12.07.2022 Release Notes

These are the release notes for Dec 07, 2022.

FEATURES

User-defined cluster pool configuration (Data Plane only)
- A Spark cluster pool is an elastic pool of Spark clusters. Each pool dynamically scales executors up and down depending on load. Users control the scaling parameters of a single cluster and the cluster pool.
- All Spark-related functionality for Data Plane environments now runs on consolidated Spark clusters.
- Allows users to configure one or more Spark cluster pools on the site admin page.
- Users can select the default site-level Spark cluster pool or a user-configured cluster pool to execute Spark tasks in a Data Service or Dataflow.

ENHANCEMENTS

Add incremental read support for the following Read Connectors for all Data Planes:
- Marketo
- Shopify
- Google Analytics 4
Add incremental and snapshot replication strategies for Snowflake Read Connector
Salesforce Read Connector supports date type
Support direct copy for Databricks Data Plane
Avoid OOM error for Databricks Read Connector by streaming data
Improve performance by tuning page and partition size for Databricks Read Connector
Update Databricks connector version
Multi-table Postgres Read Connector performance improvements
Increase reliability of spark logs download (now a tar instead of zip archive)
Update Spark 3.2.0 build to 3.2.2
Support new api token for Hubspot Read Connector

BUGFIXES
All Data Planes

Fix MS SQL incremental read for datetime column
Fix a bug where the Snowflake Connection would cancel a query job after 5 minutes without a connection.
Fix credentials for PySpark Transforms in Data Plane environments

Spark Data Plane:

BigQuery Data Plane

Fix BigQuery migration for Data Service/Dataflow name change;
Drop Dataset name restrictions
Fix issue causing incremental read connectors to miss partitions in BigQuery Data Plane

Databricks Data Plane

Snowflake Data Plane

Fix failures with Snowflake Data Plane using reserved words as column names by always uppercase and quoting references