Database Lab tutorial for any PostgreSQL database.The documentation is significantly extended: 3 tutorials, 26 user guides, 6 references, and counting:.Basic data transformation and masking supported: specify any custom script that will be applied each time a new snapshot is prepared (option preprocessingScript in both logicalSnapshot and physicalSnapshot jobs, see the Configuration reference).For continuously updated physically initialized data directory (which effectively makes your DLE a specialized replica), snapshot management is fully automated: snapshots are created and destroyed based on the schedule defined in the configuration file (see the reference Job physicalSnapshot).Any managed cloud PostgreSQL offering is now supported, with additional features for Amazon RDS (see DLE tutorial for Amazon RDS and the guide Data source: AWS RDS).Both physical (pg_basebackup, WAL-G, more) and logical methods (dump/restore, Amazon RDS, Heroku Postgres, more) are supported (see the guide Database Lab Engine data sources).Automated data retrieval: specify the source and the method of initializing the data directory and how it is to be updated.Instead of using custom scripts for initial and continuous data retrieval, it is now possible to configure everything in a declarative manner to get the data and be up and running. Version 2.0 speeds up and empowers the initialization of DLE itself. It was already possible to provision full-sized multi-terabyte database clones in just a few seconds and use them for a broad spectrum of tasks such as database schema changes verification, SQL query analysis, or general application testing. The previous versions of the Database Lab introduced the core technology: thin clone provisioning, based on either ZFS (default) or LVM. As a result, building dev&test environments for projects with many databases (such as those that adopted microservice architecture) becomes much easier. In DLE 2.0, all these tasks can be flexibly configured in a single configuration file. This release continues our strategy to automate all routine tasks such as initialization of the PostgreSQL data directory, data transformation, and snapshot management. This can become a game-changer in your development and testing workflow, improving time-to-market, and reducing costs of your non-production infrastructure. ![]() All these copies are independently modifiable and created/destroyed in just a few seconds. Using Database Lab API or CLI (and if you are using Database Lab SaaS, GUI), on a single machine with, say, a 1 TiB disk, you can easily create and destroy dozens of database copies of size 1 TiB each. The Postgres.ai team is proud to announce version 2.0 of Database Lab Engine (DLE) for PostgreSQL, a modern database tool for building powerful development and testing environments based on thin cloning. ![]() ![]() Database Lab Engine 2.0 for PostgreSQL released
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