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Businesses need to make information in relational databases broadly available for analysis, data science, BI and ML, without impacting the performance of the source database with resource-consuming queries.
The best practice for doing this is change data capture (CDC), which transmits change events based on database log activity. Upsolver creates and maintains a live database replica in your data lake or data warehouse. You can then access live operational data, with minimal impact on the source database. And you can cleanse and transform the data as you ingest it – mask, normalize, join, aggregate and more.
SQLake replicates your MySQL, Postgres or SQL Server database to your data lake and data warehouse. It performs transformations and outputs live tables to Snowflake, Redshift, query engines such as Athena or Redshift Spectrum, or search engines such as Elasticsearch.
Upsolver’s CDC solution for database replication provides simplicity, data freshness, reliability and cost-effectiveness. It is based on the popular Debezium Engine, but unlike the open source solution, doesn’t require Kafka Connect and a Kafka cluster.
Upsolver stores the initial database snapshot plus raw change events in Amazon S3 as an immutable log, which allows for easy time travel and reprocessing of past data.
CDC support will be expanded to other popular databases such as Oracle Database, Microsoft SQL Server, IBM DB2 and MongoDB in the near future.
Unlike most CDC systems, which focus solely on moving data, Upsolver is a scalable, stateful processing engine that allows you to transform the data in-flow. Some examples of its power include:
Unlike previous generations of agent- or appliance-based CDC solutions, Upsolver is a cloud-native service that runs in your AWS VPC. Your data never leaves your control, and installation takes only a few minutes.
Upsolver pricing is simple. Start with a free fully-featured 30-day trial. Then stay on board for as little as $99 / TB ingested.
To build a CDC pipeline, you use the Upsolver IDE or CLI to
1) Connect to your source database,
2) Snapshot your database and then ingest change events,
3) Transform (e.g. cleanse, filter, aggregate, enrich, join) the data, and
4) Output live tables (merging changed rows) to one or more destination system.
See how easy it is to continuously stream change logs for database replication from relational databases.
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