Engineering insights, product updates, and tutorials from the StreamHouse team.
Every INSERT, UPDATE, and DELETE in your database can be a stream event. Here's how to build Change Data Capture pipelines with StreamHouse for real-time data synchronization, search indexing, and cache invalidation.
Schemas change. Producers and consumers deploy at different times. Here's how StreamHouse's Schema Registry handles Avro, Protobuf, and JSON Schema evolution with automatic compatibility checking.
Elasticsearch is expensive. Kafka + Fluentd is complex. Here's how StreamHouse replaces your entire log pipeline with a single system — ingest, store, query, and alert on logs with SQL.
StreamHouse embeds Apache DataFusion for SQL stream processing. Here's how we turned a batch query engine into a streaming powerhouse — with tumbling windows, stream joins, and continuous queries.
At high throughput, S3 will throttle you. Here's how StreamHouse uses token buckets, circuit breakers, and adaptive backoff to handle 10,000+ S3 operations per second without dropping events.
A deep dive into StreamHouse's binary segment format — how records become blocks, blocks become segments, and segments land in S3 with LZ4 compression and CRC32 integrity checks.
How does StreamHouse organize billions of events into ordered, replayable streams? A deep dive into topics, partition assignment, offset tracking, and consumer groups.
What happens when an agent crashes mid-write? A deep dive into StreamHouse's WAL, CRC32 checksums, sync policies, and the recovery process that ensures your events survive any failure.
StreamHouse agents hold no persistent state — no disks, no replication, no rebalancing. Here's how lease coordination, failure detection, and S3 make this possible.
Traditional message brokers weren't designed for the cloud. Here's why we rethought streaming from first principles with object storage at the core.
A practical guide to migrating your existing Kafka workloads to StreamHouse with zero downtime and minimal code changes.
Learn how to build an end-to-end real-time analytics pipeline using StreamHouse SQL processing, from ingestion to dashboard.
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