manage compute infrastructure. You can even create stored procedures using PySpark and call them from your SQL-based pipelines.
Make decisions and feed ML models in near real-time
Data teams are also increasingly being asked to deliver real-time analytics and AI solutions, reducing the time between signal, insight, and action. BigQuery now helps make real-time streaming data processing easy with new support for continuous SQL queries, an unbounded SQL query that processes data the moment it arrives via SQL statement. BigQuery continuous queries amplifies downstream SaaS applications, like Salesforce, with the real-time enterprise knowledge of your data and AI platform. In addition, to support open source streaming workloads, we are announcing a preview of Apache Kafka for BigQuery. Customers can use Apache Kafka to manage streaming data workloads and feed ML models without the need to worry about version upgrades, rebalancing, monitoring and other operational headaches.
Scale analytics and AI with governance and enterprise features
To make it easier for you to manage, discover, and govern data, last year we brought data governance capabilities like data quality, lineage and profiling from Dataplex directly into BigQuery. We will be expanding BigQuery to include Dataplex’s enhanced search capabilities, powered by a unified metadata catalog, to help data users discover data and AI assets, including models and datasets from Vertex AI. Column-level lineage tracking in BigQuery is now available in preview, which will be followed by a preview for lineage for Vertex AI pipelines. Governance rules for fine-grained access control are also in preview, allowing businesses to define governance policies based on metadata.
For customers looking for enhanced redundancy across geographic regions, we are introducing managed disaster recovery for BigQuery. This feature, now in preview, offers automated failover of compute and storage and will offer a new cross-regional service level agreement (SLA) tailored for business-critical workloads. The managed disaster recovery feature provides standby compute capacity in the secondary region included in the price of BigQuery’s Enterprise Plus edition.
A unified experience for all data users
As Google Cloud’s single integrated platform for data analytics, BigQuery unifies how data teams work together with BigQuery Studio. Now generally available, BigQuery Studio gives data teams a collaborative data workspace that all data practitioners can use to accelerate their data-to-AI workflows. BigQuery Studio lets you use SQL, Python, PySpark, and natural language in a single unified analytics workspace, regardless of the data’s scale, format or location. B All development assets in BigQuery Studio are enabled with full lifecycle capabilities, including team collaboration and version control. Since BigQuery Studio’s launch at Next ‘23, hundreds of thousands of users are actively using the new interface.2
Gemini in BigQuery for AI assistive and collaborative experiences
We announced several new innovations for Gemini in BigQuery that help data teams with AI-powered experiences for data preparation, analysis and engineering as well as intelligent recommendations to enhance user productivity and optimize costs. BigQuery data canvas, an AI-centric experience with natural language input, makes data discovery, exploration, and analysis faster and more intuitive. AI augmented data preparation in BigQuery helps users to cleanse and wrangle their data and build low-code visual data pipelines, or rebuild legacy pipelines. Gemini in BigQuery also helps you write and edit SQL or Python code using simple natural language prompts, referencing relevant schemas and metadata.
How Deutsche Telekom is innovating with the BigQuery platform
“Deutsche Telekom built a horizontally scalable data platform in an innovative way that was designed to meet our current and future business needs. With BigQuery at the center of our enterprise’s One Data Ecosystem, we created a unified approach to maintain a single source of truth while fostering de-centralized usage of data across all of our data teams. With BigQuery and Vertex AI, we built a governed and scalable space for data scientists to experiment and productionize AI models while maintaining data sovereignty and federated access controls. This has allowed us to quickly deploy practical usage of LLMs to turbocharge our data engineering life cycle and unleash new business opportunities.” – Ashutosh Mishra, VP of Data Architecture, Deutsche Telekom
Start building your AI-ready data platform
To learn more and start building your AI-ready data platform, start exploring the next generation of BigQuery today. Read more about the latest innovations for Gemini in BigQuery and an overview of what’s next for data analytics at Google Cloud.
1. Google internal data – YoY growth of data processed using Apache Spark on Google Cloud compared with Feb ‘23. 2. Since the August 2023 announcement of BigQuery Studio, monthly active users have continued to grow.
