ETL Workflows: How to Optimize ETL Processes in Data Warehouse

ETL (Extract, Transform, Load) is a crucial process in data warehousing and analytics. It involves collecting data from diverse sources, organizing it frequently, and transferring it to the desired data repository. ETL optimization focuses on improving the efficiency and performance of the ETL process.  ETL optimization refers to the techniques and strategies used to improve the efficiency, performance, and scalability of the ETL process.

Optimizing the ETL process is crucial for organizations that deal with large volumes of data or have complex data integration requirements. The goal is to minimize the time and resources required for data extraction, transformation, and loading while ensuring the accuracy and quality of the data.

This post provides a comprehensive guide on optimizing ETL  processes, covering workflow, challenges, solutions, and benefits.

How to model ETL workflows?                                               

The workflow for ETL optimization is outlined as follows:

• Data Fetching: Raw data is fetched from the CRM using API calls, .Net, Python, and files from a shared drive. This data is typically stored in a staging area or temporary storage.
• Data Transformation: The ETL process had been completely handled with Pentaho Data Integration 9.1 tool and MS SQL Server 2016.
• Loading into SQL DB: The transformed data is loaded into the MS SQL Server 2016 database. This involves creating the necessary tables or updating existing ones. 
• Data Warehouse Creation: A created Data warehouse for primary and secondary data with an effective checklist at each pipeline which made data quality fully available with the BI tool.
• Data Replication: To reduce the traffic on API calls and improve performance, data replication is implemented between other servers.
• Business Insights with Power BI: Power BI is used as a business intelligence tool to deliver insights to end users. It provides interactive dashboards, visualizations, and reporting capabilities to analyze and present the data effectively.

Challenges

• Received duplicate data from CRM through API call.
• Lack of time delivery of data through the shared drive.
• Slow processing time for ETL.
• Lack of data at the time of the Power BI scheduled refresh.

Solution

• Received duplicate data from CRM through an API call

A stored procedure has been developed to eliminate redundant and duplicate records obtained from the CRM system via an API call. The procedure ensures that historical data is captured and maintains a set of cleaned tables, which serve as a reliable source for data warehousing purposes.

• Lack of timely delivery of files through the shared drive

To create a checklist for each runtime, utilize the last modification date and time. Additionally, implement an error handling mechanism to send notifications in case the file is not uploaded to the shared drive.

 

• Slow processing time for ETL

The ETL workflow and data dependencies were analyzed, to enable parallel execution. Indexes were created for tables used in SCD, resulting in a significant reduction of the runtime from 1 hour to just 5 minutes. Clustered and non-clustered indexes were implemented based on the lookup fields used for data capture.

• Lack of data at the time of the Power BI scheduled refresh.

Established a secondary data set specifically designed for utilization in the front-end BI tool. The workflow incorporates comprehensive error-handling procedures that validate the data at each stage of the pipeline, ensuring high data quality and reliability.

What Are The Benefits Of ETL Optimization?                                      

Optimizing the ETL (Extract, Transform, Load) process can bring several benefits to an organization. Here are some key advantages of ETL optimization:

Reduced Time-to-Insights

The ETL process can significantly decrease the time it takes for users to receive business insights. By reducing the data processing and transformation time from 3 hours to 30 minutes, decision-makers can access timely information, enabling them to make faster, data-driven decisions. 

Enhanced Data and Visualization Stability:

ETL optimization can improve the quality and reliability of the data being processed. This, in turn, leads to more reliable visualizations and reports, providing stakeholders with a solid foundation for making informed decisions.

 Improved Confidence in Data

With ETL optimization, you can implement data validation and data quality checks at various stages of the ETL pipeline. When the data is trusted, stakeholders can have greater faith in the insights derived from it, leading to more confident and accurate business decisions.

Scalability and Agility

ETL optimization can make the data pipeline more scalable and adaptable to changing business needs. By implementing efficient ETL workflows, organizations can handle larger volumes of data, accommodate new data sources, and adapt to evolving business requirements.

 Maximize your data potential: ETL optimization at its finest!        

Are you looking to maximize the potential of your data and optimize your ETL processes? Look no further! We offer ETL optimization services that will take your data management to new heights.

With our expertise in data engineering and ETL best practices, we can help you streamline your data pipelines, improve data quality, and enhance overall performance. Our team of experienced professionals will work closely with you to understand your specific requirements and tailor a solution that fits your unique needs.