ELT Execute

In this article

An account for the Snap

You must define an account for this Snap to communicate with your target CDW. Click the account specific to your target CDW below for more information:

Overview

You can use this Snap to execute SQL queries in the target database—Snowflake, Redshift, Azure Synapse, Databricks Lakehouse Platform (DLP), or BigQuery. You can run the following types of queries using this Snap:

  • Data Definition Language (DDL) queries

  • Data Manipulation Language (DML) queries

  • Data Control Language (DCL) queries


Prerequisites

Valid accounts and access permissions to connect to the following:

  • Source: AWS S3, Azure Cloud Storage, or Google Cloud Storage

  • Target: Snowflake, Redshift, Azure Synapse, DLP, or BigQuery

If you want to use the COPY INTO command for loading data into the target database, you must pass (expose) these account credentials inside the SQL statement. Hence, we recommend you to consider using the ELT Load Snap as an alternative.

Limitations

  • This Snap does not support multi-statement transaction rollback of any of the DDL, DCL or DML statements specified.

  • Each statement is auto-committed upon successful execution. In the event of a failure, the Snap can rollback only updates corresponding to the failed statement execution. All previous statements (during that Pipeline execution runtime) that ran successfully are not rolled back.

  • You cannot run Data Query Language (DQL) queries using this Snap. For example, SELECT and WITH query constructs.

  • Use this Snap either at the beginning or in the end of the Pipeline. 

  • This Snap executes the SQL query only during Pipeline Execution. It does NOT perform any action (including showing a preview) during Pipeline validation.

  • ELT Snap Pack does not support Legacy SQL dialect of Google BigQuery. We recommend that you use only the BigQuery's Standard SQL dialect in this Snap.

Known Issues

  • While you specify an SQL statement in the SQL Statement Editor of an ELT Snap as an expression, the dynamic validation for the expression displays inline errors when there is more than one incoming document and without the '__sql__' key to the current Snap, when you select Get Preview Data checkbox in the previous Snap, and when Preview Document Count in your user settings is set to a value more than 1.

    • To prevent this error and similar ones, do not select the Get Preview Data checkbox in the previous Snap, set the Preview Document Count in your user settings to 1, or append a condition where 1 = 0 to the SQL statement with the Get Preview Data checkbox selected.

  • 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 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.

  • When your Databricks Lakehouse Platform instance uses Databricks Runtime Version 8.4 or lower, ELT operations involving large amounts of data might fail due to the smaller memory capacity of 536870912 bytes (512MB) allocated by default. This issue does not occur if you are using Databricks Runtime Version 9.0.
  • ELT Pipelines created prior to 4.24 GA release using one or more of the ELT Insert Select, ELT Merge Into, ELT Load, and ELT Execute Snaps may fail to show expected preview data due to a common change made across the Snap Pack for the current release (4.26 GA). In such a scenario, replace the Snap in your Pipeline with the same Snap from the Asset Palette and configure the Snap's Settings again.
  • The Snap’s preview data (during validation) contains a value with precision higher than that of the actual floating point value (float data type) stored in the Delta. For example, 24.123404659344 instead of 24.1234. However, the Snap reflects the exact values during Pipeline executions. 

In any of the supported target databases, this Snap does not appropriately identify nor render column references beginning with an _ (underscore) inside SQL queries/statements that use the following constructs and contexts (the Snap works as expected in all other scenarios):

  • WHERE clause (ELT Filter Snap)
  • WHEN clause
  • ON condition (ELT Join, ELT Merge Into Snaps)
  • HAVING clause
  • QUALIFY clause
  • Insert expressions (column names and values in ELT Insert Select, ELT Load, and ELT Merge Into Snaps)
  • Update expressions list (column names and values in ELT Merge Into Snap)
  • Secondary AND condition
  • Inside SQL query editor (ELT Select and ELT Execute Snaps)

Workaround

As a workaround while using these SQL query constructs, you can:

  • Precede this Snap with an ELT Transform Snap to re-map the '_' column references to suitable column names (that do not begin with an _ ) and reference the new column names in the next Snap, as needed.
  • In case of Databricks Lakehouse Platform where CSV files do not have a header (column names), a simple query like SELECT * FROM CSV.`/mnt/csv1.csv` returns default names such as _c0, _c1, _c2 for the columns which this Snap cannot interpret. To avoid this scenario, you can:
    • Write the data in the CSV file to a DLP table beforehand, as in: CREATE TABLE csvdatatable (a1 int, b1 int,…) USING CSV `/mnt/csv1.csv` where a1, b1, and so on are the new column names.
    • Then, read the data from this new table (with column names a1, b1, and so on) using a simple SELECT statement.
  • In case of Databricks Lakehouse Platform, all ELT Snaps' preview data (during validation) contains a value with precision higher than that of the actual floating point value (float data type) stored in the Delta. For example, 24.123404659344 instead of 24.1234. However, the Snap reflects the exact values during Pipeline executions.

