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The SnapLogic platform goes live on Cloudflare on 11 Aug . When launched, the SnapLogic UI will automatically redirect to the specific Cloudflare CDN addresses. This change enhances the SnapLogic platform’s response time, security, and performance. To ensure continued access to the SnapLogic UI, only customers who restrict outbound IP addresses to a predefined list of IP addresses must extend their allowlist to add all the specific IP addresses. Learn more to understand how this impacts your organization. |
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Snap Pack | Date of Update | Snap Pack Version | Updates |
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| 42920rc17045 | A new Snap Pack for Databricks Lakehouse Platform (Databricks or DLP) introduces the following Snaps:
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Known Issues
When you add an input view to the Databricks - Delete Snap, ensure that you configure the Batch size as 1 in the Snap’s account configuration. For any other batch size, the Snap fails with the exception:
Multi-batch parameter values are not supported for this query type.[This issue is fixed in the September 2022 Release] The Databricks - Unload Snap fails with the error:
External source/target type is invalidwhen you attempt unloading data from your DLP instance to a DBFS location.
Data Automation
ELT Snap Pack
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The ELT Load Snap can infer the schema from the source files located in S3 buckets and create, overwrite, and append the target table in your Redshift instance with the source data. It can infer the table schema from the data available in AVRO, CSV, JSON, ORC, and PARQUET files. Learn more at Automatic Schema Inference with ELT Load Snap.
Known Issues
In the case of Azure Synapse and Redshift, if the source value is NULL the ELT Pivot Snap fails to return valid results either to the target table or the downstream Snaps.
When loading data from a CSV file to a target DLP table, the header names in the file must exactly match the column names in the target table. Otherwise, the ELT Load Snap returns the error—
Column names should be the same in the target table and CSV fileand aborts the load operation.You cannot add a column to your BigQuery target table with a deleted column name using the ELT Load Snap, as BigQuery reserves deleted column names and data until the pre-configured time travel duration (from 2 through 7 days).
Due to an issue with BigQuery table schema management (the time travel feature), an ALTER TABLE action (Add or Update column) that you attempt after deleting a column (DROP action) in your BigQuery target table causes the table to break and the Snap (ELT Load, ELT SCD2, or ELT Execute) to fail.
As a workaround, you can consider either avoiding ALTER TABLE actions on your BigQuery instance or creating (CREATE) a temporary copy of your table and deleting (DROP) it after you use it.
Due to an issue with the Simba Spark JDBC driver for DLP, you cannot insert a NULL value in the nullable columns of Boolean data type in your DLP instance using any of the write-type Snaps—ELT Load, ELT SCD2, ELT Insert Select, ELT Merge Into, and ELT Execute, when the target table schema is available.
The only workaround currently available for this issue is to upgrade your JDBC driver to databricks-jdbc-2.6.25-1.jar, use the corresponding JDBC driver class (
com.databricks.client.jdbc.Driver) and JDBC URL in your Snap account.
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The ELT Load Snap does not cause any NULL values in new columns added to the target table through the Alter table Load Action.
The ELT Merge Into Snap fails when you perform an UPDATE action in the (hash) distribution key column of an Azure Synapse table. The failure occurs because Azure Synapse does not support modifying values in a table (hash) distribution key column.
Due to an issue with DLP, aborting an ELT Pipeline validation (with preview data enabled) causes only those SQL statements that retrieve data using bind parameters to get aborted while all other static statements (that use values instead of bind parameters) persist.
For example,
select * from a_table where id = 10will not be aborted whileselect * from test where id = ?gets aborted.
To avoid this issue, ensure that you always configure your Snap settings to use bind parameters inside its SQL queries.
The ELT Math Function Snap fails during Pipeline execution even after successful validation against the Redshift CDW due to the incompatible or incorrect data types associated with the target table columns created during the Pipeline validation. To prevent this failure, we recommend that you manually delete the table created during validation before running the Pipeline.When you add an input view to the Databricks - Delete Snap, ensure that you configure the Batch size as 1 in the Snap’s account configuration. For any other batch size, the Snap fails with the exception: Multi-batch parameter values are not supported for this query type.
Flows
New Features
Introduced the following endpoints:
Redshift as a source to fetch data from a specific table of the Redshift database and also as a target to insert, update, or delete data into its database.
Snowflake as a source to fetch data from a specific table of the Snowflake database and also as a target to insert, update, or delete data into its database.
Google BigQuery as a target to load data into the BigQuery data platform.
MongoDB as a source to execute the find command on your MongoDB database and also as a target to execute the insert, update, or delete command on its database.
PostgreSQL as a source to fetch data from a specific table of the PostgreSQL database and also as a target to execute SQL statements to insert, update, or delete data within a specified table of the PostgreSQL database.
SQL Server as a source to fetch data from a specific table of a SQL Server database and also as a target to insert, update, or delete data within a specific table of the SQL Server database.
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