Post Upgrade Tasks

Starting with 5.7.0, the Transformer-provided libraries for Delta Lake stages have been upgraded from Delta Lake version 0.7.0 to version 2.4.0, which supports Spark 3.4.x.

After upgrading from an earlier Transformer version, review pipelines that include Delta Lake stages that use Transformer-provided libraries to ensure that the Spark upgrade does not adversely affect data processing.

For information about Delta Lake versions and Spark compatibility, see the Databricks documentation.

Starting with 5.6.0, the Azure SQL destination requires a Microsoft JDBC driver for SQL Server version 8 or later. In addition, Transformer also includes a Microsoft JDBC driver for SQL Server with the destination.

After upgrading from an earlier Transformer version, if an existing Azure SQL pipeline uses an earlier driver version and is configured to perform a bulk copy, the pipeline may fail with a java.lang.NoSuchMethodError error.

Starting with 5.5.0, lookup processors use an internal row_number column to perform lookups and repartition data. This change applies to all lookup processors: Delta Lake Lookup, JDBC Lookup, and Snowflake Lookup.

Due to the new internal column, with this release, a lookup processor configured to sort columns and to return the first matching row can no longer include a row_number column in stage properties.

The same lookup processor also now creates partitions based on the specified lookup fields.

In previous releases, the Transformer Docker images used Alpine Linux as a parent image.

Starting with version 5.4.0, the Transformer Docker images use Ubuntu 22.04 LTS (Jammy Jellyfish) as the parent image.

If you build custom Transformer images using a Docker image for Transformer 5.3.x or earlier as the parent image, review your Dockerfiles. Make all required updates so they are compatible with Ubuntu Jammy Jellyfish before you build a custom image based on Docker images for Transformer 5.4.0, streamsets/transformer:scala-2.11_5.4.0 or streamsets/transformer:scala-2.12_5.4.0, or later versions.

Starting with the Snowflake JDBC driver 3.13.25 release in November 2022, the Snowflake JDBC driver converts underscores to hyphens, by default. This can adversely affect communicating with Snowflake when Snowflake connection information specified in a Snowflake stage or connection, such as a URL, includes underscores.

After you upgrade to Snowflake JDBC driver 3.13.25 or later, review your Snowflake connection information for underscores.

When needed, you can bypass the default driver behavior by setting the allowUnderscoresInHost driver property to true. For more information and alternate solutions, see this Snowflake community article.

In version 5.2.0, four origins - My SQL JDBC Table, Oracle JDBC Table, PostgreSQL JDBC Table, and SQL Server JDBC Table - replace the Table property that accepted a single table with the Tables property that accepts a list of multiple tables.

After upgrading from a version earlier than Transformer 5.2.0, review your SDK for Python code for these origins and replace origin.table with origin.tables.

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>

If using Transformer prebuilt with Scala 2.12, then after upgrading from a version earlier than Transformer 5.2.0, review how pipelines use the generated XML files and make necessary changes to account for the new initial declaration.

Starting with version 4.1.0, when you use Transformer prebuilt with Scala 2.12, you must install Java JDK 11 on the Transformer machine. In previous releases, though required by Transformer prebuilt with Scala 2.12, a Java JDK 11 installation was not enforced.

If you upgrade from Transformer 4.0.0 to Transformer 4.1.0 or later prebuilt with Scala 2.12, you must have Java JDK 11 installed on the Transformer machine for Transformer to start.

Starting with version 4.1.0, Transformer submits jobs to Databricks differently from previous releases. As a result, you have 60 days from a Databricks job run to view job details.

In previous releases, with each pipeline run, Transformer creates a standard Databricks job, but uses it only once. This job counts toward the Databricks jobs limit.

With version 4.1.0 and later, Transformer submits ephemeral jobs to Databricks. An ephemeral job runs only once, and does not count towards the Databricks job limit.

However, the details for the ephemeral jobs are only available for 60 days, and are not available through the Databricks jobs menu. When necessary, access Databricks job details while they are available. Pipeline details remain available through Transformer as before.

For information about accessing job details, see Accessing Databricks Job Details.

Starting with version 4.1.0, to use an ADLS Gen1 or Gen2 stage in a pipeline that runs on an Apache Spark for Azure HDInsight cluster, you must configure the stage to use the ADLS cluster-provided libraries stage library.

Update ADLS stages in existing Azure HDInsight cluster pipelines to use the ADLS cluster-provided libraries stage library.

Starting with version 4.1.0, when you enable HTTPS for Transformer, you can store the keystore and truststore files in the Transformer resources directory, <installation_dir>/externalResources/resources. You can then enter a path relative to that directory when you define the keystore and truststore location in the Transformer configuration properties.

In previous releases, you could store the keystore and truststore files in the Transformer configuration directory, <installation_dir>/etc, and then define the location to the file using a path relative to that directory. You can continue to store the file in the configuration directory, but StreamSets recommends moving it to the resources directory when you upgrade.