Download JAR files tagged by postgres with all dependencies

Posmulten library is an open-source project for the generation of SQL DDL statements that make it easy for implementation of Shared Schema Multi-tenancy strategy via the Row Security Policies in the Postgres database.

To run all integration tests, you need to set up all the databases: 1) Derby: $ export DERBY_HOME=/path/to/db-derby-10.14.2.0-bin $ cd $DERBY_HOME $ mkdir databases; cd databases $ java -jar $DERBY_HOME/lib/derbyrun.jar server start 2) MS SQL Server (for official and jtds drivers): $ podman run -itd --name pax.jdbc.sqlserver -e 'ACCEPT_EULA=Y' -e 'SA_PASSWORD=PaxJdbc!(*67' -e 'MSSQL_PID=Express' \ -p 1433:1433 -d mcr.microsoft.com/mssql/server:2022-latest 3) MariaDB: $ podman run --name pax.jdbc.mariadb -p 3306:3306 -e MYSQL_ROOT_PASSWORD=paxjdbc -d mariadb 4) MySQL: $ podman run --name pax.jdbc.mysql -p 3307:3306 -e MYSQL_ROOT_PASSWORD=paxjdbc -d mysql 5) PostgreSQL: $ podman run --name pax.jdbc.postgresql -e POSTGRES_USER=paxjdbc -e POSTGRES_PASSWORD=paxjdbc -p 5432:5432 -d postgres:latest 6) Oracle (a bit unofficial): $ podman run -itd --name pax.jdbc.oracle -p 1521:1521 --privileged=true oracleinanutshell/oracle-xe-11g

Oozielogger is a application to continuously replicate oozie events into mysql/postgres databes. this includes any of coordinator start, stop, failure, SLA etc.. workflow start, stop, failure, SLA evts this basically consume Oozie's ActiveMQ queue and post back to database so that, end user reporting can happen.

Parent project for the backend database drivers. Drivers are kept independent of the database implementation as some databases may share the same drivers (for instance, many Postgres-derived databases use the same JDBC drivers, as they implement the same wire protocol). However, database-dependent configuration is possible from the database backend module.

Capture PostgreSQL database changes using Debezium integration. Note that this plugin is intended for advanced use cases and requires a proper Debezium setup as explained e.g. in [the following blueprint](https://kestra.io/blueprints/194-use-debezium-to-trigger-a-flow-whenever-new-entries-hit-a-postgres-database-then-send-notification-to-slack-and-process-data-in-python). For simple use cases, consider using the [JDBC-based PostgreSQL plugin](https://kestra.io/plugins/plugin-jdbc-postgres) instead.

Bundles required by the Apache Marmotta Kiwi Repository. This list includes DB drivers for H2 and Postgres and caching dependencies for Guava and Hazelcast. Dependencies for other supported options (MySQL, Infinispan, EH Cache) are commented. Users that want to use such options will need to create their own version of this bundle list or to provide the commented bundles by some other means.

InteGraal has been designed in a modular way, in order to facilitate software reuse and extension. It should make it easy to test new scenarios and techniques, in particular by combining algorithms. The main features of Graal are currently the following: (1) internal storage to store data by using a SQL or RDF representation (Postgres, MySQL, HSQL, SQLite, Remote SPARQL endpoints, Local in-memory triplestores) as well as a native in-memory representation (2) data-integration capabilities for exploiting federated heterogeneous data-sources through mappings able to target systems such as SQL, RDF, and black-box (e.g. Web-APIs) (3) algorithms for query-answering over heterogeneous and federated data based on query rewriting and/or forward chaining (or chase)

InteGraal has been designed in a modular way, in order to facilitate software reuse and extension. It should make it easy to test new scenarios and techniques, in particular by combining algorithms. The main features of Graal are currently the following: (1) internal storage to store data by using a SQL or RDF representation (Postgres, MySQL, HSQL, SQLite, Remote SPARQL endpoints, Local in-memory triplestores) as well as a native in-memory representation (2) data-integration capabilities for exploiting federated heterogeneous data-sources through mappings able to target systems such as SQL, RDF, and black-box (e.g. Web-APIs) (3) algorithms for query-answering over heterogeneous and federated data based on query rewriting and/or forward chaining (or chase)