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HomeCloud ComputingAnnouncing BigQuery Studio — a collaborative analytics workspace to accelerate data-to-AI workflows

Announcing BigQuery Studio — a collaborative analytics workspace to accelerate data-to-AI workflows

Organizations that are effective at using data and AI are more profitable than their competitors and see improved performance across a variety of business metrics, according to recent research: Already, 81% of organizations have increased their data and analytics investments over the previous two years. However, many organizations are still struggling to extract the full business value of data, with over 40% citing disparate analytics tools and data sources, and poor data quality as their biggest challenges.

Google Cloud is in a unique position to offer a unified, intelligent, open, and secure data and AI cloud for organizations. Thousands of customers across industries worldwide use Dataproc, Dataflow, BigQuery, BigLake and Vertex AI for data-to-AI workflows. Today, we are excited to announce BigQuery Studio — a unified, collaborative workspace for Google Cloud’s data analytics suite that helps accelerate data to AI workflows from data ingestion and preparation to analysis, exploration and visualization — all the way to ML training and inference. It allows data practitioners to: 

Use SQL, Python, Spark or natural language directly within BigQuery and leverage those code assets easily across Vertex AI and other products for specialized workflows

Extend software development best practices such as CI/CD, version history and source control to data assets, enabling better collaboration 

Uniformly enforce security policies and gain governance insights through data lineage, profiling and quality, right inside BigQuery

Single interface for all data teams

Disparate tools create inconsistent experiences for analytics professionals, requiring them to use multiple connectors for data ingestion, switch between coding languages, and transfer data assets between systems. This significantly impacts time-to-value of organizations’ data and AI investments. 

BigQuery Studio addresses these challenges by bringing an end-to-end analytics experience in a single, purpose-built platform. It provides a unified workspace including a SQL and a notebook interface (powered by Colab Enterprise which is currently in preview), allowing data engineers, data analysts and data scientists to perform end-to-end tasks including data ingestion, pipeline creation, and predictive analytics, all using the coding language of their choice. 

For example, analytics users like data scientists can now use Python in a familiar Colab notebook environment for data analysis and exploration at petabyte-scale right inside BigQuery. BigQuery Studio’s notebook environment supports browsing of datasets and schema, autocompletion of datasets and columns, and querying and transformation of data. Furthermore, the same Colab Enterprise notebook can be accessed in Vertex AI for ML workflows such as model training and customization, deployment, and MLOps.

Notebook experience in BigQuery Studio

Additionally, by leveraging BigLake with built-in support for Apache Parquet, Delta Lake and Apache Iceberg, BigQuery Studio provides a single pane of glass to work with structured, semi-structured and unstructured data of all formats across cloud environments such as Google Cloud, AWS, and Azure. 

Shopify, a leading commerce platform, has been exploring how BigQuery Studio complements its existing BigQuery environment. 

“Shopify has invested in employing a team with a diverse array of skill sets to remain ahead of trends for data science and engineering. In early testing with BigQuery Studio, we liked Google’s ability to connect different tools for different users within a simplified experience. We see this as an opportunity to reduce friction across our team without sacrificing scale we expect from BigQuery” – Zac Roberts, Data Engineering Manager, Shopify

Maximize productivity and collaboration

BigQuery Studio improves collaboration among data practitioners by extending software development best practices such as CI/CD, version history and source control to analytics assets including SQL scripts, Python scripts, notebooks and SQL pipelines. Additionally, users will be able to securely connect with their favorite external code repositories, so that their code can never be out of sync.

Version control for data assets

In addition to enabling human collaborations, BigQuery Studio also provides an AI-powered collaborator for contextual chat and code assistance. Duet AI in BigQuery can understand the context of each user and their data, and uses it to auto-suggest functions, and code blocks for SQL and Python. Through the new chat interface, data practitioners can use natural language to get personalized real-time guidance on performing specific tasks, reducing the need for trial and error and searching documentation for a needle in a haystack.

Code composition, code completion, and chat interface in Duet AI in BigQuery

Unified security and governance

BigQuery Studio lets organizations derive trusted insights from trusted data by helping users understand data, identify quality issues, and diagnose problems. Data practitioners can track ​data ​lineage, profile data, and enforce data-quality constraints to help ensure that data is high-quality, accurate, and reliable. Later this year, BigQuery Studio will surface personalized metadata insights like summaries of datasets or recommendations for how to derive deeper analysis. 

Additionally, BigQuery Studio allows admins to uniformly enforce security policies for data assets by reducing the need to copy, move, or share data outside of BigQuery for advanced workflows. With unified credential management across BigQuery and Vertex AI, policies are enforced for fine-grained security without needing to manage additional external connections or service accounts. For example, using simple SQL in BigQuery, data analysts can now use Vertex AI’s foundational models for images, videos, text, and language translations for tasks like sentiment analysis and entity detection over BigQuery data without requiring to share data with third party services.

Data quality, lineage, and profiling

What BigQuery Studio customers are saying

“Our data & analytics team is ceaselessly committed to staying ahead of the curve of data engineering and data science. During our initial trials with BigQuery Studio, we were particularly impressed by Google’s prowess in integrating diverse tools into a singular, streamlined experience. This fusion not only diminishes friction but also significantly amplifies our team’s efficiency, a testament to the power of BigQuery.” – Vinícius dos Santos Mello, Staff Data Engineer, Hurb

“As an early adopter of BigQuery Studio, we were impressed with its ability to not only minimize friction but also ensure robust data protection and centralization. The added support for Pandas DataFrames will further streamline our processes, saving valuable time for our team to collaborate and stay ahead of the curve.” – Sr. Director Analytics Engineering, Aritzia

“Duet AI in BigQuery has helped our data team at L’Oréal accelerate our transformation by making it easier for us to explore, understand, and use our data. With Duet AI, we can quickly query our data to get the insights we need to make better decisions for our business. We are excited to continue working with Duet AI to further our transformation and achieve our business goals.” – Antoine Castex, Data Platform Architect, L’Oréal

Getting started

BigQuery Studio is now available for customers in preview. Check out the documentation to learn more and sign up to get started today.

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