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 add window functions such as rank-relation functions, aggregate functions, and bitwise functions to the table's data. See Snowflake Window Functions, Redshift Window Functions, Azure Synapse Window Functions, Azure Databricks Window Functions, or BigQuery Expressions, functions, and operators for details on these functions.

Prerequisites

None.

Limitation

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

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/Output	Type of View	Number of Views	Examples of Upstream and Downstream Snaps	Description
Input	Document	Min: 1 Max: 1	ELT Select ELT Copy	The data on which to add the window functions.
Output	Document	Min: 1 Max: 1	ELT Transform ELT Insert-Select	The input data modified with the window functions. If the Pass through check box is selected, the output also includes the original input data.

Snap Settings

SQL Functions and Expressions for ELT

You can use the SQL Expressions and Functions supported for ELT to define your Snap or Account settings with the Expression symbol = enabled, where available. This list is common to all target CDWs supported. You can also use other expressions/functions that your target CDW supports.

Parameter Name

Data Type

Description

Default Value

Example

Label

String

Specify a 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.

ELT Window Functions

Aggregate Salary Data

Get preview data

Check box

Select this checkbox to include a preview of the query's output. The Snap performs limited execution and generates a data preview during Pipeline validation.

In the case of ELT Pipelines, only the SQL query flows through the Snaps but not the actual source data. Hence, the preview data for a Snap is the result of executing the SQL query that the Snap has generated in the Pipeline.

The number of records displayed in the preview (upon validation) is the smaller of the following:

Number of records available upon execution of the SQL query generated by the Snap.
The value set in the Preview Data Count setting (default is 50 records).

Rendering Complex Data Types in Databricks Lakehouse Platform

Based on the data types of the fields in the input schema, the Snap renders the complex data types like map and struct as object data type and array as an array data type. It renders all other incoming data types as-is except for the values in binary fields are displayed as a base64 encoded string and as string data type.

Not selected

Selected

Pass through

Check box

Select to include the original data along with the aggregated data in the output. Otherwise, the output has only aggregated data.

Not selected

Selected

Rank Related Functions List

Use this field set to enter the rank-related function to apply to the table's data. You must also specify an alias for each rank-related function. Specify each rank-related function as a separate row. Click to add a new row.

This field set contains the following fields:

Rank Function
Alias Name

Rank Function

String/Suggestion

Enter the rank-related function to apply to the table data. Alternatively, click to view and select a rank function from the suggestion list. The list displays the functions supported by the database that you selected in the Account settings. An error is displayed if an account is not configured. See the Troubleshooting section for details.

The following functions are available for Snowflake, Redshift, Azure Synapse and Databricks Lakehouse Platform:

CUME_DIST
DENSE_RANK
PERCENT_RANK
ROW_NUMBER
RANK

The Snap fails during Pipeline validation/execution if you specify an incorrect function name.

For more information on the ranking functions, refer to the following:

N/A

CUME_DIST

Alias Name

String

Enter the alias name to associate with the specified rank function.

N/A

TOTAL

General Window Functions List

Use this field set to apply the aggregate function on the specified field in the table's data. You must also specify an alias for each aggregate function. Specify each aggregate function as a separate row. Click to add a new row.

This field set contains the following fields:

Aggregate Function
Field
Alias Name

Aggregate Function

String/Suggestion

Enter the name of the aggregate function to apply to the table data. Alternatively, click to view and select an aggregate function from the suggestion list. The list displays the functions supported by the database that you select in the Account settings. An error is displayed if an account is not configured. See the Troubleshooting section for details.

The available functions are:

Snowflake

Redshift

Azure Synapse

COUNT
SUM
MIN
MAX
AVG
ANY_VALUE
MEDIAN
MODE
STDDEV_POP
STDDEV_SAMP
VAR_POP
VAR_SAMP
CONDITIONAL_CHANGE_EVENT
CONDITIONAL_TRUE_EVENT
COUNT_IF
RATIO_TO_REPORT
NTILE
SKEW
KURTOSIS

See Snowflake Window Functions for more information.

COUNT
SUM
MIN
MAX
AVG
MEDIAN
STDDEV_POP
STDDEV_SAMP
VAR_POP
VAR_SAMP
RATIO_TO_REPORT
NTILE
SKEW
MODE
KURTOSIS

See Redshift Window Functions for more information.

COUNT
COUNT_BIG
SUM
MIN
MAX
AVG
STDEV
STDDEV
VAR
VARP
NTILE
SKEW
MODE
KURTOSIS

See Synapse Window Functions for more information.

