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

BigQuery Studio is a unified, collaborative workspace for Google Cloud’s data analytics suite. It 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. It also extends software development best practices such as CI/CD, version history and source control to data assets, enabling better collaboration. Additionally, it uniformly enforces security policies and gains governance insights through data lineage, profiling and quality, right inside BigQuery.

Here are 5 reasons that you must try BigQuery Studio:

• It is a unified workspace for all data analytics needs, 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.

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

• It uniformly enforces security policies and gains governance insights through data lineage, profiling and quality, right inside BigQuery.

• It provides a single interface for all data teams, which addresses the challenges of disparate tools creating inconsistent experiences for analytics professionals.

Notebook experience in BigQuery Studio

In addition, by using BigLake with built-in support for Apache Parquet, Delta Lake, and Apache Iceberg, BigQuery Studio provides a single interface for working with structured, semi-structured, and unstructured data of all formats across cloud environments such as Google Cloud, AWS, and Azure.

Productivity and collaboration

• BigQuery Studio improves collaboration among data practitioners.

• Extending software development best practices such as CI/CD, version history and source control to analytics assets.

• These assets include SQL scripts, Python scripts, notebooks and SQL pipelines.

• Additionally, users will be able to securely connect with their favorite external code repositories.

• Ensures that their code can never be out of sync.

Version control for data assets

Duet AI 

BigQuery Studio provides an AI-powered collaborator called Duet AI. Duet AI 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.

Explain a SQL query

You can prompt Duet AI in BigQuery to explain a SQL query in natural language. This explanation can help you understand a query whose syntax, underlying schema, and business context might be difficult to assess due to the length or complexity of the query.

1. In the Google Cloud console, go to the BigQuery page.

2. In the query editor, open or paste the query that you want explained.

3. Highlight the query that you want Duet AI to explain, and then click Explain this query.

More information for Duet AI

Write queries with Duet AI assistance

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

Unified security and governance

BigQuery Studio allows data analysts to 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. By combining the power of BigQuery and Vertex AI, BigQuery Studio provides a comprehensive platform for data analysis and machine learning.

Data quality, lineage, and profiling

Getting started

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

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

How does BigQuery compare to other data warehouses?

BigQuery is a good choice for businesses that need to analyze large amounts of data quickly and cost-effectively. It is also a good choice for businesses that need to scale their data warehouse as their business grows.

Here are some of the features that make BigQuery stand out:

• Google BigQuery is a serverless, multi-cloud data warehouse technology with machine learning capabilities.

• It supports partitioning, resulting in improved query performance.

• It is scalable and can execute real-time queries on petabytes of data relatively fast.

• It offers a wide range of security features, including column-level security, Cloud DPL, and encryption keys management.

• It integrates with a wide range of data integration tools, BI, and AI solutions.

• It allows for data streaming and data backup and recovery.

• It is suitable for corporations with varied workloads and those interested in effective data mining.

Here are some of the other data warehouses that are available:

Data Source

Each of these data warehouses has its own strengths and weaknesses. It is important to choose the data warehouse that best meets the needs of your business.

BigQuery can be connected with other GCP tools:

• The BigQuery Data Transfer Service: This service allows you to automatically transfer data from other GCP services, such as Cloud Storage and Cloud SQL, to BigQuery.

• The BigQuery API: This API allows you to programmatically interact with BigQuery from other applications.

• The BigQuery web UI: This UI allows you to manage your BigQuery data and jobs.

picture source

Conclusion

Other data warehouses that are available include Amazon Redshift, Microsoft Azure SQL Data Warehouse, and Snowflake. Each of these data warehouses has its own strengths and weaknesses, and it is important to choose the data warehouse that best meets the needs of your business.

For example, Amazon Redshift is a good choice for businesses that need to analyze data from a variety of sources, including Amazon S3 and Amazon RDS. Microsoft Azure SQL Data Warehouse is a good choice for businesses that are already using Microsoft Azure services. Snowflake is easy to set up and configure.