ELT Intersect

In this article


Use this Snap to add an INTERSECT SQL operator to the separate queries coming from upstream Snaps. This Snap also allows you to preview the result of the INTERSECT SQL operation on the incoming SQL queries. You can validate the modified query using this preview functionality.


The INTERSECT SQL operation does not eliminate duplicate records. You can add the ELT Unique Snap to the ELT Intersect Snap to remove duplicates. 





Known Issues

  • ELT Pipelines targeting a Databricks Lakehouse Platform (DLP) instance might fail due to a very long or complex SQL query that they build. As a workaround, you can set an advanced (URL) property useNativeQuery to 1 in your ELT Database Account configuration as shown below:
  • 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)


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


  • Min: 2
  • Max: No limit
  • ELT Select
  • ELT Aggregate
Multiple SQL queries.


  • Min: 1
  • Max: 1
  • ELT Merge Into
  • ELT Insert-Select
The incoming SQL queries joined with the INTERSECT operator. 

Snap Settings

Parameter NameData TypeDescriptionDefault ValueExample 
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 IntersectCommon Records
Get preview dataCheckbox

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 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 selectedSelected
Retain duplicates (Databricks Lakehouse Platform only)Checkbox

Select this checkbox (when your target database is Databricks Lakehouse Platform) to include duplicate records/values, if any, that match the intersection criteria in the output data set. Else, the Snap does not retain any existing duplicate entries in the output.

This setting is ignored in case of all other supported target databases/CDWs except Databricks Lakehouse Platform.

Not selectedSelected




Retrieving Common Records Between Two Tables

We need a query that combines two SELECT queries with an INTERSECT operator to retrieve common records between two tables. This example shows how we can use the ELT Intersect Snap to achieve this result.

First, we build SELECT queries to read the target tables. To do so, we can use two ELT Select Snaps, in this example: Read Part A and Read Part B. Each of these Snaps is configured to output a SELECT * query to read the target table in the database. Additionally, these Snaps are also configured to show a preview of the SELECT query's execution as shown:

Read Part A ConfigurationRead Part B Configuration

A preview of the outputs from the ELT Select Snaps is shown below:

Read Part A OutputRead Part B Output

Then, we connect the ELT Intersect Snap to the output view of the ELT Select Snaps. The SELECT * queries in both of these Snaps form the inputs for the ELT Intersect Snap. Upon execution, the ELT Intersect Snap combines both incoming SELECT * queries and adds the INTERSECT operator.

A preview of the ELT Intersect Snap's output is shown below:

We can add also an ELT Insert-Select Snap downstream and write the result of this query into another table.

Download this Pipeline.

Retaining Duplicate Values/Entries in Intersection Data (Databricks Lakehouse Platform)

In this example Pipeline which connects to a Databricks Lakehouse Platform database, we demonstrate how you can retain duplicate records in the common data that the ELT Intersect Snap produces in the output.

We use two ELT Select Snaps to capture data from the two different datasets that we want to extract common records from.

ELT Select Snap (Source 1)

Snap Output

ELT Select Snap (Source 2)

Snap Output

Let us connect the Snaps (that produce these two datasets) to the two input views of the ELT Intersect Snap. Notice that we have selected the Retain Duplicates (Databricks Lakehouse Platform only) check box to ensure that the Snap includes duplicate matching records in its output view.

ELT Intersect Snap

Snap Output

We can see that the output of this Snap contains exactly four matching records including a duplicate record ("FIRSTNAME": "Bruke", ...)

In the end, we write this list of common records between the two source data sets into a DLP table out_ma_dl_elt_intersect_02 by configuring the ELT Insert Select Snap as shown below:

Download this Pipeline.


Important Steps to Successfully Reuse Pipelines

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

  File Modified

File ELT_Intersect_Example.slp

Aug 20, 2020 by Mohammed Iqbal

File ELT_Intersect_FEP2_DLP.slp

Aug 18, 2021 by Anand Vedam

Snap Pack History

 Click here to expand...


Snap Pack Version 




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.


  • 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.
  • 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:
    • 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-Patch425patches10017 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 ]: 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-Patch425patches9725 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 SnowflakeIf 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.
4.25main9554 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-Patch424patches8793 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.

4.24main8556 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.
4.23main7430 StableIntroduces 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.





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. 

See Also