Databricks Lakehouse Platform

BigQuery

COUNT
SUM
MIN
MAX
AVG
STDDEV_POP
STDDEV_SAMP
VAR_POP
VAR_SAMP
NTILE
SKEW
MODE
KURTOSIS

See Azure Databricks Window Functions for more information.

ANY_VALUE
ARRAY_AGG
AVG
CORR
COUNT
COUNTIF
COVAR_POP
COVAR_SAMP
MAX
MIN
NTILE
STDDEV_POP
STDDEV_SAMP
STRING_AGG
SUM
VAR_POP
VAR_SAMP
SKEW
MODE
KURTOSIS

See BigQuery Aggregate Functions and Statistical Aggregate Functions for more information.

The Snap fails during Pipeline validation/execution if you specify an incorrect function name.

N/A

COUNT

Field

String/Expression

Enter the name of the field in the input table on which to apply the aggregate function.

N/A

SALARY

Alias Name

String

Enter the alias name to associate with the specified aggregate function.

N/A

TOTAL_SALARY

Bitwise Aggregate Functions List (Snowflake Only)

Specify the bitwise aggregate function to apply to the specified field in the table's data. You must also specify an alias for each bitwise aggregate function. Specify each bitwise aggregate function as a separate row. Click to add a new row.

Applicable only to the Snowflake database.

This field set contains the following fields:

Bitwise Aggregate Function
Field
Alias Name

Bitwise Aggregate Function

String/Suggestion

Enter the name of the bitwise aggregate function to apply to the table data. Alternatively, click the button to view and select a bitwise aggregate function from the list of supported bitwise aggregate functions.

Available options are:

The Snap fails during Pipeline validation/execution if you specify an incorrect function name.

N/A

BITOR_AGG

Field

String/Expression

Enter the name of the field in the input table upon which to apply the bitwise aggregate function.

N/A

RANGE

Alias Name

String

Enter the alias name to associate with the specified bitwise aggregate function.

N/A

BITOR_RANGE

Boolean Aggregate Functions List (Snowflake Only)

Specify the boolean aggregate function to apply to the specified field in the table's data. You must also specify an alias for each boolean aggregate function. Specify each boolean aggregate function as a separate row. Click to add a new row.

Applicable only to the Snowflake database.

This field set contains the following fields:

Boolean Aggregate Function
Field
Alias Name

Boolean Aggregate Function

String/Suggestion

Enter the name of the boolean aggregate function to apply to the table data. Alternatively, click the button to view and select a boolean aggregate function from the list of supported bitwise aggregate functions.

Available options are:

The Snap fails during Pipeline validation/execution if you specify an incorrect function name.

N/A

BOOLOR_AGG

Field

String/Expression

Enter the name of the field in the input table upon which to apply the boolean aggregate function.

N/A

RANGE

Alias Name

String

Enter the alias name to associate with the specified boolean aggregate function.

N/A

BOOLOR_RANGE

Value Based Window Functions List

Use this field set to specify the columns for which to apply value-based Window functions. Each Window function must be specified as a new row.
Click to add a row.

This field set contains the following fields:

Value Window Function
Field
nth Value
Alias Name

Value Window Function

String/Suggestion

Required. Enter the name of the window function to apply to the table data. You must ensure that the function is spelled correctly, else the Snap displays an error. See the Troubleshooting section for details.

Alternatively, click to view and select a function from the list of window functions. The list displays the functions supported by the database that you select in the Account settings.

The available functions are:

Snowflake

Redshift

Azure Synapse

FIRST_VALUE_IGNORE_NULLS
FIRST_VALUE_RESPECT_NULLS
LAST_VALUE_IGNORE_NULLS
LAST_VALUE_RESPECT_NULLS
NTH_VALUE_FROM_FIRST_IGNORE_NULLS
NTH_VALUE_FROM_FIRST_RESPECT_NULLS
NTH_VALUE_FROM_LAST_IGNORE_NULLS
NTH_VALUE_FROM_LAST_RESPECT_NULLS

See Snowflake Window functions for more information.

FIRST_VALUE_IGNORE_NULLS
FIRST_VALUE_RESPECT_NULLS
LAST_VALUE_IGNORE_NULLS
LAST_VALUE_RESPECT_NULLS
NTH_VALUE_FROM_FIRST_IGNORE_NULLS
NTH_VALUE_FROM_FIRST_RESPECT_NULLS
NTH_VALUE_FROM_LAST_IGNORE_NULLS
NTH_VALUE_FROM_LAST_RESPECT_NULLS

See Redshift Window functions for more information.

