#datacanvas

Turn data into insights faster with the updated BigQuery data canvas.

Today's data-driven world requires quick insights. Many struggle to transform raw data into meaningful intelligence. Complex SQL queries, tedious investigations, and the split between technical and non-technical users slow progress. BigQuery Data Canvas is a visual workspace that democratises data research and unleashes BigQuery data potential. At Google Cloud Next '25 this month, Google Cloud introduced Data Canvas, powered by Gemini, with AI-assisted conversation. One question covers data exploration, visualisation, and workflow analysis in this chat experience.

Data canvas is a paradigm change for data practitioners, not just a new function. By seamlessly combining visual processes with BigQuery and Gemini, Google Cloud is turning raw data into valuable insights.

Key traits: In-depth

Explore the data canvas assistant's powers.

Gemini fuels AI data agents.

Google has added Gemini, their powerful AI model, to Data Canvas to boost data exploration. It lets you ask data questions, enhance queries using natural language, and obtain informative and useful replies. The Gemini-powered data canvas queries and provides dataset information when you select “Show me the top 10 customers by revenue”. Gemini's data discovery recommendations help you find relevant datasets.

The Gemini-powered AI conversation experience captures workflow analysis from data exploration to visualisation with one prompt. Not sure where to start? Start analysing your data by following the directions. BigQuery Data Canvas uses Gemini to generate natural language enquiries about your data and SQL queries based on your favourite tables. To answer your questions, Gemini might use many data sources.

You may also direct the system to transfer domain knowledge about your data to increase answer accuracy. You may tell Gemini that your company's fiscal year is not January–December using system instructions. Some system directives, such “always present findings with charts, use green colour for positive and red colour for negative,” might influence how your replies are presented and returned.

Complex Python analysis for forecasting and anomaly identification will soon be available in chat. If your inquiry is complex, Gemini chat assistant will use a Python code interpreter to reply. Turn this option on in the settings bar.

Visual query building: Explore several perspectives in one place. Imagine a single hub where you can filter, link, aggregate, or display data from several tables, each in its own container, on the same page for data analysis. Data Canvas uses a DAG (Directed Acyclic Graph) method instead of a linear one, allowing you to explore alternative views, return to prior phases, or compare outcomes.

Data may be added easily by finding and adding tables to the canvas. Starting with natural language queries to your data, Data Canvas will automatically generate the SQL, which you can analyse and edit. Our node-based technique lets SQL professionals and rookie analysts pursue insights without having to cope with complex query syntax.

Interactive visualisations: Instant insights

Data Canvas offers interactive tables, graphs, and charts. Customising visualisations, interactively examining data, and identifying patterns and irregularities are easy. Do you want to see regional sales distribution? Data Canvas automatically creates a chart using the “Region” and “Sales” variables. Just choose the best visualisation or make your own to watch your data come to life. These visualisations may be downloaded as PNGs or imported into Looker Studio for customisation and dissemination.

Implementing data canvas in practice

New AI functionalities on BigQuery data canvas may be used in many ways. Here are some industry-specific ideas to inspire you.

Telecom diagnostics and support: Faster service restoration

Consider a telecom support team helping clients. Using a data canvas, support tickets may be queried for customer phone, postcode, service, timestamp, and nearby mobile tower. BigQuery processes support tickets hourly. Each data item is maintained in a distinct node inside a canvas, so analysts may perform this study without switching query tabs. This visual method helps them find local outages, send personnel to the right towers, and fix service interruptions faster.

E-commerce analytics: Expanding sales and customer engagement

Imagine marketing personnel analysing customer purchasing data to optimise adverts. Data canvas lets them graphically mix customer and product information, filter by prior purchases, and examine demographic sales tendencies. By quickly identifying high-value customer categories, best-selling goods, and marketing success, they can make data-driven judgements.

Supply chain optimisation: logistics simplification

Logistics management may utilise Data Canvas to track inventories, delivery routes, and bottlenecks. Visualising supply chain data may boost efficiency, cut costs, and enhance delivery dates. They may also create interactive dashboards to track performance indicators and make real-time changes.

Gemini In BigQuery overview

The Gemini for Google Cloud product suite’s Gemini in BigQuery delivers AI-powered data management assistance. BigQuery ML supports text synthesis and machine translation using Vertex AI models and Cloud AI APIs in addition to Gemini help.

Gemini In BigQuery AI help

Gemini in BigQuery helps you do these with AI:

Explore and comprehend your data with insights. Generally accessible (GA) Data insights uses intelligent queries from your table information to automatically and intuitively find patterns and do statistical analysis. This functionality helps with early data exploration cold-start issues. Use BigQuery to generate data insights.

Data canvas lets BigQuery users find, transform, query, and visualize data. (GA) Use natural language to search, join, and query table assets, visualize results, and communicate effortlessly. Learn more at Analyze with data canvas.

SQL and Python data analysis help. Gemini in BigQuery can generate or recommend SQL or Python code and explain SQL queries. Data analysis might begin with natural language inquiries.

Consider partitioning, clustering, and materialized views to optimize your data infrastructure. BigQuery can track SQL workloads to optimize performance and cut expenses.

Tune and fix serverless Apache Spark workloads. (Preview) Based on best practices and past workload runs, autotuning optimizes Spark operations by applying configuration settings to recurrent Spark workloads. Advanced troubleshooting with Gemini in BigQuery can identify job issues and suggest fixes for sluggish or unsuccessful jobs. Autotuning Spark workloads and Advanced troubleshooting have more information.

