ELT Select

Owned by Anand Vedam
Last updated: May 09, 2024 by Gouri Bhagchandani

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 build SELECT SQL queries to fetch records from the specified table. Executed in its default state after providing database, schema, and table name, the Snap builds a standard SELECT * FROM query. However, you can use the inbuilt SQL query editor to build complex queries to perform complex operations spanning multiple tables such as JOIN, AGGREGATE, LIMIT, and DISTINCT. Additionally, this Snap can also connect to multiple upstream Snaps. This enables you to build a query to perform operations on multiple tables together or separately at the same time. 

Prerequisites

A valid SnapLogic account to connect to the database in which you want to execute the query if you also want to preview the data.

SnapLogic accounts are also required if you want to use the Snap's suggest feature in fetching schema and table names.

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 Issue

  • Suggestions displayed for the Schema Name field in this Snap is from all databases that the Snap account user can access, instead of the specific database selected in the Snap's account or Settings.

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

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

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

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

  • Inside SQL query editor (ELT Select and ELT Execute Snaps)

Workaround

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

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

Snap Input and Output


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

Document

  • Min: 0
  • Max: No limit
  • ELT Transform
  • ELT Copy
The database, schema, table names in which the query must be executed. You can also pass this information using an upstream ELT Snap. Additionally, you can also pass such information for several tables to the Snap using a separate input view for each table.
Output

Document

  • Min: 1
  • Max: 1
  • ELT Copy
  • ELT Intersect

A SELECT SQL query for the table specified in the Snap. If you use the SQL editor, then an SQL query built based on your inputs in the editor. Optionally, the output also includes a preview of the query's output if the Get preview data checkbox is selected.

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 NameData TypeDescriptionDefault ValueExample 
LabelString
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 SelectFetch Sales Data
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 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.

If you have selected this field, then you must ensure that the database name is also provided, either in the Snap's Database Name field or in the Account. Otherwise, the Snap's output contains only the SQL built using the Snap. This does not indicate that the SQL is correct. 

Not selectedSelected
Database NameString

The name of the database in which the target/source tables are located. Leave it blank to use the database name specified in the account settings.

You must ensure that the database name is correct. Otherwise, even though the Snap builds an SQL query, you will receive an error when executing the Snap since the query executes only in the target database.  

If your target database is Databricks Lakehouse Platform (DLP), you can, alternatively, mention the file format type for your table path in this field. For example, DELTA, CSV, JSON, ORC, AVRO. See Table Path Management for DLP section below to understand the Snap's behavior towards table paths.

N/A

TESTDB

DELTA

ORC

Schema Name (Not applicable to Databricks Lakehouse Platform)String

The name of the database schema. In case it is not defined, then the suggestion for the table name retrieves all tables names of all schema in the specified database (except for DLP) when you click .

  • Ensure that you include the exactly same schema name including the double quotes, if used, when you repeat the schema name in the Table Name field.
  • Leave this field blank if your target database is Databricks Lakehouse Platform.
N/APUBLIC
Table NameString

The name of the table or view for which you want to build the SQL queries. If it is not defined, then the Snap retrieves all the table names and views associated with the specified schema when you click  

  • Ensure that you include the exactly same schema name, if at all, including the double quotes as specified in the Schema Name field.

If your target database is Databricks Lakehouse Platform (DLP), you can, alternatively, include the path in this field. For example, `/mnt/elt/mytabletarget`. Learn more about the Snap’s behavior toward paths in the Table Path Management for DLP section.

When your target database is BigQuery, this Snap supports wildcard search in this field. 

Learn more on wildcard search in BigQuery: Querying wildcard tables.

N/A

PUBLIC.EMPLOYEES

`/mnt/elt/emp`

Enable SQL query editorCheckbox

Select this to enable the SQL query editor. You can use this editor to build all types of SQL queries that will execute on multiple tables. Enabling the SQL query editor activates the fieldset Input View to Virtual Table Map in the Snap.