FIRST_VALUE_RESPECT_NULLS
LAST_VALUE_RESPECT_NULLS

See Azure Synapse Window functions for more information.

Databricks Lakehouse Platform

BigQuery

FIRST_VALUE_IGNORE_NULLS
FIRST_VALUE_RESPECT_NULLS
LAST_VALUE_IGNORE_NULLS
LAST_VALUE_RESPECT_NULLS

See Azure Databricks Window Functions for more information.

FIRST_VALUE_IGNORE_NULLS
FIRST_VALUE_RESPECT_NULLS
LAST_VALUE_IGNORE_NULLS
LAST_VALUE_RESPECT_NULLS
NTH_VALUE_IGNORE_NULLS
NTH_VALUE_RESPECT_NULLS

See BigQuery Navigation Functions for more information.

Redshift does not natively support the NTH_VALUE_FROM_LAST_RESPECT_NULL and NTH_VALUE_FROM_LAST_IGNORE_NULL functions. But, the ELT Window Functions Snap supports them through special rewrites (using internal SQL rewrites) on Redshift.

N/A

FIRST_VALUE_IGNORE_NULLS

Field

String/Expression

Required. Apply the window function to the target column.

N/A

QTY

nth Value

String/Expression

Enter a number for the nth value function.

This field is ignored by the FIRST_VALUE and the LAST_VALUE functions during the execution.

N/A

Alias Name

String

Required. Specify the column alias name in which to display the result of the aggregate function. You can also reference this name in downstream Snaps to process the data further.

N/A

LEAD and LAG Window Functions List

Use this field set to add LEAD and LAG Window functions. Specify each function as a new row.

Click to add a row.

This field set contains the following fields:

Lead Lag Window Function
Field
Offset Value
Default Value
Alias Name

Lead Lag Window Function

String/Suggestion

Required. Enter the name of the Lead Lag function to use. You must ensure that it is spelled correctly, else the Snap displays an error. See the Troubleshooting section for details.
Alternatively, click to view and select a function from the list of window functions. The list displays the functions supported by the database that you select in the Account settings. The available functions are:

Snowflake

Redshift

Azure Synapse

LEAD_IGNORE_NULLS
LEAD_RESPECT_NULLS
LAG_IGNORE_NULLS
LAG_RESPECT_NULLS

See Snowflake Lead and Lag Window Functions for more information.

LEAD_IGNORE_NULLS
LEAD_RESPECT_NULLS
LAG_IGNORE_NULLS
LAG_RESPECT_NULLS

See Redshift Lead and Lag Window Functions for more information.

LEAD_RESPECT_NULLS
LAG_RESPECT_NULLS

See Azure Synapse Lead and Lag Window Functions for more information.

Databricks Lakehouse Platform

BigQuery

LEAD_RESPECT_NULLS
LAG_RESPECT_NULLS

See Azure Databricks Lead and Lag window functions for more information.

LEAD_RESPECT_NULLS
LAG_RESPECT_NULLS

See BigQuery Lead and Lag Window Functions for more information.

Redshift does not natively support the following default value expressions in the LEAD and LAG syntax which is supported by Snowflake and Azure Synapse. But, the ELT Window Functions Snap supports these functions through special rewrites:

COALESCE ([LEAD Expression], [Default Value Expression])
COALESCE ([LAG Expression], [Default Value Expression])

N/A

LEAD_IGNORE_NULLS

Field

String/Expression

Required. Apply the Lead Lag Window function to the target column.

N/A

QTY

Offset Value

String/Expression

Enter the number the LEAD or LAG function uses to fetch the leading or lagging value from the input table partition for the given input field.

N/A

Default Value

String/Expression

Enter a SQL expression the LEAD or LAG functions use when the offset goes out of the bounds of the window.

N/A

Alias Name

String

Specify the column alias name in which to display the result of the aggregate function. You can also reference this name in downstream Snaps to process the data further.

N/A

lead_rn

Linear Regression Related Functions List

Use this field set to add Linear Regression Related Window functions. Specify each function as a new row.

Click to add a row.

