What is ETL: Full Guide to Extraction, Transformation, and Loading

Everyone's heard some form of the age-old adage, "Information is power." Today, managing data is what gives organizations huge advantages over their competitors. Collecting, cleaning, and delivering data is crucial for reaching correct conclusions and improving operations and strategy.

This is where Extract, Transform, and Load (ETL) throws itself into play. ETL is the process of pulling data from many sources and managing it for quick and accurate analysis. This may sound like a simple pipeline, but companies deal with thousands of different data sources, and figuring out how and where to apply their findings is a massive order.

This post will explore the ETL process, its key components, implementation methods, and the challenges it brings to organizations.

Understanding the ETL Process

ETL is a fundamental process in many organization's data processes. There are countless ways to configure and customize ETL to fit your needs, but it always consists of three main stages: Extraction, Transformation, and Loading.

Extraction

This is the first step of the ETL process, in which data is collected from various places. The extraction stage may pull from sources such as databases, applications, APIs, spreadsheets, and much more. The goal is to efficiently pull all of the raw data while maintaining its integrity.

Transformation

Transformation is also known as data cleaning. This step restructures data in ways that make it easier for programs or humans to compare against each other.

For example, data using different measurements, such as miles vs. kilometers, would be transformed to use the same measuring systems. Other examples include normalizing abbreviations, rounding decimals, and merging similar categories.

Besides making data easier to compare, the cleansing process also aims to remove problematic inputs that would harm the result. So, engineers may look for factors like repetitive data points or extreme outliers. All of this is meant to push data into useful forms rather than a nonsensical jumble of characters.

Loading

The loading phase takes the transformed data and puts it into a final destination like databases or data warehouses. Putting everything in a centralized location prepares the data for comparative analysis or allows for more informed decisions with larger datasets. Loading is primarily performed in two ways, depending on the type of pipeline an organization prefers: real-time or batch.

Batch loads are scheduled loads that occur every hour, day, or week. They are less resource-heavy but come with the downside of not allowing the organization to make off-the-cuff decisions. Real-time loads are the opposite. They require more maintenance but help look at data as it comes in and use it to make expedient strategy changes.

Key Components: ETL Tools and Software

Integrating an ETL process into your data management infrastructure requires careful setup and administration.

The sheer deluge of information businesses work with basically requires automated tools. It's simply too much for humans to handle. The best tools provide user-friendly interfaces to automate the creation and management of ETL workflows. Popular choices include the Apache Nifi Suite, Talend, Stitch Data, and Google Cloud Data Fusion.

You'll notice that all of these tools are designed for cloud-based ETL processes. This is because traditional ETL data warehouses don't hold a candle to their cloud-powered brethren's processing and analytical power.

Making the most out of these tools still requires considerable experience and coding skills. However, these tools will likely become increasingly user-friendly and automated as technology advances.

What is ELT?

Modern ETL, often dubbed ELT, is a response to the mass movement of data to cloud-based solutions. It's a modernization of ETL, which sometimes leads to slower processing or harder-to-handle data.

The problem with traditional ETL is that the cloud service doesn't handle the transformation process. Thus, it doesn't know the optimal way to clean the data.

By holding off on transforming the data until it's loaded into the end destination, the data can be transformed into a result that best suits the final program's needs.

Challenges and Best Practices of ETL

While ETL is essential for managing data, it requires proper configuration and maintenance to continue smooth operations. Addressing these challenges with clean data habits is necessary to get appropriate insights and remain competitive in your field. Administrators new to ETL may experience the following issues:

• Low data quality
• Unmanageable volume
• Implementing security measures
• Slow processing speeds

While the answer may sometimes be to route more resources to ETL, finding ways to implement a few best practices is the most likely fix.

Decrease Raw Data

This may seem counterintuitive because more data allows for more accurate decision-making. However, a lot of raw data is wiped out during the transformation phase, so being able to remove it earlier in the process will speed up your data pipeline. If you notice data being repeatedly removed during the transformation phase, fix the source problem rather than cleaning it forever.

Perform More Frequent Batches

If you're struggling to process too much data at once, then you can lower the burden on your pipeline by speeding up how often you update. Switch to end-of-day data updates rather than weekly.

In the same vein, organizations should use incremental data updates. Full updates clog an ETL pipeline by adding all available data during updates, but incremental updates only include the data that appeared after the most recent load.

Integrate Parallel Processing

One of the beautiful things about computers is their ability to multitask. Their processing power far outstrips humans, allowing them to perform multiple integrations simultaneously, saving your organization resources. Not all infrastructures can support parallel processing, but it's worth consideration.

Keep Current with New Technologies While Staying Safe

ETL has become the bedrock of data integration and analysis over the past few years. It allows businesses to utilize the previously unmanageable number of data points from applications, services, and devices. Understanding the ETL process and how to maximize its efficiency is an invaluable skill to have on your team.

As data volumes and complexity grow, ETL is poised to remain a critical process for operations of all sizes. The timely adoption of automation tools may determine your business's survival. New technologies like AI are sprouting up and changing the field every day. Visit our constantly updating library to keep up with the latest advancements in cybersecurity and meet the demands of an increasingly data-driven world.