Snap Input and Output

Input/OutputType of ViewNumber of ViewsExamples of Upstream and Downstream SnapsDescription
Input 

Document


  • Min: 0
  • Max: 1
  • ELT Select

  • ELT Insert Select

  • ELT Filter

An upstream Snap is not mandatory. Use the input view to connect the Snap as the terminating Snap in the Pipeline.

Output

Document

  • Min: 0
  • Max: 1
  • ELT Select

  • ELT Transform

A downstream Snap is not mandatory. Use the output view to connect the Snap as the first Snap in the Pipeline.

Snap Settings

  • Click the = (Expression) button in the Snap's configuration, if available, to define the corresponding field value using expression language and Pipeline parameters. 

  • Field names marked with an asterisk ( * ) in the table below are mandatory.

Field Name

Type

Field Dependency

Description

Label*

String

None.

The name for the Snap. You can modify this to be more specific, especially if you have more than one of the same Snap in your Pipeline.

Default Value: NA

Example: ELT Execute for SF

SQL Statements*

Fieldset

None.

Use this field set to define your SQL statements, one in each row. Click  to add a new row. You can add as many SQL statements as you need.

SQL Statement Editor*

String/Expression

None.

Enter the SQL statement to run, in this field. The SQL statement must follow the SQL syntax as stipulated by the target database—Snowflake, Redshift, Azure Synapse, Databricks Lakehouse Platform or BigQuery.
Alternatively, you can reference the SQL query from the preceding Snap's output and execute it. For example, enter $__sql__ in this field with the expression button '=' enabled to reference the SQL statement, if available, in the inputSQL1 key of the preceding Snap. 

SQL Comments are allowed

You can include inline comments and multi-line comments before, inside, or after your statement in this editor. It supports all standard SQL comment syntaxes as listed below:

-- comment text# comment text/* multi-line
comment text */
// comment text

Default Value: NA

Example: drop table base_01_oldcodes;

Pipeline Execution Statistics

As a Pipeline executes, the Snap shows the following statistics updating periodically. You can monitor the progress of the Pipeline as each Snap performs its action.

  • Records Added

  • Records Updated

  • Records Deleted


You can view more information as follows, by clicking the Download Query Details link to download a JSON file. In case of DLP, the Snap captures and depicts additional information (extraStats) on DML statement executions.

The statistics are also available in the output view of the child ELT Pipeline.

Troubleshooting

ErrorReasonResolution

Failure: DQL statements are not allowed.

The ELT Execute Snap does not support Data Query Language (DQL) and hence statements containing SELECT and WITH are not allowed.

Remove any DQL statements (containing SELECT, WITH) and enter one of the following statement types:

  • Data Definition Language (DDL): CREATE, ALTER, DROP, TRUNCATE, RENAME and so on. 

  • Data Control Language (DCL): GRANT, REVOKE

  • Data Manipulation Language (DML): INSERT, UPDATE, DELETE, MERGE, CALL and so on.

Multiple source rows are attempting to update or delete the same target row.

When you configure an ELT Execute Snap with a MERGE INTO statement that performs Update or Delete operation on a Databricks Lakehouse Platform cluster, it may return an error if multiple source rows attempt to update or delete the same target row. 

To prevent such errors, you need to preprocess the source table to have only unique rows.

No matching signature for operator = for argument types: INT64.

(Target CDW: BigQuery)

BigQuery treats the values of Pipeline parameters as String, by default. Passing a value with any other data type causes this error (INT64 in this example).

Cast any non-String Pipeline parameter used in your SQL statement to its target data type for the Snap to work as expected.

Ex: Consider using SELECT * FROM pipe.param01 WHERE id = cast( _id as INT ); instead of SELECT * FROM pipe.param01 WHERE id = _id;

Keyword RANGE is not acceptable as a column name.

(CDW: Databricks Lakehouse Platform)

This can happen with any reserved keyword if it is used as a column/field name in the table to be created.Ensure the enclose such column names (reserved keywords) between backticks (`). For example: `RANGE' STRING.

[Simba][SparkJDBCDriver](500051) ERROR processing query/statement. Error Code: 0

Cannot create table ('<schema name>`.`<table name>`'). The associated location (`…<table name>`) is not empty but it's not a Delta table

(Target CDW: Databricks Lakehouse Platform)

The specified location contains one of the following:

  • A non-Delta table (such as CSV, ORC, JSON, PARQUET)

  • A corrupted table

  • A Delta table with a different table schema

So, the Snap/Pipeline cannot overwrite this table with the target table as needed.

