The News: Google adds capabilities for BigQuery data platform for analytics and AI. Read the announcement information on the Google Cloud blog.
Google BigQuery Advances Analytics for AI
Analyst Take: At Google Next 2023, the company introduced additional advances for its BigQuery data platform. In the competitive world of Data Platforms where the focus is on AI, Google continues to add improvements for development of AI and analytics models. The important elements of the announcement include:
- BigQuery Studio was announced as a preview with integration of different functions to make it simpler to operate the end-to-end workflows for AI development. Specifically, BigQuery Studio provides a single interface for the different tools that are used in the workflow. In addition to consolidation for management of the different tools, BigQuery Studio also is integrated with Colab Enterprise, which is a new offering with security and compliance support for the Colab data science notebook developed by Google Research.
- Also added is access to Vertex AI foundation models directly from BigQuery, simplifying the process of creating new models built on foundation models such as PaLM 2.
- Model inference from BigQuery was enhanced with support for additional formats TensorFlow, ONNX, and XGBoost.
- Vector and semantic search were added to BigQuery for model tuning. This capability greatly improves the retrieval of unstructured data.
- Duet AI was announced in preview mode and is available in BigQuery. Duet AI is a collaboration tool that is generative AI-powered to provide fast data analysis and contextual assistance for writing SQL. Duet AI is used to understand data and usage patterns to determine the questions to ask via SQL statements.
Key Takeaways
A big takeaway is that Google has seen the need to simplify the AI/ML development environment by providing BigQuery Studio that integrates the operation of several of the Google tools in the workflow. Ease of use is an indicator of the evolution and maturing of Data Platforms used in AI/ML. This is a competitive area and continuing improvements are to be expected.
Additionally, evolving BigQuery as a data platform includes adding new capabilities such as Duet AI and support for use of foundational models.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Randy draws from over 35 years of experience in helping storage companies design and develop products. As a partner at Evaluator Group and now The Futurum Group, he spends much of his time advising IT end-user clients on architectures and acquisitions.
Previously, Randy was Vice President of Storage and Planning at Sun Microsystems. He also developed disk and tape systems for the mainframe attachment at IBM, StorageTek, and two startup companies. Randy also designed disk systems at Fujitsu and Tandem Computers.
Prior to joining The Futurum Group, Randy served as the CTO for ProStor, where he brought products to market addressing a long-term archive for Information Technology and the Healthcare and Media/Entertainment markets.
He has also written numerous industry articles and papers as an educator and presenter, and he is the author of two books: Planning a Storage Strategy and Information Archiving – Economics and Compliance. The latter is the first book of its kind to explore information archiving in depth. Randy regularly teaches classes on Information Management technologies in the U.S. and Europe.