This field set contains the following fields:

Window Function
First Field
Second Field
Alias Name

Window function

String/Suggestion

Required. Enter the name of the Linear Regression function to use. You must ensure that it is spelled correctly, else the Snap displays an error. See the Troubleshooting section for details.
Alternatively, click to view and select a function from the list of window functions. The list displays the functions supported by the database that you select in the Account settings. The available functions are:

CORR
COVAR_POP
COVAR_SAMP
REGR_AVGX
REGR_AVGY
REGR_COUNT
REGR_INTERCEPT
REGR_R2
REGR_SLOPE
REGR_SXX
REGR_SXY
REGR_SYY

See Snowflake Window Functions for more information.

Linear Regression Window Functions in Redshift, Azure Synapse, Databricks Lakehouse Platform, and BigQuery

These databases do not natively support the window functions listed above. But, the ELT Window Functions Snap supports all of these functions on Redshift, Azure Synapse, Databricks Lakehouse Platform, and BigQuery databases through special rewrites.

Accuracy in Output Values

The Scale and Precision that the different databases (Snowflake/Redshift/Azure Synapse) allow for calculation of the values has a direct bearing on the accuracy of the Window Function values that the Snap returns in the output.

N/A

REGR_AVGY

First Field

String/Expression

Required. Specify the first field (argument) for the selected function.

N/A

QTY

Second Field

String/Expression

Required. Specify the second field (argument) for the selected function.

N/A

Alias Name

String

Required. Specify the column alias name in which to display the result of the Window function. You can also reference this name in downstream Snaps to process the data further.

N/A

WinFunc_RegrValue

Aggregate Concatenation Functions List (Not valid for Azure Synapse, Databricks Lakehouse Platform)

This field set enables you to specify the list of aggregate concatenation functions to be used for aggregation. The list displays the functions supported by the database that you select in the Account settings. Each function must be specified as a new row. Click to add a row.

This field set contains the following fields:

Aggregate Concatenation Function
Field Name
Alias Name
Delimiter

Aggregate Concatenation Function

String/Expression/Suggestion

Required. Select the aggregate concatenation function to use. Click to retrieve the list of the supported functions. The list displays the functions supported by the database that you select in the Account settings. An error is displayed if an account is not configured. See the Troubleshooting section for details.

Alternatively, you can also enter the name of the aggregate concatenation function to use; you must, however, ensure that it is spelled correctly. Otherwise, the Snap displays an error.

You can use the following aggregate functions:

Snowflake	Redshift	BigQuery
LISTAGG LISTAGG_DISTINCT ARRAY_AGG ARRAY_AGG_DISTINCT	LISTAGG DISTINCT_LISTAGG	ARRAY_AGG STRING_AGG

See Snowflake Aggregate Functions, Redshift SQL Functions Reference, Aggregate Functions (Transact-SQL), or BigQuery Aggregate Functions in Standard SQL for more information on the respective aggregate function.

N/A

LISTAGG

Field Name

String/Expression

Required. Specify the column name to apply the concatenation function.

N/A

NEW_LOCATIONS

Alias Name

String

Required. Specify the column in which to display the result of the concatenation function. You can also reference this name in downstream Snaps to process the data further.

N/A

UNIQUE_LOCS_LIST

Delimiter

String/Expression

Specify the delimiting character (string constant) to be used to separate the concatenated values.

, (comma)

; (semi-colon)

Percentile Distribution Functions List (Not valid for Azure Synapse, Databricks Lakehouse Platform)

This field set enables you to specify the list of percentile distribution functions to be used for aggregation. The list displays the functions supported by the database that you select in the Account settings. Each function must be specified as a new row. Click to add a row.

This field set contains the following fields:

Percentile Distribution Function
Percentile
Alias Name

Percentile Distribution Function

String/Expression/Suggestion

Required. Select the percentile distribution function to use. Click to retrieve the list of the supported functions. The list displays the functions supported by the database that you select in the Account settings. An error is displayed if an account is not configured. See the Troubleshooting section for details.

Alternatively, you can also enter the name of the percentile distribution function to use; you must, however, ensure that it is spelled correctly. Otherwise, the Snap displays an error.

You can use the following aggregate functions:

Snowflake	Redshift
PERCENTILE_DISC PERCENTILE_CONT	APPROXIMATE_PERCENTILE_DISC PERCENTILE_CONT
BigQuery
PERCENTILE_CONT_IGNORE_NULLS PERCENTILE_CONT_RESPECT_NULLS PERCENTILE_DISC_IGNORE_NULLS PERCENTILE_DISC_RESPECT_NULLS

See Snowflake Aggregate Functions, Redshift SQL Functions Reference, or BigQuery Navigation Functions for more information on the respective aggregate function.