Ensure that you take appropriate action (mentioned below) on the existing table before running your Pipeline again (to create another Delta table at this location).

Move or drop the existing table from the schema manually using one of the following commands:

Access the DBFS through a terminal and run:

  • dbfs mv dbfs:/<current_table_path> dbfs:/<new_table_path> to move the table or

  • dbfs rm -r dbfs:/<table_path> to drop the table.

OR

Use a Python notebook and run:

  • dbutils.fs.mv(from: String, to: String, recurse: boolean = false): boolean to move the table/file/directory or

  • dbutils.fs.rm(dir: String, recurse: boolean = false): boolean to drop the table/file/directory.

[Simba][SparkJDBCDriver](500051) ERROR processing query/statement. Error Code: 0

Cannot create table ('<schema name>`.`<table name>`'). The associated location (`…<table name>`) is not empty but it's not a Delta table

(CDW: Databricks Lakehouse Platform)

A non-Delta table that currently exists is corrupted and needs to be dropped from the schema before creating a Delta-formatted table.

However, this corrupted table can only be dropped manually—by accessing the DBFS through a terminal. The Pipeline cannot perform this operation.

Drop the corrupted table and then try creating the new table in Delta format (using the Pipeline).

To drop the corrupted table, from the terminal, access the DBFS and run the following command:

dbfs rm -r dbfs:/<table_path>

Syntax error when database/schema/table name contains a hyphen (-) such as in default.schema-1.order-details.

(CDW: Azure Synapse)

Azure Synapse expects any object name containing hyphens to be enclosed between double quotes as in "<object-name>".Ensure that you use double quotes for every object name that contains a hyphen when your target database is Azure Synapse. For example: default."schema-1"."order-details".

Cannot execute bigger SQL queries.
The operation failed due to a quota of no more than 32500 Literals per Query, the actual value is 35080.

(CDW: Azure Synapse)

The SQL query should not exceed the maximum allowed limit of 32500 Literals per Query. 

Execute two or more SQL queries in place of one bigger query. See, 

 http://aka.ms/dwsoftlimits for more details

Fail to populate 1000 rows.

Not enough resources for query planning - too many subqueries or query is too complex.

(CDW:Big Query)

The Snap fails to execute if the query is too complex.

Split one large SQL query into two or more simple ones or request a higher quota limit from Google BigQuery.

Examples

Sample Queries for the ELT Execute Snap

Example 1: Updating a Target Table Based on Updates to Another Table

The following Pipeline updates a target (backup) table - OUT_ELT_EXECUTE_SF_003 periodically based on the updates to another table DT_EXECUTE_03 (source). These tables are present in a Snowflake database, and we use data views from this database to present the updates that the Pipeline does in the target table.

There are two steps to achieve this functionality using the ELT Execute Snap:

  1. Create a Pipeline with only the ELT Execute Snap for performing the periodic update.

  2. Create a Scheduled Task from this Pipeline to trigger a job at specific times of the day, as needed. See Tasks Page for information on creating Scheduled Tasks from Pipelines.

    • This task regularly looks into the DT_EXECUTE_03 table for updates and inserts the latest data from this table into the target (backup) table.

Before we create the Pipeline:

Source Table: DT_EXECUTE_03

Target Table: OUT_ELT_EXECUTE_SF_003

We configure the ELT Execute Snap to run a DML query, as follows. 

Once we create the Scheduled Task (after saving this Pipeline), the task runs as scheduled. Then, the ELT Execute Snap copies the data from the source table and inserts into the target (backup) table.

After the Scheduled Task/Pipeline is run:

Target Table: OUT_ELT_EXECUTE_SF_003

Download this Pipeline.

Example 2: Using one ELT Execute Snap to Create and Fill a Table

In this example Pipeline, we create a new table in the Redshift database and fill data into this table using an ELT Execute Snap. We later read the data from this table using an ELT Select Snap.

Let us observe the configuration of the ELT Execute Snap (first Snap in the above Pipeline).

ELT Execute Snap

Snap Output

We have added two SQL statements into the SQL Statements field set—one for creating/overwriting a table and another for inserting a row into same table. ELT Execute Snap does not have a data preview except for the placeholder SQL statement that indicates the Snap is validated successfully. The Snap executes the SQL queries real-time when we run the Pipeline.

We connect an ELT Select Snap to the ELT Execute Snap to read the data from the newly-created table in the Redshift database. In this Snap:

  • We use the same ELT Database account that we use for the previous Snap.

