At Google Cloud Next, we saw a continuation of the trends we’ve seen with recent announcements from other cloud data platforms like Snowflake. Google Cloud Platform (GCP) is infusing its suite of AI tools across their product portfolio making it easier for developers and non-developers alike to increase their productivity leveraging AI, as well as create and deploy AI powered applications. Consistent with GCP’s past focus and DNA, many of these tools are aimed at developers or teams with a heavily technical skillset, with only a few announcements really focused on business users.

Relevant Announcements

In the data and analytics space, there were a few key announcements:

• Introduction of BigQuery Studio which aims to create a holistic workbench for data teams across data engineering, data science, machine learning, and AI.
• Enhancements to Colab with a new Enterprise tier promising enhanced governance, security, compliance, and collaboration features as well as tighter integration to Google Vertex AI platform
• Duet code generation for BigQuery queries in SQL and Python
• Duet AI code conversion for data modernization and migrations increasing the speed with which enterprises implement a cloud data platform
• Duet AI integration with Looker enabling business users the ability to query their data with natural language queries leveraging Looker’s unified semantic layer over enterprise data in BigQuery
• A better-together-story for AI which integrates open source models as well as first part and third party commercial models into Vertex AI (e.g. Meta’s LLAMA model, Antropic, Cohere, AI21)
• AlloyDB AI which enables building AI applications using familiar Postgres features
• Improvements to GCP’s first-party TPU architecture and offerings from NVIDIA including new offerings powered by the latest NVIDIA H100 for enterprises looking to train sophisticated LLMs and other AI models

Previous announcements to highlight: cross-cloud data access in BigQuery, BigQuery Omni allowing access to structured and unstructured data, and the ability to use pre-trained Vertex AI models through BigQuery ML. Now with BigQuery Studio, they’re attempting to create a single workbench for data teams to collaborate.

Our Take

Similar to Snowflake, Google Cloud Platform has set the vision for BigQuery to become the center of the universe when it comes to an enterprise’s data. And just like Snowflake, GCP is pursuing a better together story integrating best in class third-party and open source technology in addition to first-party Google developed models and features. However the dizzying array of tools and platforms still feels disjointed and complicated. It’s difficult to keep everything straight and deciding when to use which tool can be a challenge for folks who are not GCP experts. While it’s clear that the services integrate well together, the GCP data analytics suite still seems aimed at developers who understand how the powerful “Lego blocks” that GCP offers fit together into a holistic solution.

How DAS42 Can Help

We are Looker and GCP experts. Starting with your most pressing business needs, DAS42 can create a holistic data architecture on the Google CloudGCP Platform centered on BigQuery and Looker that will move your business forward. By layering on best practices, data governance, and enablement in addition to technology, we ensure that everyone throughout your organization can adopt modern data tools infusing data-driven decision making to all aspects of your business.