#pg_lake

#pg_lake

Oracle firmly believes that MySQL's enduring strength arises from this vibrant global community. We are excited to work with the MySQL Community on the strategy we announced in Belgium, January 29, 2026, including adding more features and functionality, accelerating innovation directly in the MySQL core.

Then coming on to the next point, which is you can create your own sessions and user properties. Now you can do this in the GA4 interface under Explorations.

Databricks today announced the general availability of Lakebase on AWS, a new database architecture that separates compute and storage. The managed serverless Postgres service is designed to help organizations build faster without worrying about infrastructure management. When databases link compute and storage, every query must use the same CPU and memory resources. This can cause a single heavy query to affect all other operations. By separating compute and storage, resources automatically scale with the actual load.

Uber has built HiveSync, a sharded batch replication system that keeps Hive and HDFS data synchronized across multiple regions, handling millions of Hive events daily. HiveSync ensures cross-region data consistency, enables Uber's disaster recovery strategy, and eliminates inefficiency caused by the secondary region sitting idle, which previously incurred hardware costs equal to the primary, while still maintaining high availability. Built initially on the open-source Airbnb ReAir project, HiveSync has been extended with sharding, DAG-based orchestration, and a separation of control and data planes.

Developers have spent the past decade trying to forget databases exist. Not literally, of course. We still store petabytes. But for the average developer, the database became an implementation detail; an essential but staid utility layer we worked hard not to think about. We abstracted it behind object-relational mappers (ORM). We wrapped it in APIs. We stuffed semi-structured objects into columns and told ourselves it was flexible.

Snowflake and Google Cloud are deepening their collaboration by integrating the Google Gemini 3 model into Snowflake Cortex AI. Companies can now develop generative AI applications without moving data between platforms. The integration of Gemini 3 into Snowflake Cortex AI marks a significant step forward in both parties' AI strategy. Developers will have access to Google's large language model within Snowflake's secure data environment. This enables building, deploying, and scaling AI agents and generative AI applications without copying or moving data.

Snowflake adds observability capabilities via Trail The company also added new observability features in the form of Snowflake Trail, which provides visibility into data quality, pipelines, and applications, enabling developers to monitor, troubleshoot, and optimize their workflows. It is built with OpenTelemetry standards so developers can integrate with popular observability and alert platforms including Datadog, Grafana, Metaplane, PagerDuty, and Slack, among others.

The more attributes you add to your metrics, the more complex and valuable questions you can answer. Every additional attribute provides a new dimension for analysis and troubleshooting. For instance, adding an infrastructure attribute, such as region can help you determine if a performance issue is isolated to a specific geographic area or is widespread. Similarly, adding business context, like a store location attribute for an e-commerce platform, allows you to understand if an issue is specific to a particular set of stores

The main advantage of going the Multi-Cloud way is that organizations can "put their eggs in different baskets" and be more versatile in their approach to how they do things. For example, they can mix it up and opt for a cloud-based Platform-as-a-Service (PaaS) solution when it comes to the database, while going the Software-as-a-Service (SaaS) route for their application endeavors.

A new open-source project, VillageSQL, has been introduced as a tracking fork of MySQL aimed at expanding extensibility and addressing feature gaps increasingly relevant to AI and agent-based workloads. Announced by founder Dominic Preuss, VillageSQL Server for MySQL is positioned as a drop-in replacement that maintains compatibility with upstream MySQL while adding a structured extension framework. The alpha release is now available for experimentation.

Google has overhauled Firestore Enterprise edition's query engine, adding Pipeline operations that let developers chain together multiple query stages for complex aggregations, array operations, and regex matching. The update removes Firestore's longstanding query limitations and makes indexes optional, putting the database on par with other major NoSQL platforms. Pipeline operations work through sequential stages that transform data inside the database.

But it still contains useful things and can be upgraded to from MySQL 8.4 LTS; the MySQL Configurator automatically does the upgrade without user intervention during MSI installations on Windows. The major changes include: A new Vector datatype is supported in CREATE and ALTER statements. JavaScript Stored Programs, which support JavaScript-based stored programs and functions, has come to MySQL Enterprise Edition. JavaScript Stored Programs can call SQL, and SQL can call them.