  • Define/select the values for the database, schema and the table name to identify the table that the previous Snap is configured to create.

    • Alternatively, we can enable the SQL query editor and include the select * from dev.public.new_table_trg; statement. 

    • It is also important here to note that we cannot run this DQL query using the ELT Execute Snap.

ELT Select Snap

Snap Output

Download this Pipeline.

Downloads

Important Steps to Successfully Reuse Pipelines

  1. Download and import the Pipeline into SnapLogic.
  2. Configure Snap accounts as applicable.
  3. Provide Pipeline parameters as applicable.

  File Modified

File ELT_Execute_FEP1.slp

Nov 08, 2021 by Anand Vedam

File ELT_Execute_Multiple_FEP2.slp

Nov 08, 2021 by Anand Vedam

Snap Pack History

 Click here to expand...

Release

Snap Pack Version 

Date

Type

Updates

August 2024

438patches28010

 Latest

The ELT Insert-Select Snap no longer fails to execute SQL statements that contain multiple multiline comment character pairs (/* and */) and/or multiple quoted substrings. Quoted substrings refer to schema, database, table, or column identifiers, which are delimited to allow special characters.

  • We recommend that you upgrade your main27765 (August 2024 GA release) ELT Snap Pack to this latest version.

August 2024main27765 Stable

Upgraded the jOOQ library for the ELT Snap Pack from v3.9.1 to v3.17.x.

May 2024437patches27372 Latest

Enhanced the pipeline execution statistics of ELT Insert-Select Snap to be displayed in its output view and to allow downloading detailed stats as a JSON file that includes additional statistics (extraStats) on DML statement executions on target Databricks Lakehouse Platform (DLP) tables.

May 2024437patches27246 Latest

Enhanced the ELT Execute Snap to display SQL execution statistics in the pipeline execution statistics and the output view of the Snap for all SQL statements executed. The Snap also allows you to download detailed stats as a JSON file that includes additional statistics (extraStats) on DML statement executions on target Databricks Lakehouse Platform (DLP) tables.

May 2024437patches26846 LatestFixed the issue with the ADLS Gen 2 account connection where some conflicts between internally used Azure libraries prevented the ELT Load Snap from reading files.

Fixed an issue with the ELT Merge Into Snap where the Snap’s SELECT SQL statement could not fetch the target tables information from the PG_TABLE_DEF catalog table of the Amazon Redshift instance.

  • The SELECT SQL statement now uses lowercase for both non-delimited and delimited table names and excludes double quotes from delimited table names to fetch the values from the catalog table.

PG_TABLE_DEF is a Redshift system catalog table that contains information about the tables including table names, column names, data types among their other metadata.

Enhanced the ELT Load Snap to support loading data from nested AVRO, JSONLines, ORC, or Parquet files in Azure storage to the target tables in a Databricks Lakehouse Platform (DLP) instance. Only two Load actions are supported: Drop and create table and Append table.
May 2024main26341 Stable

Fixed an issue where the ELT Merge Into Snap failed to load data into the Google BigQuery target table because of the error: Number of source column expressions in the input SQL is greater than the target table columns (even after altering the target table schema to match the source table columns). The resolution was to modify an SQL statement that was used internally to achieve the merge action.

February 2024436patches25953  Latest

Enhanced the ELT Load Snap’s capabilities to allow loading flat and nested data sets (from canonical and non-canonical formats) from your Parquet files to the target tables in Snowflake. Learn more about the usage of this feature at Load data from Parquet files and in the following example Pipelines:

February 2024main25112 StableUpdated and certified against the current SnapLogic Platform release.
November 2023435patches24461 LatestFixed an issue with the ELT Merge Into Snap that caused inaccurate aliases and table identifiers in its generated SQL statement.
November 2023435patches23671 Latest and Stable
  • Enhanced the ELT Merge Into Snap to support defining the update expressions for updating tables in your target Databricks Lakehouse Platform instance using the WHEN NOT MATCHED BY SOURCE clause, besides other supported clauses and expression lists.

November 2023main23721 Stable
  • Enhanced the ELT Merge Into and ELT SCD2 Snaps to display detailed pipeline execution statistics about the data loaded to target tables on a Databricks Lakehouse Platform instance.

    • In addition to the individual counts of rows inserted, updated, and deleted that the ELT Merge Into Snap covers, the ELT SCD2 Snap also reports the count of source rows rejected.

August 2023main22460 Stable
  • Upgraded the JDBC driver support for Snowflake to snowflake-jdbc-3.13.33.jar.

  • Updated the ELT Snowflake Account to append the connection parameter application in its JDBC URL with the value SnapLogic_iPaaS.

