Wirekite

Use case

Move data. Don't just copy it.

Real data integration handles heterogeneous sources, schema differences, and downstream fan-out. Wirekite does all three — across every major OLTP source, every major target, and every native queue.

Integration is harder than transport.

Moving bytes from A to B is the easy part. Real integration — getting unified, accurate data across a stack with multiple OLTP sources and multiple downstream consumers — is mostly schema translation, type fidelity, and ordering. Most pipelines patch these with bespoke scripts. Most of those scripts are wrong on an edge case nobody knows about until production catches it.

Wirekite is built around the assumption that the source and target speak different dialects. Type mapping is per-dialect. Schema differences are handled in the loader. Commit order is preserved across the pipeline. Validate confirms it row by row after the move.

Three things Wirekite does that scripts don't.

Schema mapping

Per-dialect, automatic.

Oracle NUMBER(38,0) lands as Snowflake NUMBER(38,0). MySQL JSON lands as Google BigQuery JSON. Timestamp precision, time zones, NULL semantics, and string encodings are handled per source × target pair — not approximated.

Heterogeneous sources

Multiple sources into one target.

Run a Wirekite job that pulls from Postgres and MySQL and Oracle in parallel and lands them in one Google BigQuery dataset or one Google Spanner instance. Each source uses its native CDC; the target gets a unified, ordered feed.

Downstream fan-out

Database and queue, same pipeline.

The same change feed that lands in a warehouse can land on Kafka, Redpanda, or Google Pub/Sub — in commit order, exactly once, native binary format. No second pipeline to maintain.

One pipeline. Everything connected.

Every major OLTP database on the left. Every major analytical target on the right. Three native queues in between.

Sources

  • Oracle
  • PostgreSQL
  • MySQL
  • SQL Server
  • MariaDB
  • SingleStore, YugabyteDB, TigerData
  • Google AlloyDB, Google Spanner (CDC)

Targets

  • Snowflake
  • Google BigQuery
  • Firebolt
  • Databricks
  • MongoDB
  • Google Spanner, Google AlloyDB
  • All sources, in reverse

Queue targets

  • Apache Kafka
  • Redpanda
  • Google Pub/Sub

Commit order. Exactly once. Native binary format. No schema registry required.

See the full matrix →

Integrate the whole stack.

Book a 30-minute demo. We'll wire up your sources and targets live, including the queue if you want one.

Book a Demo Read about queue targets →