ETL and ELT Approaches in Data Integration: 5 Critical Differences
In the world of data management, the debate between Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT) is an increasingly relevant topic. The essential difference lies in the sequence of operations: ETL processes data before it enters the data warehouse, while ELT leverages the power of the data warehouse to transform data after it’s loaded. As the digital landscape continues to evolve, understanding the critical differences between these two methodologies becomes essential in optimizing data transformation strategies.
Both ETL and ELT are integral processes in data integration, with a distinctive variation in their approach. ETL method moves data from the source to staging, then into the data warehouse, allowing for intricate data transformations and more cost-effectiveness. On the other hand, ELT uses the capabilities of the data warehouse for transformations, eliminating the need for data staging and facilitating a potentially faster data processing.
However, the complexity of this topic extends beyond simple sequencing. From considerations on data privacy and compliance to cost-effectiveness, this article delves into the five critical differences between ETL and ELT, providing you with a comprehensive guide to make an informed decision tailored to your data needs.
Overview of ETL and ELT
ETL vs. ELT is easy to explain, but understanding the potential advantages of ETL vs. ELT—requires a deeper knowledge of how ETL works with data warehouses and how ELT works with data lakes.
Both ETL and ELT are necessary integration methods in data science because information sources—whether they use a structured SQL database or an unstructured NoSQL database—will rarely use the same or compatible formats. Therefore, we have to clean, enrich, and transform our data sources before integrating them into an analyzable whole. That way, our business intelligence platform can understand the data properly and derive actionable insights that drive business success.
As we’ve touched on, regardless of whether our data managers use ETL or ELT, the data transformation and integration process involves the following three steps:
Extract: Extraction refers to pulling the source data from the original database or data source. With ETL, the data goes into a temporary staging area. With ELT, it goes immediately into a data lake or data warehouse storage system.
Transform: Transformation refers to the process of changing the structure and format of the information, so it integrates with the target data system and the rest of the data in that system.
Load: Loading refers to the process of depositing the information into a data storage system.
ETL and ELT perform these steps in a different order from one another. So, the question is: should we transform our data before or after loading it into the data repository? To answer that, we need to understand ETL and ELT separately.
ETL Process in Detail
Online Analytical Processing (OLAP) data warehouses—whether they are cloud-based or onsite—need to work with relational SQL-based data structures. Therefore, any data we load into our OLAP data warehouse must be transformed into a relational format before the data warehouse can ingest it. As a part of this data transformation process, data mapping may also be necessary to combine multiple data sources based on correlating information. This is so that our business intelligence platform can analyze the information as a single, integrated unit.
That’s why some types of data warehouses require ETL—because the transformations must happen before the data is loaded. Here are some details to understand about ETL:
- ETL requires a continuous, ongoing process with a well-defined workflow: ETL first extracts data from homogeneous or heterogeneous data sources. Next, it deposits the data into a staging area. From there, the data goes through a cleansing process, gets enriched and transformed, and is finally stored in a data warehouse.
- It used to require detailed planning, supervision, and coding by data engineers and developers: The old-school methods of hand-coding ETL transformations in data warehousing took an enormous amount of time. Even after designing the process, it took time for the data to go through each stage when updating the data warehouse with new information.
- Modern ETL solutions are easier and faster: Modern ETL, especially for cloud-based data warehouses and cloud-based SaaS platforms, happens a lot faster. By using a cloud-based ETL solution, users can instantly extract, transform, and load their data from diverse sources without having programming expertise.
ELT Process in Detail
ELT stands for “Extract, Load, and Transform.” In this process, the transformation of data occurs after it is loaded into the data storage solution. That means there’s no need for data staging. ELT can use cloud-based data warehousing solutions such as Snowflake for all different types of data—including structured, unstructured, semi-structured, and even raw data types.
The ELT process also works hand-in-hand with data lakes. Data lakes are special kinds of data stores that, unlike OLAP data warehouses, accept any kind of structured or unstructured data. Data lakes never require you to transform your data before loading it. You can immediately load any type of raw information into a data lake, no matter the format or lack thereof.
- A more recent technology made possible by high-speed, cloud-based servers: ELT is a relatively new technology made possible because of modern, cloud-based server technologies. Cloud-based data warehouses offer near-endless storage capabilities and scalable processing power. For example, platforms like Amazon Redshift and Google BigQuery make ELT pipelines possible because of their incredible processing capabilities.
- Ingest anything and everything as the data becomes available: ELT paired with a data lake lets you immediately ingest an ever-expanding pool of raw data as it becomes available. There’s no requirement to transform the data into a special format before saving it in the data lake.
- Transforms only the data you need: ELT transforms only the data required for a particular analysis. Although it can slow down the process of analyzing the data, it offers more flexibility—because you can transform the data in different ways on the fly to produce different types of metrics, forecasts, and reports. Conversely, with ETL, the entire ETL pipeline—and the structure of the data in the OLAP warehouse—may require modification if the previously decided upon structure doesn’t allow for a new type of analysis.
5 Critical Differences between ETL and ELT
Adoption of the technology and availability of tools and experts: ETL is a well-developed process used for over 20 years, and ETL experts are readily available. ELT is a new technology, so it can be difficult to locate experts and more challenging to develop an ELT pipeline compared to an ETL pipeline.
Availability of data in the system: ETL only transforms and loads the data that you decide is necessary when creating the data warehouse and ETL process. Therefore, only this information will be available.
ELT can load all data immediately, and users can determine later which data to transform and analyze.
Compliance: ETL can redact and remove sensitive information before putting it into the data warehouse or cloud server. This makes it easier to satisfy GDPR, HIPAA, and CCPA compliance standards. It also protects data from hacks and inadvertent exposure. ELT requires you to upload the data before redacting/removing sensitive information. This could violate GDPR, HIPAA, and CCPA standards. Sensitive information will be more vulnerable to hacks and inadvertent exposure. You could also violate some compliance standards if the cloud-server is in another country.
Costs: Cloud-based SaaS ETL platforms that bill with a pay-per-session pricing model (such as Integrate.io) offer flexible, scalable plans that can adjust as data ingestion grows or reduces. Enterprise-level on-site ETL solutions are much more expensive. Cloud-based SaaS ELT platforms that bill with a pay-per-session pricing model also offer flexible plans with low-cost options for smaller-scale transformations. One cost advantage of ELT is that you can load and save your data without incurring large fees, then apply transformations as needed. This can save money on initial costs if you just want to load and save information. However, financially strapped businesses may never be able to afford the processing power required to reap the full benefits of their data lake.
Waiting time to perform transformations: Data transformations take more time initially with ETL because every piece of data requires transformation before loading. Also, as the size of the data system increases, transformations take longer. However, once transformed and in the system, analysis happens quickly and efficiently.Since transformations happen after loading with ELT, on an as-needed basis—and you transform only the data you need to analyze at the time—transformations happen a lot faster. However, the need to continually transform data slows down the total time it takes for queries/analysis.