May 2023N/A Stable

Fixed a null pointer exception so no 5XX errors can occur if you download non-existent query details from the Pipeline Execution Statistics of an ELT (write-type) Snap.

May 2023main21015 StableUpgraded with the latest SnapLogic Platform release.
February 2023432patches20978 LatestFixed an issue with the ELT SCD2 Snap where the COLLATE column constraint (used in the new target table definition for Snowflake) resulted in an incorrect syntax internally, causing the pipeline to fail. The load operation succeeds with this fix.
February 2023main19844 StableUpgraded with the latest SnapLogic Platform release.
November 2022431patches19240

 

Latest
  • SnapLogic upgraded the default JDBC Driver versions used to connect the ELT Snaps with the supported CDWs.
  • The ELT Merge Into Snap displays the individual record counts inserted, updated, and deleted for Snowflake targets in the respective Records Inserted, Records Updated, and Records Deleted parameters of the Snap Statistics tab (on Pipeline execution).
  • With Google deprecating the OAuth out-of-band (OOB) flow, the Refresh Token Accounts defined for connecting your ELT Snaps to the BigQuery instances start failing in a phased manner. We recommend that you immediately modify these Snap account configurations to switch to an Access Token Account or a Service Account from the Refresh Token Account.
November 2022main18944 Stable

ELT Insert-Select, ELT Merge Into, and ELT SCD2 Snaps show the following statistics on execution.

  • Records Added

  • Records Updated

  • Records Deleted

September 2022 430patches18196 Latest

New Snap

The ELT Create View Snap enables you to create a new view when the view does not exist in the target database and/or schema or if the view already exists in the database and/or schema, and you choose to drop the existing view and re-create it.

Enhancements

  • 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.

  • 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 PivotSnap 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 field set 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 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.

August 2022main17386 StableUpgraded with the latest SnapLogic Platform release.
4.29-Patch429patches16665 Latest
  • Enhanced the ELT Snap Pack to support the latest JDBC drivers across CDWs—Azure Synapse, BigQuery, DLP, Redshift, and Snowflake. See Configuring ELT Database Accounts or the respective Account page for the exact versions.

  • Enhanced the ELT Pivot Snap to make the Value List field dynamic.

  • Enhanced the ELT DLP Account to configure S3 Bucket, Azure Storage, and DataLake Storage Gen2 Mounts.
  • Enhanced the ELT Snowflake Account with support for Key Pair Authentication.

  • Enhanced the ELT SCD2 Snap:

    • To include a new option Overwrite existing table in the Target Table action field.

    • To display the final SQL query in its output preview upon Pipeline validation.

  • Enhanced the end Snap SQL query in the ELT Insert Select Snap’s preview output to display the CREATE TABLE... or the DELETE/DROP TABLE statements to be run before the query that inserts/loads data into a new table in the Snowflake target CDW.

  • Fixed an issue with ELT Insert Select and ELT Merge Into Snaps where they cause the Pipeline to fail when the specified target table does not exist. After this fix, the Snaps create a new target table if it does not exist during Pipeline validation.

    The new table that is created will not be dropped in the event of a subsequent/downstream Snap failing during validation.

  • Fixed the issue with ELT Load Snap where the Snap caused an SQL exception—[Simba][SparkJDBCDriver](500051) ERROR processing query/statement when reading from a CSV file in S3 mount point on DBFS in the case of a DLP target instance.

  • The ELT Insert Select, ELT Merge Into, ELT Load, and the ELT SCD2 Snaps now run successfully even when the specified target table does not exist. These Snaps now create a new target table if it does not exist during Pipeline validation.

    The new table thus created will not be dropped in the event of a subsequent/downstream Snap failure during validation.

  • Updated the field names in ELT Aggregate, ELT Cast Function, ELT String Function, and ELT Transform Snaps to maintain consistency.
4.29-Patch

4.29patches16287

 Latest

Fixed an issue with the ELT SCD2 Snap where the Snap was rounding off decimal values to the nearest integer—the value 57.601000000000 in the source table was written to the target table as 58.000000000.

4.29main15993 Stable
  • Introduced the following new ELT Snaps:
    • ELT Cast Function: Snap to convert a data type of a column in the input SQL string into other supported data types.
    • ELT String Function: Snap to support the various string functions supported by the different databases.
    • ELT Router: Snap to enable routing input SQL queries into multiple output views based on the given conditional expressions.
  • Enhanced the following Snaps to display the final SQL query in their output preview upon Pipeline validation.
  • Enhanced the ELT Database Account to support OAuth2-based authentication on the target Snowflake database.