N/A

PERCENTILE_DISC

Percentile

String/Expression

Required. Specify the percentile value to be considered for applying the percentile distribution function.

N/A

0.8 (80th percentile)

Alias Name

String

Required. Specify the column in which to display the result of the percentile distribution function. You can also reference this name in downstream Snaps to process the data further.

N/A

REVENUE_CPERCENTILE

PARTITION BY Fields List

Enter the fields in the table to use to partition the output data. Specify each field in a separate row. Click to add a new row.

This field set contains the following fields:

Partition By Field

You cannot apply the window framing clause, window ordering clause, or window partitioning clause to different functions in the same Snap. You must add another ELT Window Functions Snap and configure it with the target functions to do so.

Partition By Field

String/Expression

Enter the name of the field to partition the data with.

N/A

TOTAL_SALARY

Order By Fields List

Specify the field to use to sort the output data along with the sort order preference and the placement for rows with null values in the specified field. Specify each field in a separate row. Click to add a new row.

This field set contains the following fields:

Order By Field
Sort Order
Null Value Sort Preference

Order By Field

String

Enter the name of the field/column in the table based on which to sort the rows.

N/A

TOTAL_SALARY

Sort Order

String

Select the order in which to sort the rows.

Available options are:

ASC
DESC

ASC

DESC

Null Value Sort Preference

Drop-down list

Select whether to keep rows with null values first or last after the sort operation.

Available options are:

NULLS FIRST
NULLS LAST

NULLS FIRST

NULLS LAST

Window Frame

Use this field set to specify the range of the rows in the input table upon which to apply the window functions.

This field set contains the following fields:

Start of Window Frame
End of Window Frame

The window framing clause is not applicable to all the functions specified in the Snap configuration. It depends upon the functions and the database where the window framing clause is applied.
You cannot apply the window framing clause, window ordering clause, or window partitioning clause to different functions in the same Snap. You must add another ELT Window Functions Snap and configure it with the target functions to do so.

Start of Window Frame

Drop-down list

Select the policy/condition to indicate the first row in the window frame.

Available options are:

UNBOUNDED PRECEDING
BOUNDED VALUE PRECEDING. Activates the Bounded Start Value field.
CURRENT ROW
BOUNDED VALUE FOLLOWING. Activates the Bounded Start Value field.
UNBOUNDED VALUE FOLLOWING

UNBOUNDED PRECEDING

CURRENT ROW

Bounded Start Value

Integer

Activates when you select the BOUNDED VALUE PRECEDING or BOUNDED VALUE FOLLOWING option in the Start of Window Frame field.

Enter the row value from which to start the window frame.

N/A

End of Window Frame

Drop-down list

Select the policy/condition to indicate the last row in the window frame.

Available options are:

UNBOUNDED PRECEDING
BOUNDED VALUE PRECEDING. Activates the Bounded End Value field.
CURRENT ROW
BOUNDED VALUE FOLLOWING. Activates the Bounded End Value field.
UNBOUNDED VALUE FOLLOWING

UNBOUNDED FOLLOWING

BOUNDED VALUE FOLLOWING

Bounded End Value

Integer

Activates when you select the BOUNDED VALUE PRECEDING or BOUNDED VALUE FOLLOWING option in the End of Window Frame field.

Enter the row value at which to end the window frame.

N/A

Troubleshooting

Error	Reason	Resolution
Set an account for the Snap in the Accounts tab.	Account configuration is mandatory in the ELT Window Function Snap. An account has not been selected/configured in the Snap.	Select or configure an account for the Snap. See Configuring ELT Database Accounts for details.

Examples

Calculating Aggregate Data for an Organization

We want to calculate the aggregate scores such as, MIX, MAX, and MEDIAN and also want to apply bitwise and boolean aggregate functions to a table in the Snowflake database. This Pipeline shows how you can use the ELT Windows Function Snap to accomplish this task.

First, we use the ELT Select Snap to build a query to retrieve all records from the target table.

Upon execution, this Snap builds the query as shown below:

We want to perform the following functions on the above table:

Apply rank-related functions.
Calculate aggregate data for the columns RANGE and QTY.
Apply bitwise aggregate functions to the data in the RANGE and QTY columns.
Apply boolean aggregate functions to the data in the LOCAL column.
Partition the output by the data in the DEPT column.
Sort the output based on the data in the LOCAL in descending order and keep rows with null values in this column at the last.