Use rules to customize SQL translations. (Preview) The interactive SQL translator lets you tailor SQL translations with Gemini-enhanced translation rules. Use natural language prompts to define SQL translation output modifications or provide SQL patterns to search and replace. See Create a translation rule for details.

Gemini in BigQuery leverages Google-developed LLMs. Billion lines of open source code, security statistics, and Google Cloud documentation and example code fine-tune the LLMs.

Learn when and how Gemini for Google Cloud utilizes your data. As an early-stage technology, Gemini for Google Cloud products may produce convincing but false output. Gemini output for Google Cloud products should be validated before usage. Visit Gemini for Google Cloud and ethical AI for details.

Pricing

All customers can currently use GA features for free. Google will disclose late in 2024 how BigQuery will restrict access to Gemini to these options:

BigQuery Enterprise Plus version: This edition includes all GA Gemini in BigQuery functionalities. Further announcements may allow customers using various BigQuery editions or on-demand computation to employ Gemini in BigQuery features.

SQL code assist, Python code assist, data canvas, data insights, and data preparation will be included in this per-user per-month service. No tips or troubleshooting in this bundle.

84% of enterprises think generative AI would speed up their access to insights, and interestingly, 52% of non-technical users are already using generative AI to extract insightful data, according to Google’s Data and AI Trends Report 2024.

Google Cloud goal with Google’s Data Cloud is to transform data management and analytics by leveraging their decades of research and investments in AI. This will allow businesses to create data agents that are based on their own data and reinvent experiences. Google Cloud unveiled the BigQuery preview of Gemini during Google Cloud Next 2024. Gemini offers AI-powered experiences including data exploration and discovery, data preparation and engineering, analysis and insight generation throughout the data journey, and smart recommendations to maximize user productivity and minimize expenses.

Google Cloud is pleased to announce that a number of Gemini in BigQuery capabilities, including as data canvas, data insights and partitioning, SQL code generation and explanation, Python code generation, and clustering recommendations, are now generally available.

Let’s examine in more detail some of the features that Gemini in BigQuery offers you right now.

What distinguishes Gemini in BigQuery?

Gemini in BigQuery combines cutting-edge models that are tailored to your company’s requirements with the best of Google’s capabilities for AI infrastructure and data management.

Context aware: Interprets your intentions, comprehends your objectives, and actively communicates with you to streamline your processes.

Based on your data: Constantly picks up fresh information and adjusts to your business data to see possibilities and foresee problems

Experience that is integrated: Easily obtainable from within the BigQuery interface, offering a smooth operation across the analytics workflows

How to begin using data insights

Finding the insights you can gain from your data assets and conducting a data discovery process are the initial steps in the data analysis process. Envision possessing an extensive collection of perceptive inquiries, customized to your data – queries you were unaware you ought to ask! Data Insights removes uncertainty by providing instantaneous insights with pre-validated, ready-to-run queries. For example, Data Insights may suggest that you look into the reasons behind churn among particular customer groups if you’re working with a database that contains customer churn data. This is an avenue you may not have considered.

With just one click, BigQuery Studio’s actionable queries may improve your analysis by giving you the insights you need in the appropriate place.

Boost output with help with Python and SQL codes

Gemini for BigQuery uses simple natural language suggestions to help you write and edit SQL or Python code while referencing pertinent schemas and metadata. This makes it easier for users to write sophisticated, precise queries even with little coding knowledge, and it also helps you avoid errors and inconsistencies in your code.

With BigQuery, Gemini understands the relationships and structure of your data, allowing you to get customized code recommendations from a simple natural language query. As an illustration, you may ask it to:

“Generate a SQL query to calculate the total sales for each product in the table.”

“Use pandas to write Python code that correlates the number of customer reviews with product sales.”

Determine the typical journey duration for each type of subscriber.

BigQuery’s Gemini feature may also help you comprehend intricate Python and SQL searches by offering explanations and insights. This makes it simpler for users of all skill levels to comprehend the reasoning behind the code. Those who are unfamiliar with Python and SQL, or who are working with unknown datasets, can particularly benefit from this.

Analytics workflows redesigned using natural language

Data canvas, an inventive natural language-based interface for data curation, wrangling, analysis, and visualization, is part of BigQuery’s Gemini package. With the help of data canvas, you can organize and explore your data trips using a graphical approach, making data exploration and analysis simple and straightforward.

For instance, you could use straightforward natural language prompts to collect information from multiple sources, like a point-of-sale (POS) system; integrate it with inventory, customer relationship management (CRM) systems, or external data; find correlations between variables, like revenue, product categories, and store location; or create reports and visualizations for stakeholders, all from within a single user interface, in order to analyze revenue across retail stores.

Optimize analytics for swiftness and efficiency

Data administrators and other analytics experts encounter difficulties in efficiently managing capacity and enhancing query performance as data volumes increase. BigQuery’s Gemini feature provides AI-powered suggestions for partitioning and grouping your tables in order to solve these issues. Without changing your queries, these suggestions try to optimize your tables for quicker returns and less expensive query execution.

Beginning

Phased rollouts of the general availability of Gemini in BigQuery features will begin over the following few months, starting today with suggestions for partitioning and clustering, data canvas, SQL code generation and explanation, and Python code generation.

Currently, all clients can access generally accessible (GA) features at no additional cost. For further details, please refer to the pricing details.

A snippet of the presentation about data project management given at Frontiers of Interaction conference on September 21th.

https://www.slideshare.net/topix/the-data-ring-a-canvas-for-data-project