  • Enhanced the ELT Select, ELT Insert Select, ELT SCD2, ELT Merge Into, and ELT Load Snaps to display suggestions on the Schema Name field based on the Default Database Name provided in the Snap Account configuration when the Database Name is not specified in the respective Snap.

    • Improved usability of the suggestions features for these Snaps by making them case-insensitive. For example, typing default in the Schema Name field displays both default and DEFAULT, if they co-exist. You do not need to type DEFAULT to invoke and select the schema name DEFAULT from the suggestions list.

  • Enhanced the ELT SCD2 Snap to address different feature requests and issues raised by multiple customers. These changes provide more flexibility in configuring your SCD2 operations using this Snap.

  • Removed Check for nulls and duplicates in the source field and added two dropdown lists - Null Value Behavior and Invalid Row Handling.

  • Made the following items in the Meaning field of the Target Table Temporal Fields fieldset mandatory while making the Invalid historical rows flag optional.

    • Current row 

    • Historical row

  • Enhanced the ELT Aggregate and ELT Window Functions Snaps to support the following functions across all supported CDWs:

    • KURTOSIS

    • MODE

    • SKEW

  • Enhanced the ELT Aggregate Snap to support the following GROUP BY features across all supported CDWs:

    • Group by Cube

    • Group by Grouping Sets

    • Group by Rollup

    • Automatic GROUP BY for all input columns.

  • Fixed an issue with ELT Merge Into Snap where the Snap erroneously modified the target table column name when the column name contained the target table name.

  • Fixed an issue in ELT SCD2 Snap where the Snap causes incorrect results with Snowflake targets, when:

    • The Historical Row End Date value is provided.

    • Nulls and Invalid rows are recognized, but one or more start dates in the source are null.

  • Fixed the issue in ELT Transform Snap where the Output Schema of the Snap does not populate all the column names from its Input Schema.

4.28-Patch428patches15638 Latest

Fixed the issue with ELT Merge Into Snap where the Snap erroneously modified the target table column name when it contained the target table name, due to a misinterpretation of the target table name aliases.

4.28-Patch428patches15290 Latest
  • Updated ELT SCD2 Snap to address different feature requests and issues raised by multiple customers. These changes provide more flexibility in configuring your SCD2 operations using this Snap.

    • Removed Check for nulls and duplicates in source field and added two dropdown lists - Null Value Behavior and Invalid Row Handling.

    • Refer to the ELT SCD2 scenarios to learn more.

  • Introduced a new Snap ELT Router to enable routing input SQL queries into multiple output views based on the given conditional expressions.

4.28main14627 Stable
  • Subquery Pushdown Optimization: SnapLogic now optimizes SQL queries before they are passed to the CDW to ensure the queries are performant and cost-efficient in the respective CDW. An SQL subquery means a query inside a query. Pushdown optimization refers to rewriting these incremental (nested) SQL queries produced in your ELT Pipeline to form a more optimal/performant version.

  • Introduced the following new ELT Snaps:

    • ELT Case Expression: Snap to return the action to perform on an event based on a list of events and respective expected actions.

    • ELT Coalesce: Snap to return the first non-NULL value from a list of arguments.

    • ELT Conditional Functions: Snap to perform unary and binary conditional operations on data.

    • ELT Math Functions: Snap to perform mathematical—arithmetic, logarithmic, trigonometric, exponent, root, rounding, and truncation—operations on data.

  • Enhanced all the expression-enabled fields in ELT Snaps to display suggestions from the Input Schema (emanating from the upstream Snaps) in addition to the existing standard SQL expressions and functions list.

  • Enhanced the ELT Aggregate Snap to support:

    • HAVING clause within GROUP BY clause, when the WHERE clause cannot be used.

    • GROUP BY ROLLUP.

    • New aggregate functions for DLP: ANY, SOME, KURTOSIS, and STDDEV.

  • Enhanced the ELT Load Snap to support loading data into BigQuery targets from S3 buckets and Redshift CDW. These load operations use the BigQuery Data Transfer Service (DTS) client libraries and are carried out in asynchronous mode.

  • Enhanced the ELT Load and ELT Insert Select Snaps with a new fieldset Table Options List to support defining the Table Options for creating a new table in your target CDW.

  • Enhanced the ELT Select Snap to support Common Table Expression (CTE)-based SQL queries that contain a WITH clause inside the SQL Query Editor field, when your target CDW is Azure Synapse.

4.27-Patch

427patches13923

 Latest
  • Fixed the issue with ELT SCD2 Snap where the Snap did not equate null values in the corresponding cause-historization rows of both the source and target tables (with no other changes to data in the remaining fields) as the same and produced duplicate rows in the target table, as a result. After this fix, the Snap does not cause any new duplicate rows in the target table.

