What is ETL? This ETL tutorial will tell you everything you need to know about understanding and using ETL software.
Keyword(s): etl tutorial
In the tech world, acronyms are king. There’s CRM, ERP, IT, and countless others used to describe processes and software that keep data flowing and systems running.
Here are another three letters to add to the mix: ETL. This stands for Extract-Transform-Load. It is a data warehousing process in which data is loaded from a source system and transferred to a final target.
Today, we’re providing a brief ETL tutorial covering how the process works and the key steps involved.
Ready to get started? Let’s dig in!
1. ETL Tutorial Step 1: Extraction
The first step in the ETL process is removing or extracting, the source data from the system it’s currently housed within.
This is arguably the most important step, as it sets the stage for the other steps that follow. Clean and correct data extraction makes the transformation and loading processes much easier.
Extracted data can range from a few kilobytes to substantial gigabytes, so the frequency of extraction is important to note.
One reason the lot can be so large? With some systems, it’s difficult to discern from the onset exactly which data files have been extracted. As such, a large batch extract must be performed to remove all the files, which can then be cleaned and narrowed down later.
The main key with this step is to use a tool that allows you to perform it with as little disruption as possible to the source system.
It’s important to note that a data warehousing project can contain multiple types of source files. Some of the most widely-used formats include flat files, XML databases, and Information Management Systems (IMS).
During this first phase, the ETL software will convert the data stored in the various file types into a single format that can be transformed (Step 2).
When it’s pulled from its source system and before it can enter Step 2, the data is examined. The software is validating that it has the correct domain values. If an inconsistency is found, the data will often be rejected and sent back.
2. Step 2: Transformation
Before data can enter the data warehouse, it must be scrubbed clean. This means making sure it is free of unnecessary information and only includes quality, valuable insights.
Recently, this step has become even more important in the wake of Big Data.
In a nutshell, this is the overwhelming accumulation of data that is now possible thanks to the surge in digital connectivity. Now, your systems might be able to store a massive amount of data to be analyzed. Yet, chances are there are some data points that only serve to muddle the analysis. This is where cleaning and transformation come in.
Some of the most common data cleansing techniques include deduplicating records, or linking similar ones together to avoid repeats; validating address fields and setting naming standards.
Once data is clean, the software will apply any specific business rules designated for transformation. It will also review data integrity, and create aggregates as required to prepare for loading.
3. Step 3: Loading
Typically, the extracted and transformed data will enter into a data warehouse.
Companies will usually establish their own internal schedules for the frequency of ETL, opting to perform it daily, weekly, or monthly. Some ETL systems are designed to override existing extracted information with the most recent version, while others maintain a historical record of changes.
To facilitate the loading process, consider removing any integrity restraints currently in place in your data warehouse. After the data is loaded, be sure to set these controls back into place.
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