As data became more and more available to companies, data integration became one of the most crucial challenges for organizations. In the past decades, ETL (extract, transform, load) and later ELT (extract, load, transform) emerged as data integration methods that transfer data from a source to a data warehouse. Despite their similarities, the two differ in fundamental ways.
This blog aims to give an overview of ETL and ELT processes, describe the difference between them and outline which tools are now available for organizations. We do so by covering the following points:
Today, businesses require data integration to centralize, access, and activate their data. As businesses start leveraging data from hundreds of different sources across continents and business departments and teams to make data-driven decisions, data integration has never been more critical. Although this heavily fractured landscape might seem new, it is not. In fact, the first challenges related to data integration date back to the 1960s when IBM and other companies pioneered the first Database Management Systems (DBMS), whose later advancements led to the sharing of data between computers. The process of integrating data with external machines soon became a challenge. This was challenging because organizations integrate with systems that they cannot control, and this often fails. So, it becomes really hard to create systems that don’t break when you do data integration.
Data integration can be defined as the practice of consolidating data from separated source systems to transfer it to a data warehouse. Integration starts at the ingestion phase and includes steps like cleansing, ETL mapping, and transformation. ETL plays a central role in transforming raw data into usable data — enabling data teams to trust their data and make informed business decisions. Without data integration, accurate analytics is impossible to achieve as making decisions with incomplete information most likely leads to undesirable outcomes. And if these decisions involve hundreds of data sources, terabytes of data, and millions of dollars, integration becomes especially important.
ETL was the first standardized approach to dealing with data integration which appeared in the 1970s. It first emerged as a response to the adoption of relational databases and the increasing need to move information between them efficiently. Then, in the 1980s, data integration became even more critical with the rise of data warehouses. Data warehouses could integrate data from different sources. However, they typically required custom ETLs for each data source. This led to the emergence of a wide variety of ETL tools. And by the end of the 1990s, these solutions were affordable for both enterprises and mid-market businesses.
Finally, with the emergence of cloud computing in the 2000s, cloud data warehouses and data lakes brought forward a new development: ELT, which, as we will see later in this blog, allows businesses to load unlimited raw data into a cloud data warehouse.
As organizations collect data from multiple sources — such as CRM systems, Cloud applications, and more — the ETL process aims to gather data from separate sources and create a centralized database. Practically, ETL pipelines have the role of extracting raw data from its source, transforming it, and finally loading it in the warehouse — which is a centralized database.
Let’s dive into each one of these steps:
The first step is the process of data extraction. Here, data pipelines extract data from the different sources and gather it in a staging area. The staging area is vital because it allows the extraction of data from various sources at different times, and it prevents the simultaneous performance of extractions and transformations from happening — therefore averting the risk of overwhelming data sources.
The second step is the data transformation process — in other words, making the raw data fit for analysis. This consists of improving the quality of the data by performing actions like sorting, joining, reformatting, filtering, merging, aggregation, and so on, and restructuring and reformatting the data to adapt it to the specific business need it will be used for.
The last step is loading the transformed data into the warehouse. The loading can be full — so the data is fully loaded into the warehouse at once — or incremental — where the data is loaded at different scheduled intervals using batch or stream loading. Batch loading consists of the scheduled extraction of different batches of data from a source system. In contrast, stream loading consists of data ingestion by pipelines as soon as they are made available by the source system.
The ETL process of migrating transformed data into a warehouse enables organizations to break down silos and makes it easier for data users to turn the available information into business intelligence.
ETL is beneficial for organizations because it combines diverse data into a unified view. This enables data users to find and access information more efficiently, consequently improving productivity.
Implementing ETL into your data stack can bring many benefits, including:
The main characteristic of ETL is the order in which the process is performed — specifically, the data goes through transformation before being loaded into the warehouse. This specific order is the reason behind some of the difficulties organizations face when using ETL. These are the following:
Traditional ETL is characterized by the performance of the transformation process before the loading into the warehouse. The reason for this is that, back in the day when ETL was created, storage, computation, and bandwidth were costly. Hence the need to reduce the volume of data before it gets to the data warehouse.
As storage and computation cost dramatically decreased, cloud storage grew exponentially. This led to the entrance into the market of data warehouses like Amazon Redshift and Google BigQuery. Cloud data warehouses are infinitely more scalable compared to traditional warehouses, allowing organizations to fit enormous amounts of data.
The emergence of cloud data warehouses called for a radical change in ETL processes too. If ETL processes were fine for traditional warehouses, their performance did not improve in a cloud environment. The reason for this is that the scalability issues were inherent to the process of moving data into the warehouse. At the same time, due to the arrival of cloud storage, organizations started loading large volumes of untransformed data into the warehouse without having to worry about its cost. From this moment on, it has been possible to extract data from the source systems and load it into the cloud warehouse without needing to go through the transformation process. The Extract-Load-Transform (ELT) process replaced traditional ETL.
Let’s take a side-by-side look at the differences between ETL and ELT, their pros and cons, and how they can work together to provide a holistic data integration solution for businesses. Here are the ten critical differences between ETL and ELT.
If you’ve made it so far, it probably means you are interested in integrating ETL or ELT in your data stack. But what characteristics should these tools have to meet your standards? We’ve gathered some general, critical requirements that any of these tools should have:
With companies collecting increasingly high amounts of data daily, there is a growing need to distribute data from the source to the data warehouse. This is why we have recently seen a blowing up of ETL/ELT tools in the market. In this section, I’ll briefly outline the different ETL tools that are currently being used by organizations.
ETL can be grouped into different categories based on their infrastructure. I’ll briefly go through three of these groups.
Developed by commercial organizations, these solutions are often the most mature ones in the marketplace. These tools include graphical user interfaces (GUIs) for architecting ETL pipelines, support for relational and non-relational databases, and extensive documentation and user groups.
Although they offer more functionality, enterprise ETL tools are usually more expensive and require more employee training because of their complexity.
Think of tools like IBM DataStage and Oracle Data Integrator.
Many free ETL tools offer GUIs for data-sharing processes and monitoring the flow of information. An advantage of open-source tools is that organizations can access the source code and extend its capabilities.
Think of tools like Airbyte and Talend.
Cloud service providers (CSPs) now offer ETL tools built on their infrastructure. An advantage of this type of solution is that if the organization also stores its data using the same CSP, the pipeline is optimized because all processes occur within a single infrastructure. A disadvantage, however, is that cloud-based ETL tools only work within the CSP environment and don’t support data stored in other locations.
Think of tools like Azure Data Factory and Google Cloud Dataflow.
ETL and ELT can serve your data integration purpose in different ways. So, to choose the solution that’s right for you, you need to consider factors like
The Modern Data Stack is not complete with an overseeing observability layer to monitor data quality throughout the entire data journey. Do you want to learn more about Sifflet’s Full Data Stack Observability approach? Would you like to see it applied to your specific use case? Book a demo or get in touch for a two-week free trial!
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