The Anatomy of an Effective ETL Process

Emma Kessinger

April 13th , 2020

You know the value of ETL. You know you’re ready to invest in it. But you may not know how the rubber meets the road: What does an ETL process look like, and what are the key things to think about when building one?

ETL’s acronym provides the thousand-foot process view: For a program to extract, transform, and load your data; it needs to know what data to ingest, what format that data should be in, and where to store the converted information. 

But while that general process applies to all ETL services and data types, there’s more to the story. 

Key Parts of the ETL Process

To best understand the ETL Process, it’s important to take it step-by-step, starting with:


The first part of ETL is the “E”: extraction. Simply put, extraction is about getting your data from disparate sources — Hubspot, Facebook, Google, Five9 — and moving it towards one location. 

While it may sound simple, large-scale extraction is very difficult to do without the right tool. Different sources may format their data in different ways or using different structures. ETL platforms can get data in any number of formats without a hitch, making your life much easier in the process. 

Since APIs can vary across the internet, you need an ETL tool that can deal with large numbers of different authentication methods and data architectures. An ETL platform should be able to get your data out of where it’s currently stored, no matter the protocol. 


Once you have your data extracted, it needs to be transformed into a common, functional format. Transformation typically involves the following steps: 


  • Naming: Data needs to be identified in order to be understood. Extracted data needs to be clearly renamed so that it can be parsed properly for further use down the line.


  • Cleaning: Depending on the data’s format and source, it can be full of unwanted characters and inconsistent timing formats. Cleaning the data involves making sure your data is uniform and usable.


  • Deduplicating: Deduplicating cuts down on any copies of your data that might have been made before or during the extraction process. It prevents the double counting of important facts and figures.


  • Structuring: Getting data into a consistent structure is the most technically-intensive part of ETL, requiring extensive programming in order to happen properly. Once your data is properly structured, it’s ready to be moved to its final location.


Loading is the process of moving data into your data warehouse where it can be examined and used for various purposes. Once your data is loaded, the ETL process is over. 

A data warehouse is a central repository of structured data from one or more sources. Generally, large amounts of data collected over time is kept in a data warehouse. The purpose of a data warehouse is to provide info so you can make informed decisions based on actual data and trends.  Data warehouses are incredibly useful as they can be mined using business intelligence tools where data from multiple sources can be compared, aggregated, sliced-and-diced, pivoted and charted.

ETL is at the core of getting your data to work for you by getting it into your data warehouse, but that doesn’t mean it requires no effort on your part — ETL processes often require programmers and expensive tools. Thankfully, ETLrobot can manage and automate most of the difficult parts of the process for you. If you want data to be the core of your competitive advantage, your ETL process needs to reflect that. Check out ETLrobot for an immediate and affordable solution to your data problems.


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