To do so, we add the ELT Window Functions Snap and configure it as follows:

Upon successful execution, the ELT Window Functions Snap performs the specified calculations upon the input data and shows the following output:

We can add an ELT Insert-Select or ELT Merge Into Snap downstream of the ELT Window Function Snap and write the above output into another table.

Download this Pipeline.

Downloads

Important Steps to Successfully Reuse Pipelines

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

	File	Modified
Labels No labels Preview View	File ELTWindowFunctions_Example.slp	Oct 30, 2020 by Mohammed Iqbal

Snap Pack History

Click here to expand...

Release	Snap Pack Version	Date	Type	Updates
November 2024	main29029	13 Nov 2024	Stable	Updated and certified against the current SnapLogic Platform release.
August 2024	438patches28010	10 Sep 2024	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 2024	main27765	21 Aug 2024	Stable	Upgraded the jOOQ library for the ELT Snap Pack from v3.9.1 to v3.17.x.
May 2024	437patches27372	23 Jul 2024	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 2024	437patches27246	16 Jul 2024	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 2024	437patches26846	24 Jun 2024	Latest	Fixed 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 2024	main26341	08 May 2024	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 2024	436patches25953	12 Apr 2024	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: Load data from a canonical format Parquet file Load data from a non-canonical format Parquet file
February 2024	main25112	14 Feb 2024	Stable	Updated and certified against the current SnapLogic Platform release.
November 2023	435patches24461	11 Jan 2024	Latest	Fixed an issue with the ELT Merge Into Snap that caused inaccurate aliases and table identifiers in its generated SQL statement.
November 2023	435patches23671	08 Nov 2023	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 2023	main23721	08 Nov 2023	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 2023	main22460	16 Aug 2023	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 2023	N/A	31 May 2023	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 2023	main21015	10 May 2023	Stable	Upgraded with the latest SnapLogic Platform release.
February 2023	432patches20978	04 May 2023	Latest	Fixed 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 2023	main19844	09 Feb 2023	Stable	Upgraded with the latest SnapLogic Platform release.
November 2022	431patches19240	08 Dec 2022	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 2022	main18944	10 Nov 2022	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	28 Sep 2022	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: ELT Insert-Select ELT Load ELT Merge Into ELT SCD2 ELT Select 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 2022	main17386	11 Aug 2022	Stable	Upgraded with the latest SnapLogic Platform release.
4.29-Patch	429patches16665	17 Jun 2022	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	28 May 2022	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.
4.29	main15993	14 May 2022	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. ELT Load ELT Merge Into ELT Insert Select ELT SCD2 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-Patch	428patches15638	20 Apr 2022	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-Patch	428patches15290	24 Mar 2022	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.
4.28	main14627	12 Feb 2022	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. Enhanced all Snaps in the ELT Snap Pack with the ability to push down optimized SQL subqueries along the ELT Pipelines. See Subquery Pushdown Optimization (SPDO) for details. 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 Database Account to support these operations. 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	07 Jan 2022	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-Patch	427patches13539	13 Dec 2021	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	20 Nov 2021	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.
4.27	main12833	13 Nov 2021	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 Select, ELT Merge Into, ELT 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-Patch	426patches12534	28 Oct 2021	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: … > ELT Select (with one or more of these clauses used in the SQL Statement Editor) > ELT Transform > … … > ELT Select (with or without using the SQL Statement Editor) > ELT Filter > ELT Transform > …
4.26-Patch	426patches12021	30 Sep 2021	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-Patch	426patches11646	22 Sep 2021	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-Patch	426patches11323	17 Aug 2021	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-Patch	426patches11262	16 Aug 2021	Latest	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`.
4.26	main11181	14 Aug 2021	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: Support LEFT ANTI JOIN and LEFT SEMI JOIN types on all supported databases. Display or hide the Resultant Column Names Prefix Type field based on the target database selected in the Snap's account. Enhanced the ELT Load and ELT SCD2 Snaps to provide a list of suggested data types, while adding columns to or creating a table.
