In this Article
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In a real business scenario, once the customers received receive their placed order, they have an option to return the order in the retail chain. This use case demonstrates how you can use SnapLogic ELT Snap Pack to analyze customer returns for a retail stores chain. This example demonstrates the process to find customers who have returned items worth 20% more often than the average customer returns for a store in a given state for a given year.
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To solve this big data problem, we have designed Pipelines that run on Snowflake and Microsoft Azure Databricks Lakehouse Platform
Example Pipelines -
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(SQL to SF)
Pipeline 1
This Pipeline does not require source tables as they are created on Snowflake on the fly from SQL.
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Output target tables are also created on Snowflake. The Pipeline writes from the Snowflake database to the Snowflake database. Users need not have Snowflake accounts. However, they require SQL experience.
You can download the Pipeline from here
Pipeline 2
The Pipeline writes from the Snowflake database to the Snowflake database. However, it requires source tables to be present in the Snowflake database to run the Pipeline.
An output target table is created on Snowflake. Users need not have AWS/Snowflake accounts or do not require SQL experience.
You can download the Pipeline from here
Pipeline 3
This Pipeline does not require source tables as they are created on Snowflake on the fly using ELT Load snap and the output target tables are created on Snowflake. The Pipeline writes from the Snowflake database to the Snowflake database. The Pipeline converts data from CSV to database tables and can be used for a wide variety of complex tasks. It requires table schema setup and AWS/Azure account is required. Users need not require SQL experience for this Pipeline.
You can download the Pipeline from here
Example Pipelines - Microsoft Azure Databricks Lakehouse Platform
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This Pipeline does not require source tables or raw data as they are created on Microsoft Azure Databricks Lakehouse Platform on the fly from SQL. An output target tables are also created on Microsoft Azure Databricks Lakehouse Platform. The Pipeline writes from the Microsoft Azure Databricks Lakehouse Platform database to the Microsoft Azure Databricks Lakehouse Platform. However, they require SQL experience. It can be used only for ELT demo or simple tasks.
You can download the Pipeline from here
Pipeline 5
The Pipeline writes from the Microsoft Azure Databricks Lakehouse Platform to the Microsoft Azure Databricks Lakehouse Platform. However, it requires source tables to be present in the Microsoft Azure Databricks Lakehouse Platform before execution. An output target table is created on Microsoft Azure Databricks Lakehouse Platform. Users do not require SQL experience. The Pipeline can be used for a wide variety of complex tasks.
You can download the Pipeline from here