Load data into Google Cloud Storage in minutes

Renta ELT — managed data loading into Google Cloud Storage. No data engineer, no custom scripts, no missed rows.

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Google Cloud Storage is the foundation of data lakes on Google Cloud — infinitely scalable object storage with competitive pricing and tight integration with BigQuery and Vertex AI.

Renta turns GCS into your single source of truth by automatically pulling data from 100+ sources (ad platforms, CRMs, databases, SaaS tools), normalizing the schema and writing partitioned Parquet to your bucket.

Query the same files from BigQuery via external tables, or use them in Spark, Dataproc, or Vertex AI — no duplication, no custom loaders, no missed rows.

Start loading data into GCS in 3 steps

Pick any connector — ad platforms, CRMs, databases, SaaS — and authorize the account in a couple of clicks. No code, no servers to maintain.

Connecting a data source in Renta for loading into Google Cloud Storage.

1Connect a source

Pick any connector — ad platforms, CRMs, databases, SaaS — and authorize the account in a couple of clicks. No code, no servers to maintain.

2Choose Google Cloud Storage

3Configure and sync

GCS loading capabilities

Renta lands data in GCS as a production-grade lake layer — typed, partitioned and backed by a 99.9% SLA.

Incremental sync

After the initial historical backfill Renta only writes new and changed records to GCS. This reduces storage cost, source load and refresh latency — from 15-minute windows down to daily.

Parquet and Hive-style partitioning

Renta writes compressed Parquet partitioned by event date, for example gs://your-bucket/renta/source=google_ads/date=2026-04-24/. BigQuery external tables and Spark prune partitions aggressively out of the box.

Automatic schema evolution

Renta tracks schema changes on the source side and embeds typed schemas in every Parquet file. This ensures that downstream tools can adapt to new columns without manual intervention.

Deduplication on immutable storage

GCS is immutable, so Renta writes versioned Parquet parts using natural keys from the source. Downstream BigQuery views or external tables see the latest row per primary key.

Use cases

What teams build on top of GCS data

Build a managed data lake on GCS — raw and clean Parquet layers ready for BigQuery external tables
Feed ML training in Vertex AI and feature stores with typed, partitioned datasets replicated by Renta
Run Spark and Dataproc jobs directly on Renta-written Parquet for complex data processing
Archive granular marketing and CRM history at GCS Archive pricing while keeping it query-ready
Hydrate a lakehouse on BigLake — Renta delivers the storage layer, GCP handles the management
Share data across projects via GCS bucket permissions — no exports, no duplicated storage