4.25-Patch	425patches10017	17 Jun 2021	Latest	Updated the ELT SCD2 Snap to replace End date of historical row option in the Meaning field of Target Table SCD2 Fields field set with End Date of Current Row. Breaking change This may cause the existing Pipelines to fail as the End date of historical row option no longer exists. You need to make the following update in the ELT SCD2 Snap's settings across your Pipelines after upgrading your Snap Pack to this patch: Select End Date of Current Row from the Meaning drop-down list in the second entry (highlighted in the image). Fixed the issue with the ELT Insert-Select Snap containing an open output preview that fails to retrieve output preview data in case of Redshift and Azure Synapse databases, though the Pipeline runs work as expected. Fixed an issue where the ELT Execute Snap does not error out (Snap turns Green) even when running an SQL query to drop a non-existent table from a Snowflake or Azure Synapse database. [Update on 22 Jul 2021]: Enhanced the ELT Snap previews to support the following 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 previews.
4.25-Patch	425patches9725	19 May 2021	Latest	Enhanced the ELT Snap preview to display the exact binary and varbinary values from Snowflake database during Pipeline validation, by converting the values to hexadecimal equivalents—the default setting in Snowflake. If the setting is different from hexadecimal in the Snowflake table, then the Snaps still convert the values to hexadecimal for rendering the Snap preview. Enhanced the ELT Transform Snap to display the appropriate data type (binary or varbinary) for the column names populated in the output schema. Enhanced the ELT Window Functions Snap to address potential issues due to an incorrect definition for MINUS function in case of Redshift and Azure Synapse databases.
4.25	main9554	08 May 2021	Stable	Starting with the 4.25 release, SnapLogic has now certified the ELT Snap Pack to work with Snowflake hosted on Google Cloud Platform (GCP) as the target database, in addition to the other flavors of Snowflake hosted on AWS and Microsoft Azure. Introduced the ELT Execute Snap to enable you to run DML, DDL, and DCL SQL queries in Snowflake in Snowflake, Redshift, and Azure Synapse. Introduced the ELT SCD2 Snap to support Type 2 Slowly Changing Dimensions (SCD2) updates to the target databases—Snowflake, Redshift, and Azure Synapse. Enhanced the ELT Database Account to introduce: Support for Google Cloud Storage as a storage location (source) in addition to AWS S3 and Azure Data Lake Storage (ADLS) when your target database is Snowflake. Automatic download of the JDBC driver required for the selected Database Type using the new Download JDBC Driver Automatically check box. Enhanced the ELT Load Snap to prevent changes to existing tables during Pipeline validation. If you set the Load Action as Drop and Create table, and the target table does not exist, the Snap creates a new (empty) target table based on the schema specified in its settings. Enhanced the ELT Window Functions Snap to support Covariance, Correlation, and Linear Regression Functions on Snowflake, Redshift, and Azure Synapse databases. The Snap uses function-specific query re-writes to support these functions on Redshift and Azure Synapse databases. Enhanced the ELT Merge Into and ELT Insert Select Snaps to support up to one output view, and added the Get Preview Data check box to these Snaps. You can now connect downstream ELT Snaps to these Snaps.
4.24-Patch	424patches8793	04 Mar 2021	Latest	Fixes the issue of production job failures due to ELT Insert Select Snap after upgrading to 4.24 GA by updating the ELT Transform Snap to continue allowing duplication of fields in the Expression list for the Pipeline to complete successfully. No changes are needed to your existing Pipelines. Fixes the column name collision issue in the Snap's output when the two tables being joined have columns with the same/identical names. You can specify the extent of prefix (that is, to prefix all columns, only duplicate columns, or no prefix) using the Resultant Column Names Prefix Type drop-down list. Based on the prefix you choose, a table alias name is prefixed to the identical columns in the output. Behavior Change The behavior of ELT Load Snap for Load Action during Pipeline validation across the supported databases is as follows: Append rows to existing table: Does not append the data from the source files into the target table. Overwrite existing table: Does not overwrite the data. Drop and Create table: Does not drop the target table even if it exists, but the Snap creates a new target table if a table does not exist. Alter table: Does not modify the schema of the target table.
