Google Cloud Storage as a Destination
Replicate database changes directly into a Google Cloud Storage bucket as partitioned Parquet files that downstream systems can consume.
Teams building on Google Cloud often want real-time data landing directly in GCS - not after a batch job, custom connector, or extra hop through another system. Until now, that meant stitching together ingestion logic and managing yet another moving part in the pipeline. This worked, but it added operational overhead and made real-time lakehouse workflows harder to maintain at scale.
Artie can now replicate database changes directly into Google Cloud Storage, writing data in delta Parquet format.
You can stream updates from sources like SQL Server or MySQL straight into a GCS bucket, where Artie continuously writes partitioned Parquet files. These files are immediately consumable by downstream systems like BigQuery, Spark, or Databricks - without batch jobs or custom ingestion code.
Why this matters:
- Build real-time data lakes directly on Google Cloud
- Eliminate custom connectors and batch ingestion jobs
- Improve compatibility with modern GCP analytics tools
- Reduce operational overhead while scaling reliably