Delta Lake Table tools
======================

Creating Delta Lake Tables
--------------------------

Using the ``deltalake`` Python library
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

You can use the `deltalake <https://pypi.org/project/deltalake/>`_  Python library to create Delta Tables and
write to the bucket.

Using the ``lakehouse-loader`` utility
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

You can also use the `lakehouse-loader <https://github.com/splitgraph/lakehouse-loader>`_  utility that EDB created for this
task to export data from an arbitrary Postgres instance to Lakehouse
tables in a storage bucket.

For example, with the lakehouse-loader utility:

.. code:: bash

   export PGPASSWORD="..."
   export AWS_ACCESS_KEY_ID="..."
   export AWS_SECRET_ACCESS_KEY="..."

   #  export other AWS envvars

   ./lakehouse-loader postgres-to-delta postgres://test-user@localhost:5432/test-db -q "SELECT * FROM some_table" s3://my-bucket/my_schema/my_table

This code exports the data from the ``some_table`` table in the
``test-db`` database to a Delta Table in the ``my_schema/my_table`` path
in the ``my-bucket`` bucket.

You can then query this table in the Lakehouse node by creating an
external table that references the Delta Table in the
``my_schema/my_table`` path. See `Querying Delta Lake Tables <https://enterprisedb.com/docs/edb-postgres-ai/hybrid-manager/analytics/learn/how-to/query-delta-lake-tables>`_  for details on how to do
this.