Airbyte announced the general availability of the company's Postgres source connector. The connector is available for production use and fully supported by Airbyte, making it possible to easily move data stored in Postgres to other destinations, especially for analysis. 

One of the most popular databases in use worldwide, Postgres is used extensively by companies of all types.

"There are vast amounts of data that are now able to be shared and easily moved with confidence. This has been among our top priorities to deliver a production-ready connector to Postgres users," said Michel Tricot, co-founder and CEO, Airbyte.

Now, Airbyte users can be assured that large volumes of data can be moved reliably. The Postgres source connector supports the following important features to maintain data integrity.

• Full syncs: The ability to copy an entire database from source to destination at once.
• Incremental syncs: The ability to send all data since the last sync. Incremental syncs look at changes that happen at the source. 
• Change data capture (CDC): CDC is an industry best practice for making an incremental sync.
  

There are nearly 200 data connectors now available in the Airbyte Connector Catalog and the company is on track to reach 500 by the end of the year.

Go here to access the Postgres connector for Airbyte and view a quick screen recording of how it works here.

With its growing community of 7,000 data practitioners and 300 contributors, Airbyte is redefining the standard of moving and consolidating data from different sources to data warehouses, data lakes, or databases in a process referred to as extract, load, and, when desired, transform (ELT). Over the past year and a half, more than 20,000 companies have used Airbyte to sync data from sources such as PostgreSQL, MySQL, Facebook Ads, Salesforce, Stripe, and connect to destinations that include Redshift, Snowflake, Databricks, and BigQuery.

Airbyte's open-source data integration solves two problems. First, companies always have to build and maintain data connectors on their own because most less popular "long tail'' data connectors are not supported by closed-source ELT technologies. Second, data teams often have to do custom work around pre-built connectors to make them work within their unique data infrastructure.