Load data into Snowflake in minutes

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

Trusted by teams at

Snowflake is a cloud data platform built for elastic, high-concurrency analytics across any volume of data.

Renta turns Snowflake into your single source of truth by automatically pulling data from 100+ sources — ad platforms, CRMs, databases and SaaS tools — normalizing the schema and writing ready-to-query tables.

With auto-scaling virtual warehouses, Time Travel, zero-copy clones and native Iceberg support on the Snowflake side, and incremental sync + managed schema drift on the Renta side, marketing, product and data teams can focus on dashboards, attribution models and ML forecasts instead of maintaining homegrown ETL scripts.

Start loading data into Snowflake 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 Snowflake.

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 Snowflake

3Configure and sync

Snowflake loading capabilities

Renta replicates data into Snowflake with production-grade reliability — typed, normalized and backed by a 99.9% SLA.

Incremental sync

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

Automatic schema

Renta creates tables, assigns column types and tracks schema changes on the source side. When a new field appears in the API, a column is added to Snowflake without breaking the pipeline.

Typing and normalization

Data types from every source are normalized to a single consistent format. Cross-source JOINs work without manual CAST or JSON parsing.

Upsert and deduplication

Renta uses a MERGE strategy and the source's natural keys to prevent duplicates in Snowflake. Records are updated by primary key, deletes are soft-flagged and retroactive changes are applied correctly.

Use cases

What teams build on top of Snowflake data

Join GA4, ad platform and CRM data in Snowflake to compute end-to-end CAC, LTV and ROMI per channel
Build attribution models (last-click, data-driven, Markov) on raw events and spend replicated by Renta
Train ML models with Snowflake Cortex directly on Renta tables — churn, LTV and conversion propensity forecasts
Visualize data in Snowsight, Tableau and Power BI straight from Snowflake, with no manual exports
Blend financial data from Stripe, Paddle and billing databases with marketing spend for cohort-level P&L
Share governed data with agencies and partners via Snowflake Data Sharing — no exports, no duplicated storage