Getting Started with ETL: A Beginner’s Guide

Category: Blog
content and process automation

“ETL” refers to the process of “Extracting” data from various sources, “Transforming” it into a more usable form, and then “Loading” it into a target database where it can be leveraged and analyzed.

Source data can be structured or unstructured, come from a single system or disparate sources and be managed by a single person or numerous teams worldwide. It doesn’t matter how raw, chaotic, and scattered your data is; ETL can transform it into valuable insights that inform decision-making and drive business growth.

This article provides a beginner’s overview of all things ETL. We’ll cover the ETL process in detail, the various kinds of ETL tools, the features to look out for, and why ETL is worth the investment. By the end, you’ll understand how ETL can revolutionize your data management practices.

The ETL Process in Action

ETL has been used since the 1970s to integrate and load data into centralized repositories. In the 1990s, it became the primary approach for processing and loading data into data warehouses, where it can be analyzed to drive business decisions.

Let’s take a closer look at the three-phase process that constitutes ETL.

Phase 1. Extract

The ETL process typically begins by extracting raw data, which can be structured or unstructured, from one or more source locations. These sources may include business systems (ERP or CRM), transaction databases, emails, web pages, and even IoT devices.

A robust ETL solution is designed to excel at capturing and integrating data from heterogeneous sources often supported by different vendors and located on multiple servers.

Once extracted, data is sent to a staging area for the next phase to begin.

Phase 2. Transform

After arriving in the staging area, ETL data undergoes a series of processes to prepare it for its intended purpose. This includes thorough cleansing, consolidation, and transformation to make data compatible with the technical requirements of the ETL data warehouse or target location.

The ETL admin creates rules or functions for transformation and validation. Examples of common transformations include:

Mapping a column of data in a .csv file to a different column in another table to match the schema of the target data warehouse.
Aggregating and summarizing data from multiple locations, for example, extracting maximum, minimum, and mean values.
Enriching company data with third-party data sources, such as adding demographic information to a customer database.
Removing or renaming duplicate files.
Encrypting data for security or to comply with industry or government regulations.

Phase 3. Load

The last phase in the ETL process loads the freshly transformed data from the staging area to the target site or sites. This process is either continuous or carried out in batches.

Batch loading, which usually occurs outside of office hours when systems are in low use, is traditionally the most efficient method for dealing with large data volumes. However, modern ETL solutions that enable continuous data streaming of vast quantities of data are becoming increasingly accessible.

The Load phase tends to be slower than the Extract and Transform phases. It’s, therefore, an easy way to benchmark ETL tools against each other. You can judge the speed of different ETL vendors by comparing their results for TB (terabytes) per hour or GB (gigabytes) per second, as well as the respective power of their servers. Security and Disaster Recovery

4 Key Benefits of ETL Software

Here’s why ETL software is so valuable for modern businesses.

1. Data-Driven Insights

ETL transforms scattered, unstructured data into valuable data-driven insights. Management can use these insights to make better strategic decisions when and where it matters most.

2. Single Source of Truth

ETL breaks down silos and consolidates data in a centralized repository. A single source of truth enables internal and external stakeholders (where applicable) to access insights, share information, collaborate, and innovate.

3. Enhanced Productivity and Efficiency

ETL tools automate slow, manual processes, freeing up IT and other teams to work on higher-value tasks. Decision-makers don’t have to wait for engineers to provide data for analysis–they can self-serve, getting more done with less frustration.

4. Digital Transformation

New technologies like artificial intelligence, machine learning, and the Internet of things require (and generate) data sets. Companies can use ETL to handle this big data. For example, they can extract data from multiple IoT sources to a single location where it can be used for machine learning.

The Different Types of ETL Tools

In the past, companies used to write their own ETL programs. Fortunately, this laborious process was replaced by batch-processing ETL tools, which are themselves giving way to other continuous-processing solutions. Here are the main types of ETL tools on the market today:

Batch Processing ETL Tools: These ETL tools process large volumes of data in batches and are still industry standard.
Open Source ETL Tools: Open source ETL tools provide a low-cost alternative to commercial ETL systems but often lack support and advanced transformation features.
Real-Time ETL Tools: Real-time ETL tools process and stream data in real time, which is essential for time-sensitive analytics use cases.
Cloud-Native ETL Tools: Cloud-native ETL tools extract, transform, and load data in the cloud rather than on-premises (see below).

Cloud vs. On-Prem ETL

According to a recent report from Foundry, 63% of companies will have most or all of their IT infrastructure in the cloud by the end of 2023. So, with the boom in cloud computing and SaaS (software as a service), it’s not surprising many companies are considering taking the cloud based ETL route. So, should they?

The answer for most small and medium-sized businesses is a definite “yes’. For larger enterprises, it’s a probable “yes.” Below are some of the benefits to consider.

Lower costs: The TCO of cloud-based ETL tools is less than on-prem.
Lower maintenance requirements: With cloud based ETL, maintenance is performed by the vendor.
Uptime reliability: Cloud-based solutions suffer less downtime.
Remote accessibility: Cloud-based solutions are accessible from any device with an internet connection.
Scalability: Cloud solutions have limitless capacity.
Integrations via APIs: Integrating ETL with other cloud-native solutions is easy and low-risk with APIs.

ETL Features to Lookout For

Whatever type of ETL software you opt for, consider these best-in-class features.

Intuitive: Your ETL tool should be user-friendly for technical and non-technical business users. Some ETL solutions can even translate complex data sets into presentation-ready formats.

Automated: You should be able to create automated workflows to perform the most time-consuming and repetitive tasks.

No-Code: You should be able to build transformation rules and data paths with a visual drag-and-drop interface rather than relying on IT professionals and code.

Advanced: Your ETL tool should be able to deal with the most complex calculations and transformations.

Scalable: Your ETL tool should grow with you as you scale. There shouldn’t be a future where you have to rip and replace your solution because it doesn’t have the processing power to handle your load.

Secure: Your tool should prioritize security and regulatory compliance.

Customization: Depending on the size and complexity of your business, you may want an ETL tool that provides the ability to build and customize transformations rather than relying on out-of-the-box options.

3 Key Takeaways

ETL refers to the process of extracting, transforming, and loading data from various sources to a data warehouse or other ETL database.
ETL provides valuable data-driven insights, automates manual processes, drives efficiency, and readies businesses for further digital transformation.
When evaluating ETL tools, consider the low cost, low maintenance, reliability, scalability, and security of cloud-based solutions.