  • To perform operations upon multiple tables, you must pass the table names through upstream Snaps. Specify each table name in a separate input view.
  • You must be careful when phrasing the query to prevent SQL injection. See Preventing SQL Injection for details. 
Not selectedSelected
Input View to Virtual Table Map

Activates when you select the Enable SQL query editor checkbox. This is useful when you enable multiple input views in the ELT Select Snap. Use this field set to map each input view to a dummy table. You will reference the table name specified in each input view with this dummy/virtual table name. 

  • Each input view corresponds to the SQL coming from the upstream Snap and that SQL is referenced by the dummy table name.
  • Virtual table name columns must not have duplicates.
  • One input view must not be associated with different table names. 

Click + to add rows. Each input view must be specified in a separate row.

This field set consists of the following fields:

  • Input View
  • Virtual Table Name
Input ViewStringSelect the input view whose table reference is to be mapped.N/Ainput2
Virtual Table NameStringSpecify the dummy/virtual table name that will be assigned to the table reference in the selected input view. N/Atbl1
SQL Query EditorQuery editor

Enter the SQL queries that you want to build using this Snap. Even though the Snap's name is ELT Select, you can use this SQL query editor to build any SQL query.

SQL Comments are allowed

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

-- comment text# comment text/* multi-line
comment text */		// comment text
N/ASELECT DISTINCT  FROM MYTABLE

Table Path Management for DLP

#

File Format Type
(Database Name field)

Table Path exists?#All other requirements
are valid?		Snap Operation Result
1DELTAYesYesSuccess
2DELTANoYesFailure. Snap displays error message.
3DELTAYesNoFailure. Snap displays error message.
4AVRO/CSV/JSON/ORC/otherYesYesSuccess.

# We recommend that you specify a table path that resolves to a valid data file. Create the required target file, if need be, before running your Pipeline.

Preventing SQL Injection

You can pass Pipeline parameters as values in an SQL query; however, if you do not phrase the query properly it can lead to the parameter's name being bound as a value in the database. This potentially incorrect information being inserted into the database is known as SQL injection. It is thus necessary to take precautions when including Pipeline parameters in your SQL query to prevent SQL injection. Based upon the intended use of the Pipeline parameter, use one or both the following methods to prevent accidental SQL injection:

Method-1: Simple Substitutions

You can reference the Pipeline parameter directly with a JSON-path without enabling expressions.

For example, consider a Pipeline parameter name which contains the value of a column. You want to write a SELECT query to fetch records from the table mytable where the column's name matches the value in the Pipeline parameter. 

SELECT * FROM mytable WHERE colname = _name

Method-2: Dynamic Substitutions

You must enable expressions when referencing table names using Pipeline parameters. Format the query, except the Pipeline parameter's reference, as a string. 

For example, if you you want to write a SELECT query to fetch records from a table and you are passing the table's name in the Pipeline parameter table:

"SELECT * FROM" + _table

In the example above, you must still use JSON-paths if you want to use another Pipeline parameter for substituting values in the SQL:

"SELECT * FROM " + _table + " WHERE colname = _name"

The Snap evaluates the expression and also carries out path substitutions.

Here is how it works

The Snap pre-processes the query to extract any JSON-Paths and converts them to bound parameters. For example, consider the following query:

SELECT * FROM mytable WHERE name = _name

The Snap converts this query into the following before turning it into a prepared statement for the database:

SELECT * FROM mytable WHERE name = ?

The Snap evaluates the JSON-Path to get the value to bind the Pipeline parameter in the prepared statement. 

Using escape characters

When expressions are disabled, use \ as an escape character to treat underscore (_) as a string.

For example:

SELECT \_2, \_3 FROM mytable WHERE colname = \_name 

Troubleshooting

ErrorReasonResolution

Database cannot be blank.

(when seeking the suggested list for Schema Name field)

Suggestions in the Schema Name and Target Table Name fields do not work when you have not specified a valid value for the Database Name field in this Snap.

Specify the target Database Name in this Snap to view and choose from a suggested list in the Schema Name and Target Table Name fields respectively.

