Data ingestion
for any use case

Extract and load data from any source with ease, flexibility and speed. Zero maintenance and infrastructure management.

Ingest any data from any source, on your terms

Extract & load in minutes

Instantly connect to key data sources with 200+ managed integrations

Build advanced data pipelines in a few clicks

Connect all your data

Never leave data behind! Connect to any source with self-serve REST API

Cut overhead and data silo costs by working from one place

Manage with ease

Cut ELT maintenance with managed integrations

Gain full visibility into your ingestion processes and costs

Automate data pipelines in minutes with Rivery’s data ingestion

Best-in-class data replication

  • Replicate data with total ease from any relational or NoSQL database with CDC (change data capture)
  • Custom SQL query (batch) replication for databases where CDC isn’t an option
  • Replicate volumes of data fast with baked in incremental loads, auto mapping, and schema drift handling

Granular control over the data ingestion

  • Move data through Rivery or your own file zone/lake
  • Select predefined structures ready for analysis or the raw data
  • Control field level data and calculated columns for any scenario

No more siloed replication

  • Orchestrate and monitor end-to-end ELT workflows vs. just data ingestion
  • Configure your own custom connections without external solutions
  • Trigger a data refresh in Tableau/Power BI with reverse ETL for faster insights


Configure your own custom connection

Antoine Lefebvre,

Data Engineering Product Owner, at BlaBlaCar.

“Migrating Facebook ads took us 1 week to implement. Before that, we had to update the APIs every 3 months and our data analysts would be blocked for days. For one use case with bus GPS data we used 2 different APIs – and in half a day our pipelines were up and running.

How it works


Select your source from 200+ connectors or configure your own.


Select the target warehouse or lake to ingest the data into.


Auto map fields, modify the target schema and set the load mode as needed.

See it in action – Source to Target pipeline setup


What is data ingestion?

Data ingestion (also known as cloud data replication or extract and load), is the process of extracting data from a data source (i.e. database, SaaS application, files, etc.) and loading it into a target data lake or data warehouse. The ingestion process can be executed in batch, real-time or stream processing.

What is the difference between data ingestion and ETL?

Data Ingestion is just the Extract and Load part of the ETL (Extract, Transform and Load) process. Once data is ingested into your target lake or warehouse, you need to transform it to match the desired business logic and serve it to the consumption layer or directly to users. Note ETL can be done as ETL or ELT, learn more here.

Can Rivery help migrate my database to the cloud?

Yes, Rivery supports both CDC and batch replication as well as auto-migration capabilities to automatically perform a full migration of all the database table data in a few clicks. Learn more about database data migration best practices here.

How do I control the schedule of the data replication?

Rivery offers multiple ways to automate the data replication process. This includes the ability to schedule a source to target data pipeline using a selection from a user interface or by providing your own cron expression. The pipeline can also be scheduled as part of a complete workflow with its own dependencies. Finally, the pipeline can be triggered via an API as well as part of an external process.

What is a managed API integration?

A managed API integration (or automated data integration) is a pre-built integration to a certain data source (i.e. a SaaS application), that is designed to allow for a no-code automated data ingestion from that source via the source API. For this managed source integration, Rivery is in charge of updating the integration every time the source API changes so your data engineers don’t have to monitor and maintain that integration.

What are some common examples of data ingestion?

Data ingestion is the starting point for any data integration process. Common examples are:

  • Ingesting data from an operational database such as SQL Server or PostgreSQL into a cloud data storage or data warehouse
  • Loading files from a cloud storage such as AWS S3 or GCP Cloud Storage to a cloud data warehouse
  • Extracting and loading data from SaaS applications or any REST API such as Salesforce, Google Ads, Facebook Ads, Shopify and TikTok. into a cloud data warehouse such as Snowflake, BigQuery or Databricks

Replicate data from any source to any lake or warehouse

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