  • Fixed the issue with ELT Load Snap where the Snap fails with the error Database encountered an error during Bulk Load process when you specify a CSV file to load data from, with the Load Action as Alter Table. The Snap now performs the specified ALTER TABLE actions—ADD/DROP columns—and loads the data into the target table accordingly (without the need to manually modify the source or target tables beforehand).

4.27-Patch427patches13539 Latest
  • Fixed the issue with ELT SCD2 Snap where the Snap failed when you define more than one TargetTable Natural Key in the Snap configuration to load SCD2 data into the target CDW instance.

  • Fixed an issue with the ELT SCD2 Snap where the Snap failed to update the previous current rows in the target SCD2 table to historical rows when you define an End Date of Historical Row in Target Table Temporal Fields.

  • Fixed an issue with the ELT SCD2 Snap where the Snap failed to insert new rows when you define the values that exist in the most recent historical rows of the target SCD2 table as the cause-historization values.

  • Fixed the issue with ELT Transform Snap where the Snap does not omit the source columns marked for removal from the output view—using an empty Target Path for one or more columns selected in the Expression field of the Snap’s Mapping Table. See Using Empty Target Paths to Omit Rows from the Snap Output to understand how to perform this operation.

4.27-Patch

427patches13030

 Latest
  • Fixed the following issues with the ELT SCD2 Snap:

    • Where the Snap failed to get the right data type due to column name case mismatches between what is used in the Snap and what is actually used in the Azure Synapse tables (returned by the JDBC driver). You no longer need to type the column names in the exact case that Azure Synapse expects.

    • The Snap failed with the error—start_date does not exist—while writing SCD2 data to a Redshift table column start_date that is specified as the Start Date of Current Row in the Target Table Temporal Field(s) field set.

    • The Snap failed with the error—Reference 'END_DATE' is ambiguous—while merging SCD2 updates into DLP tables.

    • Where the Snap failed due to lack of required access privileges on the target database (for example, create table rights to create temporary tables as needed). The Snap now runs the input SQL statement and the elaborate sub-queries instead of attempting to create a temporary table in such scenarios.

  • Fixed the issue with the ELT SCD2 and ELT Load Snaps that fail to perform an add/drop operation involving multiple columns on a Redshift target table.

4.27main12833 Stable
  • Enhanced the ELT Aggregate Snap to support COUNT_IF aggregate function for Redshift and Azure Synapse target databases.
    Eliminating duplicates with COUNT_IF aggregate function
    Note: Selecting the Eliminate Duplicates checkbox while using COUNT_IF aggregate function does not eliminate duplicate records in case of Snowflake and BigQuery databases, as there is no native support for this feature. However, for Redshift, Azure Synapse and Databricks Lakehouse Platform (DLP), the duplicates are eliminated from the list of records when you select this checkbox for COUNT_IF function.

  • Enhanced the ELT Database Account connecting to a Databricks Lakehouse Platform (DLP) to support two new options - Optimize Write and Auto Compact for creating/replacing a table using any of the ELT Insert SelectELT Merge IntoELT Load, and ELT SCD2 Snaps.

  • Updated the Expressions and Functions Supported for ELT in the Snap and Account configuration sections. This list is common to all target CDWs supported. You can use these expressions to define your Snap or Account settings with the Expression symbol = enabled, where available.

  • Enhanced the ELT Load Snap to ensure that the Snap uses the default S3 Folder name specified in the Snap's account to accurately resolve the defined File Name Pattern.

  • Enhanced the ELT Select and ELT Execute Snaps to allow SQL comments inside the SQL Query Editor and SQL Statement Editor fields respectively.

  • Enhanced the ELT Transform Snap to display the exact data types of fields listed in the Input Schema and Target Schema in case of Azure Synapse.

  • Enhanced the ELT Merge Into Snap to to support MERGE INTO ALL option and automatic source table aliasing.

  • Enhanced the ELT Load Snap by adding the Source File to Target Table Columns Map field set to enable mapping of columns between the source file and the target table. In case of Databricks Lakehouse Platform (DLP) the Snap is enhanced further to support delimiters other than the comma in the source CSV files.

  • Enhanced the ELT Join Snap to support Left Anti and Left Semi join types in BigQuery though it does not natively support these join types.

  • Enhanced the underlying load mechanism for ELT SCD2 Snap from Insert-and-Update mode to Merge-into mode to substantially improve the Snap's performance while working with large and very large volumes of data (upwards of 500M rows or 50GB size).