SQL exception from Snowflake: Syntax error in one or more positions in the SQL query.
Column names in Snowflake tables are case-sensitive. It stores all columns in uppercase unless they are surrounded by quotes during the time of creation in which case, the exact case is preserved. See, Identifier Requirements — Snowflake Documentation.Ensure that you follow the same casing for the column table names across the Pipeline.

Database encountered an error during preview processing

(Target CDW: BigQuery)

This can happen when the target database cannot recognize/interpret the table name.Ensure that the table name is valid and the table with provided name exists. Using a wildcard in the SQL editor mode, ensure that the table name is enclosed between backticks (`). For example: `scd*`, `mode*`.

Examples

Retrieving a Fixed Number of Records from a Table

In a typical scenario, we use the SELECT command to retrieve records from a table. We can control how many records are retrieved by specifying a WHERE condition. However, if we want to limit the number of records retrieved from the table without any conditions, or if we want to retrieve a fixed number of records starting from a specific row, we must use the SELECT command with the LIMIT clause. This example shows how we can use the ELT Limit Snap to achieve this result.

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:

Then, we add the ELT Limit Snap and configure it as needed. In this example, we want to retrieve the next 5 records after the first record. So, we configure the ELT Limit Snap as shown below:

Based on this configuration, the ELT Limit Snap retrieves 5 (ELT Limit field's value) records starting from the second record (ELT Limit Offset field's value)

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

Download this Pipeline.

Downloads

Important Steps to Successfully Reuse Pipelines

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

  File Modified

File ELT_Select_Limit_Example.slp

Aug 20, 2020 by Mohammed Iqbal


Snap Pack History

 Click here to expand...

Release

Snap Pack Version 

Date

Type

Updates

November 2024main29029 StableUpdated and certified against the current SnapLogic Platform release.
August 2024

438patches28010

 Latest

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

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

August 2024main27765 Stable

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

May 2024437patches27372 Latest

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

May 2024437patches27246 Latest

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

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

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

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

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

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

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

February 2024436patches25953  Latest

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

February 2024main25112 StableUpdated and certified against the current SnapLogic Platform release.
November 2023435patches24461 LatestFixed an issue with the ELT Merge Into Snap that caused inaccurate aliases and table identifiers in its generated SQL statement.
November 2023435patches23671 Latest and Stable

  • Enhanced the ELT Merge Into Snap to support defining the update expressions for updating tables in your target Databricks Lakehouse Platform instance using the WHEN NOT MATCHED BY SOURCE clause, besides other supported clauses and expression lists.

November 2023main23721 Stable

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

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

August 2023main22460 Stable

  • Upgraded the JDBC driver support for Snowflake to snowflake-jdbc-3.13.33.jar.

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

May 2023N/A Stable

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

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

 

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

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

  • Records Added

  • Records Updated

  • Records Deleted

September 2022 430patches18196 Latest

New Snap

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

Enhancements

  • The ELT Insert-Select Snap is more flexible and easier to use, especially if the number of columns in your source data set is very large. You can choose to update values only in a subset of columns in the target table.

  • The ELT Execute Snap can retrieve and execute SQL queries from the upstream Snap's output when referenced in the SQL Statement Editor using the Expression language (with the Expression button enabled).

  • The ELT Load Snap can infer the schema from the source files in Amazon S3, ADLS Gen2, Microsoft Azure Blob Storage, or Google Cloud Storage location and use it to create, overwrite, and append the target table in your Snowflake instance with the source data. The source files can be in the AVRO, CSV, JSON, ORC, or PARQUET format. Learn more at Automatic Schema Inference with ELT Load Snap.

  • Target Table Name in the following Snaps supports retrieving editable views with the table names from the selected target schema:

  • The pivot values in the ELT PivotSnap turns dynamic when you select Enable dynamic pivot values. The following field settings are added as part of this dynamic pivot values feature:

    • Filter Predicate List: A field set to filter the predicate list of the pivot values.

      • Pivot Values Filter: Condition required to filter the pivot values.

      • Boolean Operator: Predicate condition type through AND or OR Boolean operators

    • Sort Order: Sorting order of the pivot values.

  • You can specify the type of Microsoft Azure external storage location (source)—an Azure Data Lake Gen2 or a Blob Storage—to access your source data using the Storage Integration type of authentication and load it to your target Snowflake instance.

August 2022main17386 StableUpgraded with the latest SnapLogic Platform release.
4.29-Patch429patches16665 Latest

  • Enhanced the ELT Snap Pack to support the latest JDBC drivers across CDWs—Azure Synapse, BigQuery, DLP, Redshift, and Snowflake. See Configuring ELT Database Accounts or the respective Account page for the exact versions.

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

  • Enhanced the ELT DLP Account to configure S3 Bucket, Azure Storage, and DataLake Storage Gen2 Mounts.

  • Enhanced the ELT Snowflake Account with support for Key Pair Authentication.

  • Enhanced the ELT SCD2 Snap:

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

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

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

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

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

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

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

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

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

4.29patches16287

 Latest

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

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

  • Enhanced the ELT Database Account to support OAuth2-based authentication on the target Snowflake database.

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

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

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

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

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

    • Current row 

    • Historical row

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

    • KURTOSIS

    • MODE

    • SKEW

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

    • Group by Cube

    • Group by Grouping Sets

    • Group by Rollup

    • Automatic GROUP BY for all input columns.

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

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

    • The Historical Row End Date value is provided.

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

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

4.28-Patch428patches15638 Latest

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

4.28-Patch428patches15290 Latest

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

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

    • Refer to the ELT SCD2 scenarios to learn more.

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

4.28main14627 Stable

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

  • Introduced the following new ELT Snaps:

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

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

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

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

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

  • Enhanced the ELT Aggregate Snap to support:

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

    • GROUP BY ROLLUP.

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

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

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

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

4.27-Patch

427patches13923

 Latest

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

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

4.27-Patch427patches13539 Latest

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

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

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

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

4.27-Patch

427patches13030

 Latest

  • Fixed the following issues with the ELT SCD2 Snap:

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

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

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

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

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

4.27main12833 Stable

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

  • Enhanced the ELT Database Account connecting to a Databricks Lakehouse Platform (DLP) to support two new options - Optimize Write and Auto Compact for creating/replacing a table using any of the ELT Insert 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-Patch426patches12534 Latest

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

4.26-Patch426patches12021 Latest

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

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

Latest

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

    • Intermittent null-pointer exceptions in the ELT Load Snap on Databricks Lakehouse Platform (DLP).

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

      • The query contains a LIMIT clause on a Snowflake, Redshift or Databricks Lakehouse Platform target instance: The SQL query created during Pipeline validation includes an additional LIMIT clause, for example: SELECT * FROM "STORE_DATA"."ORDERS" LIMIT 10 LIMIT 990. 

      • The query contains an OFFSET clause (supported in case of Snowflake and Redshift): The SQL query created during Pipeline validation looks like SELECT * FROM "STORE_DATA"."ORDERS" LIMIT 10 OFFSET 4 LIMIT 990. 
4.26main11181 Stable

  • Enhanced the ELT Snap preview to support the following Snowflake data types: array, object, variant, and timestamp.

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

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

  • Enhanced all ELT Snaps to display the Get preview data checkbox below the Snap's Label field.

  • The ELT Database account is now mandatory for all Snaps in the ELT Snap Pack.

    Breaking change

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

  • Enhanced the ELT Aggregate Snap to support Linear Regression functions on Redshift and Azure Synapse. The Snap also supports these functions on Databricks Lakehouse Platform.
  • Enhanced the ELT Execute Snap to enable running multiple DML, DDL, and DCL SQL statements from the same Snap instance.
  • Enhanced the ELT Join Snap to:
    • 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 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.
4.25main9554 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.

4.22

main6403

 

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. 

See Also

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