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Snap Pack | Date of Update | Snap Pack Version | Updates |
|---|---|---|---|
| 430patches17796 | Learn more about this the Databricks patch update updates here. | |
| 430patches18196 | Learn more about the ELT patch updates here. | |
| 430patches18099 |
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| 430patches18223 | The MongoDB Update Snap handles the lineage correctly in the Ultra Pipeline and works as expected. The requests are now acknowledged correctly. | |
| 430patches18119 | The Transcoder Snap used in a low-latency feed Ultra Pipeline now acknowledges the requests correctly. | |
| 430patches18070 | The Pipeline Execute Snap with binary output that is used in a low-latency feed Ultra Pipeline now works as expected. The requests are now acknowledged correctly. | |
| 430patches17933 |
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| 430patches18036 | The Salesforce Read Snap now correctly parses the 2-byte UTF-8 characters in Windows OS in the PK chunking mode. | |
| 430patches17802 | The Avro Parser Snap now displays the decimal number correctly in the output view if the column’s logical type is defined as a decimal. | |
| 430patches17872 | The Azure Directory Search Snap does not fetch duplicate records when the Group result checkbox is deselected. | |
| 430patches17962 | The Snowflake Bulk Load Snap now triggers the metadata query only once even for invalid input, thereby improving the performance of Snap. | |
| 430patches17894 | The Generic JDBC Snaps connecting to the DB2 database now take lesser time to execute thereby improving the performance. | |
| 430patches17851 | The REST Post Snap now works without displaying any errors when the Show all headers checkbox is selected and the Content-type is text/xml or application/xml. | |
| 430patches17894 | The following Snaps now work as expected when the table name is dependent on an upstream input: | |
| 430patches17841 | Introduced the Google AlloyDB Snap Pack— a fully managed PostgresSQL-compatible database service that you can use for all your database workloads. This Snap Pack offers the following Snaps: | |
430patches17737 | AutoPrep enables you to handle empty or null values. |
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The ELT Insert-Select Snap is more flexible and easier to use, especially if the number of columns in your source data set is very large. You can choose to update values only in a subset of columns in the target table.
The ELT Execute Snap can retrieve and execute SQL queries from the upstream Snap's output when referenced in the SQL Statement Editor using the Expression language (with the Expression button enabled).
The ELT Load Snap can infer the schema from the source files in Amazon S3, ADLS Gen2, Microsoft Azure Blob Storage, or Google Cloud Storage location and use it to create, overwrite, and append the target table in your Snowflake instance with the source data. The source files can be in the AVRO, CSV, JSON, ORC, or PARQUET format. Learn more at Automatic Schema Inference with ELT Load Snap.
The Target Table Name in the following Snaps supports retrieving editable views with the table names from the selected target schema:
The pivot values in the ELT Pivot Snap turns dynamic when you select Enable dynamic pivot values. The following field settings are added as part of this dynamic pivot values feature:
Filter Predicate List: A fieldset to filter the predicate list of the pivot values.
Pivot Values Filter: Condition required to filter the pivot values.
Boolean Operator: Predicate condition type through AND or OR Boolean operators
Sort Order: Sorting order of the pivot values.
You In the ELT Account, you can specify the type of Microsoft Azure external storage location (source)—an Azure Data Lake Gen2 or a Blob Storage—to access your source data using the Storage Integration type of authentication and load it to your target Snowflake instance.
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Metrics Page
Charts on the Metrics page show trends in runtime behavior for a Snaplex node:
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