Explore the ETL process, its importance, the transformation to ELT, and the tools needed to consolidate and analyze data effectively.
Updated June 27, 2024
The ETL process, born in the 1980s alongside the rise of data warehouses, has long been a vital part of data management. But with the advent of cloud technology, the game has changed.
Is ETL still relevant? Absolutely, but it's not your father's ETL process anymore. A frequent oversight in the development and design of ETL is the premature selection of tools and code writing without fully grasping the underlying business needs.
In this article, we will focus on diving into the ETL process, explaining what it is and how it differentiates from ELT. By understanding it, you can decide if this process works best for your organization, depending on your unique business objectives.
ETL, or "Extract, Transform, Load," is a time-tested method of data integration that unifies information into a centralized location, most commonly a database. The process of ETL can be broken down into the following three stages:
Source: IBM Technology
Let's delve into these three essential steps in more depth in the section below, exploring their complexities and relevance in today's data-driven landscape.
The ETL process revolves around three fundamental steps: Extraction, Transformation, and Loading. These phases interconnect to collect, refine, and unify data, making it actionable and reliable. Here's how each step plays a vital role:
The extraction step initiates the ETL process by gathering raw data scattered across various sources, formats, and platforms. This consolidation is key to effective data strategy and analysis. Examples of sources include:
The data is generally pulled into a staging area that sits between the data source and the target destination. Although this process can be hard-corded by a team of data engineers, ETL tools use automation to create a time-efficient and reliable workflow.
Transformation takes the extracted data and refines it to ensure quality, accessibility, and alignment with organizational needs. This phase may happen in an ETL server or be modernized with cloud computing. Key transformation tasks include:
Custom rules may also be implemented to meet specific reporting requirements and enhance data quality.
The final step in the ETL process is to load the transformed data into the target destination, typically a database. There are two types of data loading:
In the full loading method, all the data that comes from a transformation batch goes into the target destination as new, unique records. This process is fairly easy to implement and it doesn't require monitoring whether or not all the data is up to date every time you reload the table. However, full loading is unsustainable in the long run because the datasets grow exponentially and can become difficult to maintain.
The incremental loading approach is more manageable and allows for faster processing. In this method, the system compares incoming data with what's already in the target destination, and only creates additional records if new and unique data is found. Yet, incremental data loads present challenges too. The constant movement of data requires monitoring for errors and potential incompatibility and sequencing issues.Below is a quick example of ETL by Isaac Vaghefi:
The ETL process plays a pivotal role in enabling organizations to make data-driven decisions. But why exactly is this process so indispensable? Here's an exploration of the core reasons:
Read More: Data Pipeline vs ETL: Which Delivers More Value?
While the ETL process offers numerous advantages, it's not without its drawbacks. Here are some key challenges that organizations may face:
While the ETL process has its own distinct benefits and drawbacks, the advent of cloud technology has given rise to an alternative approach: ELT. Unlike the traditional ETL process, ELT is tailored to take advantage of the vast capabilities of cloud computing. The next section delves into the characteristics of ELT, contrasting it with traditional ETL, and exploring why it might be the future of data integration, especially in the context of scalable cloud or hybrid-based data architectures.
ELT (extract, load, transform) is similar to but different from ETL processes. While most traditional ETL software extracts and transforms data before ever loading it into a database, ELT extracts and loads the data first, then transforms it. With ELT, there is no need to clean the data on specialized ETL hardware before putting it into a data warehouse. Instead, ELT creates a "push-down" architecture to take advantage of the native capabilities of the cloud:
Read More: ETL vs. ELT and the Evolution of Data Integration Techniques
A traditional data warehouse architecture cannot expand, at least not quickly or cost-effectively, to keep and handle the volume of data we generate and collect today. The cloud is significantly more scalable in terms of storage and processing. However, traditional ETL is unlikely to benefit from the best practices and inherent benefits that a cloud data warehouse provides.
On the contrary, cloud-native ELT makes use of the best aspects of a cloud data warehouse, including scalability as needed, massively parallel processing of multiple jobs at once, and rapid job spin-up and tear-down. Traditional ETL is effective if you are still on-premises and your data is small and predictable. However, as more companies choose a cloud- or hybrid-based data architecture, that is less and less the case. ELT is the future.
Modern ETL/ELT tools should automate the extraction, transformation, and loading process so that companies can focus on business value and eliminate outdated, labor-intensive, and time-consuming practices like hand-coding.
Choosing the right ETL/ELT tool is not only about extracting, transforming, and loading data but also about aligning the tool with your specific business requirements. Here are some comprehensive considerations to help you make the best decision:
Your company can hire developers to build an ETL process, which is an expensive and long process. That's why most people opt for an ETL/ELT tool that can do all of the above—those data processing solutions are more cost-effective, fast, and more applicable across a variety of data management strategies.
The decision between traditional ETL and modern ELT is not just a technological choice but a strategic business decision. Both have their unique strengths and challenges, and selecting between them requires alignment with organizational needs and goals.
But the critical aspect lies not in the construction of the ETL or ELT process itself, but in what you do with the data and the insights you derive from it. The real value is in transforming raw information into actionable strategies, not merely in choosing or building a process.
Ultimately, the focus should be on leveraging these tools to align data with decision-making. That's the true path to success, and it emphasizes not the pathways but the destinations they lead to.