4.24	main8556	14 Nov 2020	Stable	Adds support for Azure Synapse database. You can now use the ELT Snap Pack to transform tables in the Snowflake, Redshift as well as Azure Synapse databases. Updates the Snap Pack with the following features: ELT Database Account: Enhances the ELT Database Account to support the Azure Synapse database. ELT Aggregate: Enhances the Snap to: Support Azure Synapse's T-SQL aggregate functions and the aggregate functions in Snowflake and Redshift databases. General Aggregate Function COUNT_IF in Snowflake. General Aggregate Functions in Snowflake. Linear Regression Aggregate Functions in Snowflake. Aggregate Concatenation Functions in Snowflake, Redshift, and Azure Synapse. Percentile Distribution Functions in Snowflake and Redshift. Suggest appropriate column names to select from, in the Snap fields. This applies to Snowflake, Redshift, and Azure Synapse databases. ELT Insert Select: Enhances the Snap to: Suggest appropriate column names to select from, in the Snap fields. Create Hash-distributed tables using the Target Table Hash Distribution Column (Azure Synapse Only) field when the Load Action is selected as Drop and Create table and a condition like WHEN NOT MATCHED BY TARGET. ELT Join: Enhances the Snap to support Natural JOINS (NATURAL INNER JOIN, NATURAL LEFT OUTER JOIN, NATURAL RIGHT OUTER JOIN, and NATURAL FULL OUTER JOIN) in addition to the INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, and CROSS Joins in Azure Synapse Database. This enhancement also makes account configuration mandatory when using this Snap. Fixes the column name collision issue in the Snap's result set when the two tables being joined have columns with the same/identical names. You can specify the Resultant Column Names Prefix Type drop-down list. Based on the prefix type you choose, a table alias name is prefixed to identical columns in the output. ELT Load: Enhances the Snap to: Support the File Name Pattern option using Key Based Mechanism for Redshift database. Suggest appropriate column names to select from, in the Snap fields. This applies to Snowflake, Redshift, and Azure Synapse databases. Create Hash-distributed tables using the Target Table Hash Distribution Column (Azure Synapse Only) field when the Load Action is selected as Drop and Create table. ELT Merge Into: Enhances the Snap to: Suggest appropriate column names to select from, in the Snap fields. This applies to Snowflake, Redshift, and Azure Synapse databases. Include the Target Table Hash Distribution Column (Azure Synapse Only) field for the Snap to create hash-distributed tables always. Include the Update Expression List - When Not Matched By Source field set to allow defining one or more Update Expressions for the WHEN clause - WHEN NOT MATCHED BY SOURCE. This applies to Azure Synapse database. Include the Target Table Alias field to specify the alias name required for the target table. The Snap is also equipped with the ability to auto-replace the actual table names (with the alias name), if any, used in the ON clause condition, secondary AND conditions, Update Expression list, or Insert Expression list. This applies to Snowflake, Redshift, and Azure Synapse databases. ELT Transform: Enhances the Snap to: Display input schema and output schema based on the upstream and downstream Snaps connected to this Snap. Delete fields mentioned in the Expression field from the Snap's output when the mappings have an empty Target Path. ELT Window Functions: Enhances the Snap to support the following Window Functions in addition to the existing ones: Value Based Analytic Functions LEAD and LAG Analytic Functions Fixes the issue of displaying generic error messages for Triggered Task failures with ELT Pipelines by displaying detailed error messages for ease in debugging.
4.23	main7430	14 Nov 2020	Stable	Introduces the following Snaps: ELT Load: Loads data from AWS S3 buckets and Azure clusters into the Snowflake and Redshift tables. ELT Sample: Generates a data subset from the source table. ELT Pivot: Converts row data into column data. ELT Unpivot: Converts column data into row data. ELT Window Functions: Provides support for SQL Window Functions in ELT Pipelines.
4.22	main6403	12 Sep 2020	Stable	Introduces the ELT Snap Pack that provides you with the Extract, Load, and Transform (ELT) capabilities. Use the following Snaps to build SQL queries that are executed in the Snowflake database: ELT Aggregate : Builds SQL query to perform aggregate functions such as SUM, COUNT, MIN, and MAX. Also offers the GROUP BY functionality. ELT Copy: Creates copies of the input SQL query. ELT Filter: Adds a WHERE clause in the input SQL query. Use this capability to create filters/conditions for your data set. ELT Insert Select: Performs the INSERT INTO SELECT operation on the specified table. ELT Intersect: Adds an INTERSECT SQL operator in the input queries. ELT Join: Builds SQL query with a JOIN clause. ELT Limit: Adds a LIMIT clause in the incoming SQL query. ELT Merge Into: Performs the MERGE INTO operation on the specified table. ELT Minus: Adds a MINUS SQL operator in the input queries. ELT Select: Builds an SQL SELECT query and provides a built-in SQL query editor that enables you to construct complex queries. ELT Sort: Adds the ORDER BY keyword in the input query. ELT Transform: Builds transformation-based SQL queries for the specified table. ELT Union: Adds a UNION ALL or UNION DISTINCT operator in the input queries. ELT Unique: Builds a SELECT DISTINCT SQL query.

Overview

Prerequisites

Limitation

Known Issues

Snap Input and Output

Snap Settings

Troubleshooting

Examples

Calculating Aggregate Data for an Organization

Downloads

New Snap

See Also