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

Use this Snap to add aggregate functions such as COUNT, SUM, MIN, and MAX along with the GROUP BY clause in the incoming SQL query. GROUP BY clauses are used to group table records based on columns that contain classifying data and are optional in this Snap. This Snap also allows you to preview the result of the output query. You can validate the modified query using this preview functionality. The supported aggregate functions vary based on the account configuration. See the description of the Aggregate Function field in the Snap Settings section for details.

Prerequisites

None.

Limitations

Only those columns that are referenced in this Snap are passed to the output. The rest are dropped.

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

If you define a HAVING predicate in the HAVING Predicate List field set of the ELT Aggregate Snap (to apply on the Snap’s output data), the Snap connected downstream of this ELT Aggregate Snap returns an error corresponding to this predicate during the Pipeline execution.

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.

Due to an issue with Databricks Runtime Version 11 and above, the Snap fails to calculate the value for the linear regression aggregate function REGR_R2 for the target DLP instance and returns a cast exception. As a workaround, you can revert your Databricks Runtime Version to 10.5 or below.
When running without Sub-Query Pushdown Optimization (SPDO), ELT Pipelines that contain an ELT Aggregate Snap and configured with one or more GROUP BY ROLLUP fields, do not verify the column data types while inserting the Snap output values in the target table. This may lead to incorrect data written to the target table. However, as long as SPDO is on, the same Pipeline runs without this issue.

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 Transform	The SQL query in which to add the aggregate functions and the optional GROUP BY clause.
Output	Document	Min: 1 Max: 1	ELT Insert-Select ELT Limit	The modified SQL query with the aggregate functions and GROUP BY clause.

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 Aggregate

Aggregate Revenue

Get preview data

Checkbox

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

General Aggregate Functions List

This field set enables you to specify the columns for which to add the aggregate functions. Each aggregate function must be specified as a new row. Click to add a row.

This field set contains the following fields:

Function
Argument
Eliminate duplicates
Alias Name

Function*

String/Expression/Suggestion

Required. Select the aggregate function to use. Click to retrieve the list of the supported aggregate 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 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:

AVG
COUNT
COUNT_IF
MAX
MIN
SUM
SKEW
MODE
KURTOSIS

Additionally, the following aggregate functions are available based on the database type:

Snowflake	Redshift	Azure Synapse
ANY_VALUE BITAND_AGG BITOR_AGG BITXOR_AGG BOOLAND_AGG BOOLOR_AGG BOOLXOR_AGG MEDIAN STDDEV STDDEV_POP STDDEV_SAMP VAR_POP VAR_SAMP	APPROXIMATE.COUNT BIT_AND BIT_OR BOOL_AND BOOL_OR MEDIAN STDDEV_POP STDDEV_SAMP VAR_POP VAR_SAMP	APPROX_COUNT_DISTINCT COUNT_BIG GROUPING STDEV STDEVP VAR VARP
Databricks Lakehouse Platform		BigQuery
ANY BIT_OR BIT_XOR BOOL_AND BOOL_OR COLLECT_LIST COLLECT_SET EVERY FIRST_VALUE_RESPECT_NULLS FIRST_VALUE_IGNORE_NULLS LAST_VALUE_RESPECT_NULLS LAST_VALUE_IGNORE_NULLS SOME SKEWNESS STDDEV STDDEV_POP STDDEV_SAMP VAR_POP VAR_SAMP		ANY_VALUE BIT_AND BIT_OR BIT_XOR LOGICAL_AND LOGICAL_OR

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

N/A

AVG

Argument*

String/Expression

Required. Enter the field name or expression on which you want to apply the general aggregate function.

N/A

REVENUE

Eliminate duplicates

Check box

Select to apply DISTINCT to the column specified in the Field field. This means that the aggregate function is applied only to the unique values in the column.

Behavior with COUNT_IF aggregate function

Selecting this 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. However, it eliminates the duplicates from the list of records when used with a Redshift, Azure Synapse, or Databricks Lakehouse Platform (DLP) instance.

Not selected

Selected

Alias Name*

String

Required. Specify the column 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

TOTAL_REVENUE

Linear Regression Aggregate Functions List

This field set enables you to specify the columns for which to apply the linear regression aggregate functions. Each function must be specified as a new row. Click to add a row.

This field set contains the following fields:

Function
Argument 1
Argument 2
Alias Name

Function*

String/Expression/Suggestion

Required. Select the aggregate function to use. Click to retrieve the list of the supported linear regression aggregate functions. The list displays the functions supported by the database. An error is displayed if an account is not configured. See the Troubleshooting section for details.

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

You can use the following aggregate functions:

CORR
COVAR_POP
COVAR_SAMP
REGR_AVGX
REGR_AVGY
REGR_COUNT
REGR_INTERCEPT
REGR_R2
REGR_SLOPE
REGR_SXX
REGR_SXY
REGR_SYY
MINHASH (for Snowflake only)
OBJECT_AGG (for Snowflake only)

See Snowflake Aggregate Functions, Databricks Built-in Functions Reference, or BigQuery Aggregate Functions in Standard SQL for more information on the respective aggregate function.

Though not supported natively

BigQuery does not natively support the above list of Linear Regression Aggregate Functions. However, SnapLogic provides you with the ability to use these functions with BigQuery through a series of internal query rewrites.

N/A

CORR

Argument 1*

String/Expression

Required. Enter the first field name or expression on which you want to apply the Aggregate function.

N/A

REVENUE_LOC1

Argument 2*

String/Expression

Required. Enter the second field name or expression on which you want to apply the Aggregate function.

N/A

REVENUE_LOC2

Alias Name*

String

Required. Specify the column 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

LINREGAGG_REVENUE

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

This field set enables you to specify the list of aggregate concatenation functions 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

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	Azure Synapse
LISTAGG LISTAGG_DISTINCT ARRAY_AGG ARRAY_AGG_DISTINCT	LISTAGG DISTINCT_LISTAGG	STRING_AGG
BigQuery
ARRAY_AGG ARRAY_AGG_DISTINCT ARRAY_CONCAT_AGG STRING_AGG STRING_AGG_DISTINCT

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

Argument*

String/Expression

Required. Enter the field name or expression on which you want to apply the Aggregate 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, BigQuery)

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:

Function
Percentile
Alias Name

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