Google Cloud Storage pipelines,
described in plain English.
Land PostgreSQL, MySQL, and SaaS data in GCS as Parquet, JSON, or CSV — CDC streaming or scheduled snapshots — and move raw files byte-for-byte between GCS, S3, and Azure. Live on rsync.ai Cloud, no per-row fees.
rsync.ai writes to Google Cloud Storage two ways: structured output (Parquet, JSON, or CSV with a schema manifest, from Postgres/MySQL CDC or snapshots, partitioned for BigQuery external tables) and byte-identical blob passthrough (copy any object between GCS, S3, and Azure). Authenticate with a service account or workload identity; PII columns are masked before the write.
- Parquet, JSON, or CSV — Hive partitioning for BigQuery
- CDC streaming or scheduled snapshots — or both
- Service-account or workload-identity auth
- Blob passthrough between GCS, S3, and Azure Blob
Move data into GCS
Start from a database, or describe any other source in plain English.
What the GCS connector does
Structured exports for analytics, and raw blob passthrough for everything else.
Structured exports
Postgres & MySQL tables to Parquet, JSON, or CSV with a schema manifest.
Blob passthrough
Copy any object byte-for-byte between GCS, S3, and Azure — SHA-256 verified.
PII-safe
Mask or hash sensitive columns before a single byte lands in your bucket.
rsync.ai vs. Fivetran, Airbyte, custom scripts for GCS
What you give up — and gain — choosing rsync.ai for pipelines into Google Cloud Storage.