Connectors provide pluggable integration to any external data service so that an admin can easily allow end users to interact with them.
SqlAlchemy is the prefered way if the Hive API is not supported by the database. The core implementation is in sql_alchemy.py and relies on each respective SqlAlchemy dialect.
This asynchronous API based on the Thrift API of Hive is very mature and powers an excellent integration of Apache Hive and Apache Impala.
If the built-in HiveServer2 (Hive, Impala, Spark SQL), SqlAlchemy (MySQL, PostgreSQL, Oracle, Presto…) don’t meet your needs, you can implement your own connector to the notebook app:
List of all connectors
Each connector API subclasses the Base API and must implement the methods defined within. Refer to the JDBC or RdbmsApi for representative examples
The JDBC API relies on a small JDBC proxy running next to the Hue API. By default it won't be built without setting the BUILD_DB_PROXY flag, e.g.:
export BUILD_DB_PROXY=true make install
Note In the long term, SqlAlchemy is prefered as more “Python native”.
It is recommended to develop an SqlAlchemy connector if yours is not already existing.
The backends is pluggable by providing alternative client interfaces:
Various storage systems can be interacted with. The fsmanager.py is the main router to each API.
Note Apache Ozone as well as Ceph can be used via the S3 browser.
With just a few changes in the Python API, the HBase browser could be compatible with Apache Kudu or Google Big Table.
Based on the Livy REST API.
Currently only Apache Oozie is supported for your Datawarehouse, but the API is getting generic with HUE-3797.