Load data into Amazon S3 in minutes

Renta ELT — managed data loading into Amazon S3. No data engineer, no custom scripts, no missed rows.

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Amazon S3 is the foundation of modern data lakes — infinitely scalable object storage with pennies-per-GB pricing and tight integration into the AWS analytics stack.

Renta turns S3 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 Amazon Athena, Redshift Spectrum, EMR, Snowflake external tables, BigQuery external tables, Databricks or Trino — no duplication, no custom loaders, no missed rows.

Start loading data into Amazon S3 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 Amazon S3.

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 Amazon S3

3Configure and sync

Amazon S3 loading capabilities

Renta lands data in S3 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 S3. 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 (Snappy or ZSTD) partitioned Hive-style by event date, for example s3://your-bucket/renta/source=google_ads/date=2026-04-24/. Athena, Redshift Spectrum, Snowflake, BigQuery and EMR prune partitions aggressively out of the box.

Automatic schema and Glue Catalog

Renta tracks schema changes on the source side and embeds typed schemas in every Parquet file. Optional integration with AWS Glue Data Catalog registers the tables and schema evolution so Athena / Redshift Spectrum see new columns without manual DDL.

Deduplication

S3 is immutable, so Renta writes versioned Parquet parts and uses natural keys from the source. Downstream tables (Athena views, Iceberg, Snowflake external) see the latest row per primary key without duplicates.

Use cases

What teams build on top of Amazon S3 data

Build a managed data lake on S3 — raw and clean Parquet layers ready for Athena, Redshift Spectrum, Snowflake and BigQuery external tables
Run federated SQL with Amazon Athena or Trino directly on Renta-written Parquet — no warehouse load, no duplicate storage
Feed ML training and feature stores in SageMaker, Databricks and Spark with typed, partitioned datasets
Archive granular marketing, CRM and billing history at S3 Glacier pricing while keeping it query-ready
Hydrate a lakehouse on Iceberg or Delta Lake — Renta delivers append-only sources, transformation layers sit on top
Share data across accounts and vendors via S3 bucket policies — no exports, no duplicated storage