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Extract, Transform, Load (ETL): Preparing Data for Analysis [2024]

Cem Dilmegani
Updated on Jan 11
5 min read
Extract, Transform, Load (ETL): Preparing Data for Analysis [2024]Extract, Transform, Load (ETL): Preparing Data for Analysis [2024]

ETL is one key processes needed to extract value out of data. Data can then be used for a wide range of analysis, intelligence, and reporting functions. For industries that manage large amounts of data, often from disparate sources, ETL can be impactful.

What Exactly is ETL?

ETL stands for Extract, Transform, and Load, and refers to the process of transferring data from one location to another. In addition to migrating data from one database to another, it also converts (transforms) databases into a single format that can be utilized in the final destination. These steps are:

  • Extract: Collecting data from a database. At this point, the data is often from multiple and different types of sources
  • Transform: Converting recently extracted data into the correct form so that it can be placed into another database.This process is crucial to ensuring data from one database or application can be utilized by other applications and databases. Some key functions here include:
    • Standardization to a consistent set of lookup values
    • Cleansing through validity checks to remove or modify problematic data
    • Transposing, usually via denormalizing and reorganizing into a dimensional model to optimize reporting
    • Creating surrogate keys that are new values applied to similar data from different source systems
  • Load: When the data is written into the target database or data warehouse
Image source: Webopedia

This process is integral to business intelligence because it means that data from multiple sources can be brought together in a manner that provides important insights; regardless of the original format or location. To be successful in this endeavor, it is key to begin with data mapping; where the relationship between the source establishes instructions for how data is to be transformed prior to arriving in the designated location.

ETL has been around for many years, but gained prominence in the 1970’s because of a growing need to store different varieties of business information. With the arrival of data warehouses in the 1980’s and 1990’s, the popularity of ETL tools exploded, leading many businesses to have multiple, disparate ETL solutions. Since then, the necessary technologies have evolved tremendously and currently give us levels of insight and intelligence not previously thought possible.  

How Can ETL be Automated?

IT teams leverage workload automation tools and job schedulers to automate the triggering and execution of ETL tasks in order to minimize human intervention and errors. These tools enable:

  • automation of data updates at certain times or triggers
  • data loading from one platform to another via a centralized point
  • creating a log for ETL audit and compliance

What are Some of the Benefits of ETL?

Today, we have become accustomed to having data available immediately thanks to streaming analytics. However, ETL offers a deeper historical context that enables businesses to make better decisions without incomplete information. Some additional benefits include:

  • The ability to digest large amounts of data from multiple sources, particularly in bulk when there and when there are complex rules and transformations
  • Improved productivity of data professionals because of reusing and codifying the processes necessary to move data; but without the need to write more code for every task
  • Improved accuracy for more effective reporting and auditing
  • Operational resilience owing to built-in error handling functionality in tools
  • A basis for emerging integration requirements for streaming data and similar
  • Clearer visual flow and self documentation because tools are based on Graphical User Interface (GUI)
  • High ROI owing to cost savings, with some studies demonstrating a 5 year ROI of 112% when paired with the correct data warehouse solution

When do I Need ETL?

If you have a need for a data warehouse that will combine data from various sources, you’ll need a way to get that data into the warehouse. This data can give you a consolidated view of many different processes and ultimately drives better decision making. Generally speaking, there are a few cases where ETL will be particularly useful:

  • When there are multiple source systems that need to be integrated
  • When these source systems are in different formats
  • When the process must be repeated, be it daily, hourly or at another specified frequency
  • To get the most from pre-built warehouses

Some practical examples include:

  • You have a clothing brand that is distributed by several different retailers with several different platforms and systems. All the data arrives in different formats and is then made consistent and usable for BI tasks via an ETL tool.
  • You are a global logistics company that processes hundreds of thousands of shipments with data from almost every source imaginable; making clear visibility almost impossible. An ETL solution eliminates redundancy and inconsistencies in the data; leading to improved data quality.
  • You are a major financial services provider that is transferring to an ERP (enterprise resource planning) solution because of the overhead associated with the existing arrangement. But all of your data existing data needs to be successfully migrated to the new system – regardless of where or how it is starting. ETL tools can clean and warehouse the data more efficiently than any other method.

Designing and Implementing an ETL Solution

You’ve decided that an ETL tool will be key for the success of your data efforts. There are a few things to keep in mind to help you in your implementation.

Starting with Source Data

Before anything else, be sure that you understand the different sources and types of data so that you can better determine your requirements. Your analysis should further help you to understand the volume of data your solution will need to manage.

Map each column source and destination by type as this will help you to find any complex tasks and start creating the right solution. Consider using a staging table at this point prior to the actual table.

Cleaning it Up

There are often issues within the source files; sometimes ones that are difficult to find the cause of. Data validation can help you to identify issues and move them to a separate location to be fixed. If it proves to be an ongoing issue, contact the partner associated with the source to fix the issue. For known issues, consider integrating an autorrect task.

Validation and Testing

To ensure your final solution is effective and meeting all requirements, create multiple test cases and apply them. Keep the test cases consistent and run them periodically with new sources to find any necessary updates.

Other testing and validation efforts should include:

  • Confirming any business logic prior to loading into a table or file
  • Large volume data testing to eliminate performance issues
  • Negative scenario test cases to validate the process
  • Email configuration to make sure that the right users get the right emails

Solution Optimization

Once you have achieved basic functionality, you can improve your process in terms of performance and memory consumption. Additional testing of different environments and different sizes of data can help you to pinpoint opportunities for improvement, but regardless of these factors, work to make the runtime of each ETL step as short as possible.

Additionally, be sure that you have the right hardware to support your needs and that you are using parallel process whenever possible. Periodically check how long each task takes and compare them to previous times to better find ways to improve. You can also speed up your processing by dropping indexes while loading to be recreated after.

Logging and Alerting for Better Error Handling

Errors can happen even in the best laid plans and having an effective logging and alert system can minimize any related disruption. Logs should include information about execution time, success or failure, and if needed, an error description. The error description should include the project name, task name, a description, and error number. These steps will help with error analysis.

Ignore, but continue to log, errors that don’t have an impact on business logic. If the error does include business logic, fix the issue before continuing. Your alert mechanism will be integral for this, so be sure to have the right settings for the designated support team.

In the case of unexpected failure, have point of failure recovery enabled so that processes can be restarted from where the failure occurred.  

Auditing, Monitoring, and Scheduling

To ensure that all ETL jobs are complete, they must be scheduled, monitored, and audited. Consider scheduling any ETL processes during non-business hours and be sure to know ahead of time who will receive any failure messages in these hours. 

Today, ETL capacity is often found in a wide range of data integration products, including standalone software, built in tools in database servers, or as parts of EAI middleware. They now also include visual mapping tools and drag-and-drop capabilities to help simplify the and expedite the process.

We also have a marketplace that includes a variety of solution providers:

Find the Right Vendors

Header image source: SAS

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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