How to reflect data in trino catalog table using parquet file generated from databricks

To reflect data in a Trino catalog table using a Parquet file stored in an **Azure Blob Storage** container (generated from Databricks), follow these steps:

1. **Generate Parquet File in Databricks**:
   – In Databricks, write your data to a Parquet file stored in an Azure Blob Storage container. Use the `abfss` protocol for Azure Data Lake Storage Gen2 or `wasbs` for Blob Storage. Example:
     “`python
     df = spark.sql(“SELECT * FROM your_table”)
     df.write.parquet(“abfss://<container>@<storage-account>.dfs.core.windows.net/<path>/output/parquet”, mode=”overwrite”)
     “`
   – Ensure the Databricks cluster has access to the Azure storage account (e.g., via SAS token, service principal, or managed identity).

2. **Configure Trino Catalog for Azure**:
   – Set up a Trino catalog to connect to Azure Blob Storage or Data Lake Storage Gen2 using the Hive connector. Create a `catalog.properties` file (e.g., `hive.properties`) in the Trino configuration directory:
     “`properties
     connector.name=hive
     hive.metastore.uri=thrift://<metastore-host>:9083
     hive.azure.abfs-storage-account=<storage-account-name>
     hive.azure.abfs-container=<container-name>
     hive.azure.abfs-access-key=<access-key>
     # Or use SAS token or OAuth credentials
     # hive.azure.abfs-sas-token=<sas-token>
     # hive.azure.abfs-oauth-client-id=<client-id>
     # hive.azure.abfs-oauth-secret=<client-secret>
     # hive.azure.abfs-oauth-endpoint=<oauth-endpoint>
     “`
   – If using Azure Data Lake Storage Gen2, ensure the `abfss` protocol is supported in your Trino version (Trino 360+ recommended). For older versions, you may need to use `wasbs` with Blob Storage.

3. **Register the Parquet File as a Table in Trino**:
   – Create an external table in Trino pointing to the Parquet file location in the Azure container:
     “`sql
     CREATE TABLE hive.default.my_table (
         column1 datatype1,
         column2 datatype2,
         …
     )
     WITH (
         format = ‘PARQUET’,
         external_location = ‘abfss://<container>@<storage-account>.dfs.core.windows.net/<path>/output/parquet’
     );
     “`
   – Replace `column1`, `column2`, etc., with the column names and data types from the Parquet file schema. Ensure the `external_location` matches the exact path in the Azure container.

4. **Infer Schema (Optional)**:
   – If you’re unsure of the Parquet file’s schema, inspect it in Databricks:
     “`python
     parquet_file = spark.read.parquet(“abfss://<container>@<storage-account>.dfs.core.windows.net/<path>/output/parquet”)
     parquet_file.printSchema()
     “`
   – Alternatively, use a tool like `parquet-tools`:
     “`bash
     parquet-tools schema abfss://<container>@<storage-account>.dfs.core.windows.net/<path>/output/parquet
     “`

5. **Query the Table in Trino**:
   – Query the table in Trino to verify the data:
     “`sql
     SELECT * FROM hive.default.my_table;
     “`

6. **Handle Updates**:
   – If the Parquet file is updated in Databricks, Trino will automatically reflect the new data since it’s an external table, provided the schema remains compatible.
   – If the schema changes, drop and recreate the Trino table or handle schema evolution (Parquet supports adding columns, but ensure Trino’s table definition aligns).

### Additional Considerations:
– **Partitioning**: If the Parquet file is partitioned (e.g., by date or another column), include partitioning details in the Trino table definition:
  “`sql
  WITH (
      format = ‘PARQUET’,
      external_location = ‘abfss://<container>@<storage-account>.dfs.core.windows.net/<path>/output/parquet’,
      partitioned_by = ARRAY[‘partition_column’]
  )
  “`
– **Authentication**: Prefer using Azure Managed Identity or OAuth for secure access instead of access keys or SAS tokens. Configure Trino with the appropriate credentials:
  “`properties
  hive.azure.abfs-oauth-client-id=<client-id>
  hive.azure.abfs-oauth-secret=<client-secret>
  hive.azure.abfs-oauth-endpoint=https://login.microsoftonline.com/<tenant-id>/oauth2/token
  “`
– **Permissions**: Ensure the Trino service has read access to the Azure container (e.g., via RBAC roles or storage account permissions).
– **Performance**: Optimize Parquet files in Databricks using `OPTIMIZE` or `ZORDER` for better query performance in Trino.
– **Hive Metastore**: If you don’t have a Hive metastore, consider using Azure’s Glue Data Catalog (if on AWS) or a file-based metastore, though Hive metastore is most common.

### Troubleshooting:
– Verify the `abfss` path is correct and accessible from Trino.
– Check for schema mismatches between the Parquet file and Trino table definition.
– Ensure Trino’s Hive connector is configured correctly for Azure (check logs for errors).
– Test connectivity to Azure Blob Storage from Trino using tools like `az storage blob list`.

If you need specific configurations, troubleshooting steps, or examples for your setup, let me know!