Streaming Data Infrastructure

Streaming data infrastructure covers the systems that move, store, and process information continuously instead of in periodic batches. This includes event streaming platforms such as Apache Kafka and Apache Pulsar, stream processing engines, and the storage and query layers where that data lands: data warehouses, lakehouses, operational databases, and a growing class of vector stores built for AI workloads.

Why it matters now

The boundaries between these categories are shifting. AI applications need fresh, searchable context, which has pushed vector search from specialized products into general-purpose databases and cloud object storage. Established relational systems, especially Postgres, are taking on workloads that once required dedicated tools. At the same time, the largest data platform vendors are competing to become the single place where streaming, analytics, transactional data, and machine learning meet. Choices made today about formats, engines, and vendors will shape costs and flexibility for years.

What you'll find here

This hub tracks developments across the modern data stack, including:

• Streaming platforms and protocols. Major releases, new storage architectures, and community events shaping event-driven systems.
• Lakehouse and warehouse competition. Strategy, product moves, and market positioning among leading data platform companies.
• Vector search and AI retrieval. Dedicated vector databases, managed cloud services, and vector features arriving in mainstream databases.
• Operational databases. Postgres, Oracle, and other engines, including updates that change how administrators and engineers work.
• Industry adoption. How sectors like finance put real-time data and lakehouse architectures into production.

Whether you're evaluating a platform, planning a migration, or following the business of data, this page collects the coverage that explains where the infrastructure is heading.