Google Cloud Next Focuses Stack

Google Cloud’s conference in Las Vegas is three days long, from April 22 to April 24, 2026, and the sessions already posted show one clear pattern: Google wants customers building data analysis and artificial intelligence inside one house instead of moving between many tools. (googlecloudevents.com, cloud.google.com) That pitch starts with a simple idea: keep the data where it already lives. Google’s BigQuery service lets teams query large datasets with Structured Query Language, and Google now lets those same teams call Vertex Artificial Intelligence models from inside BigQuery instead of exporting data into a separate machine-learning system. (cloud.google.com) BigQuery Machine Learning is the bridge. It lets analysts build or call models with Structured Query Language, and Google’s current documentation says a “remote model” can point to a Vertex Artificial Intelligence model so training and prediction run in Vertex while the user stays in BigQuery. (cloud.google.com) Google has been tightening that link for years. In 2022, Google described five integrations between BigQuery and Vertex Artificial Intelligence, including shared data access, model training, deployment, and model management on one platform. (cloud.google.com) By 2024 and 2025, the bundle got broader. Google said BigQuery could use Gemini models through BigQuery Machine Learning, process documents and speech through Vertex Artificial Intelligence services, and run vector search so companies could build semantic search over their own business data. (cloud.google.com) The conference agenda shows that this is not just old plumbing dressed up as new marketing. One Google Cloud Next 2026 session says BigQuery can run entity extraction, classification, and document parsing on text, images, and Portable Document Format files with simple Structured Query Language commands tied to Gemini. (googlecloudevents.com) Another posted session goes one layer deeper into operations. It describes service reliability engineering workflows where BigQuery Machine Learning forecasts service-level objectives and Vertex Artificial Intelligence uses Gemini for automated root-cause analysis. (googlecloudevents.com) Google is also tightening the management layer around these tools. Its current documentation says BigQuery Machine Learning models can now be registered directly in the Vertex Artificial Intelligence Model Registry, which means teams can track BigQuery-built models beside Vertex-built models without exporting them first. (cloud.google.com) Security is being folded into the same surface too. Google’s event site says Next 2026 will feature advances in generative artificial intelligence, infrastructure, and security, and that mix fits the company’s broader push to sell one managed stack instead of separate point products. (googlecloudevents.com, cloud.google.com) The appeal is obvious for a company trying to ship something fast. If the database, the model, the notebook, the search layer, and the governance tools already talk to each other, a team can assemble a working system in weeks instead of stitching together five vendors over months. (cloud.google.com, cloud.google.com) The trade-off shows up later, when the shortcuts become habits. If your analysts write Structured Query Language that depends on BigQuery Machine Learning, your models sit in Vertex Artificial Intelligence, and your search and monitoring are wired to Google services, moving to another cloud stops looking like a software project and starts looking like a full kitchen remodel. (cloud.google.com, cloud.google.com, cloud.google.com)