Tap into the power of Rivery
Low-impact database migration
Work with the freshest data. Use CDC for real-time data synchronization and managed schema drift to identify and handle new changes in the database.
Fine-tuned database migration
Replicate data with total control. Use custom query, raw data selection and calculated columns to fine-tune whatever you need.
Robust & secure database migration
Move data wherever you need. Use custom file zones or our network with highly secure connections to your source database.
Secure database migration for any type of environment
Whether your database is on-prem or on the cloud, Rivery automates the database data migration process so you can efficiently and securely move data to the cloud.
On-premise
Migrate any on-premise relational database, including Oracle, MySQL, Microsoft SQL Server, PostgreSQL, and MongoDB, to a cloud data warehouse without inefficiencies and busywork.
Cloud
Perform cloud-to-cloud database replication, such as Redshift to Snowflake, and continuously update data changes in real-time.
Migrate to your favorite lake and warehouse
Pick your database extraction method
Arm yourself with database migration best practices
FAQ
Database data migration is the process of moving data from the source database to the target database. With Rivery you can migrate high-volumes of data with ultra low latency using Change data capture or SQL.
Change data capture tracks changes in a source dataset and automatically transfers those changes to a target dataset. Essentially, CDC eradicates the siloization of data. Changes are synced instantly or near-instantly. In practice, CDC is often used to replicate data between databases in real-time.
Standard SQL is one of the most common programming languages. You can use to it query and choose the data that matters to you. You can set it to pull data based on certain events and intervals like the last time a column was updated, or even specific rows. Learn more here
SQL-based extraction and CDC (Change Data Capture) are the main methods for operational database data extraction. Deciding between the two depends on factors like data freshness, volume, complexity, and specific use cases. Sometimes, using a combination of both can be the most effective approach. Rivery lets you mix and match between the two. Learn more here