materializeaz

Materializeaz is a software platform for declarative data materialization. It enables users to define materialized views over streaming and batch sources so queries can run against precomputed results. The aim is to reduce analytics latency by updating views as new data arrives rather than recomputing from scratch.

Views are defined with a SQL-like syntax supporting select, joins, and aggregates. Materialized views are updated incrementally, applying only the changes caused by new data. The system supports time travel by preserving historical view states for configurable windows.

Materializeaz comprises a distributed dataflow engine, a catalog of sources and views, connectors to data systems, and a query planner. It builds a graph of operators from user definitions and executes it across multiple workers, with fault tolerance and scalable parallelism.

Connectors cover streaming sources such as Kafka and cloud storage, plus sinks like databases and dashboards. Client libraries in multiple languages and a REST API facilitate integration with applications and BI tools. Deployments range from single-node development to production clusters in cloud or on-premises environments.

The project is community-driven and open source, with ongoing development and documentation. Common use cases include real-time dashboards, event analytics, and operational reporting. Users should be mindful of resource overhead and complexity in schema evolution.