4.26-Patch426patches12534 Latest
  • Fixed an issue with ELT Transform Snap where it may display incorrect schema only in the previews (during Pipeline validation). This occurs especially when the incoming SQL statement (defined in the SQL Statement Editor of the upstream Snap) contains one or more of the WHERE, GROUP BY, HAVING , ORDER BY, LIMIT, LIMIT followed by OFFSET, and SAMPLE clauses. Here are a few Pipeline scenarios where this issue might surface:

4.26-Patch426patches12021 Latest
  • Fixed an issue where the ELT Load Snap connecting to a Databricks Lakehouse Platform (DLP) instance failed to perform the load operation. Ensure that you provide a valid DBFS Folder path in the Snap's account settings as the Snap requires this folder path.

4.26-Patch426patches11646 Latest
  • Enhanced the ELT Database Account to support token-based authentication (Source Location Session Credentials) to S3 locations for Snowflake and Redshift target databases.
  • Enhanced the ELT Aggregate Snap with the following changes:
    • Revised the field labels from:
      • GROUP BY Fields List field set > Output Field to GROUP BY Field.
      • ORDER-By Fields to ORDER-BY Fields (Aggregate Concatenation Functions Only).
    • Removed the Suggestion option for Field Name field under General Aggregate Functions List field.
    • Made the Alias Name fields in the Aggregate Concatenation Functions List and the Percentile Distribution Functions List field sets mandatory.
  • If your target database is a Databricks Lakehouse Platform (DLP) instance, then the ELT Load Snap supports loading data from source CSV files that contain only comma as the separator between values.
4.26-Patch426patches11323 

Latest

  • Enhanced the ELT Database Account to allow parameterization of field values using Pipeline Parameters. You can define and use these parameters in expression-enabled fields to pass values during runtime.
4.26-Patch426patches11262Latest
  • Fixed the following Known Issues recorded in the 4.26 GA version:
    • For a Snowflake target instance, the ELT Insert Select Snap does not suggest column names to select for the Insert Column field in the Insert Expression List.
    • The Snaps—ELT Merge Into, ELT Select, ELT Join, and ELT Filter—do not prevent the risk of SQL injection when your target database is Databricks Lakehouse Platform (DLP).
    • Intermittent null-pointer exceptions in the ELT Load Snap on Databricks Lakehouse Platform (DLP).

    • The ELT Insert Select Snap attempts to create the target table even when it exists in the Snowflake database.
    • When loading data from a JSON file into a target Databricks Lakehouse Platform (DLP) instance using an ELT Load Snap, if you choose the Drop and Create Table option as the Load Action and specify an additional column (that is not available in the JSON file) for the new table, it results in one more column null added to the new target table.
    • When you use the SQL editor in the ELT Select Snap configuration to define your SQL query, the Pipeline validation fails due to a syntax error in the following scenarios. However, the Pipeline execution works as expected. The only workaround is to drop the LIMIT clause and the optional OFFSET clause from the SQL query during Pipeline validation.
      • The query contains a LIMIT clause on a Snowflake, Redshift or Databricks Lakehouse Platform target instance: The SQL query created during Pipeline validation includes an additional LIMIT clause, for example: SELECT * FROM "STORE_DATA"."ORDERS" LIMIT 10 LIMIT 990

      • The query contains an OFFSET clause (supported in case of Snowflake and Redshift): The SQL query created during Pipeline validation looks like SELECT * FROM "STORE_DATA"."ORDERS" LIMIT 10 OFFSET 4 LIMIT 990
4.26main11181 Stable
  • Enhanced the ELT Snap preview to support the following Snowflake data types: array, object, variant, and timestamp.

    • The Snaps convert the values to hexadecimal (HEX) equivalents—the default setting for the session parameter BINARY_OUTPUT_FORMAT in Snowflake. See Session Parameters for Binary Values for more information.

    • If this setting is different from hexadecimal (such as base64) in the Snowflake table, the Snaps still convert the values to hexadecimal equivalents for rendering them in the Snap preview.

  • Enhanced all ELT Snaps to display the Get preview data checkbox below the Snap's Label field.
  • The ELT Database account is now mandatory for all Snaps in the ELT Snap Pack.

    Breaking change

    Starting with the 4.26 release, all Snaps in the ELT Snap Pack (except the ELT Copy Snap) require an account to connect to the respective target database. Your existing Pipelines that do not use an account may fail. We recommend you to associate an ELT Database Account to each of the ELT Snaps (except ELT Copy Snap) for your Pipelines.

  • Enhanced the ELT Aggregate Snap to support Linear Regression functions on Redshift and Azure Synapse. The Snap also supports these functions on Databricks Lakehouse Platform.
  • Enhanced the ELT Execute Snap to enable running multiple DML, DDL, and DCL SQL statements from the same Snap instance.
  • Enhanced the ELT Join Snap to: