The rapid advancement in data warehousing technologies has enabled organizations to easily store and process massive volumes of data, and analyze it.
Most data warehouses use either ETL (extract, transform, load), ELT (extract, load, transform), or both for data integration. While both of these approaches have been popular for dealing with large amounts of data from many different sources, the shift from on-premises servers to cloud storage solutions and the need for greater agility has led to a surge in the adoption of ELT.
This ETL vs. ELT overview will help you understand how each of these methods works and which one fits your organizational needs best based on your data size.
What Is ETL?
ETL stands for extract, transform, load. It has been the more traditional approach to data integration. In this process, you Extract data from different sources, Transform the data to help improve data quality and merge data from different data sources into a target format, and then Load it into its destination, which has traditionally been an enterprise data warehouse (EDW).
ETL has been the fundamental (and traditional) approach used for data integration over the last few decades. ETL processes also often relied on a staging area to various intermediate stages of the extracted and transformed data.
The ETL process is straightforward and consists of three steps:
- Extract - The first step, extraction, is in some ways the most unique part of each data integration technology because it requires some type of support for extracting data either in batch or in real-time time, as a stream. The first connectors focused more on batch, in part because it put very little load on the source applications and helped guarantee data was extracted in a consistent form. Then came Change Data Capture (CDC), which used the write-ahead logs of databases to capture each new transaction as a stream. CDC put very little overhead on the applications, much less than triggers or other hooks into the applications. More recently you might see API- or message-based connectors as well for newer data sources and applications.
- Transform - Once you’ve extracted each source you usually have to fix bad data, to improve the data quality. Some call this data cleansing. While it is ideal to start with clean data, it’s rare to actually have it. It’s not just about typos or even short vs long first names. Some data, like addresses, is out of date. Once you’ve cleansed the data, you can merge data together and transform it into a target format. You might be doing this in a staging server, or it might just be done directly by a data integration product that has its own internal storage formats for intermediate data.
- Load - The final step is to load the transformed and cleansed data into the target, such as a data warehouse. You could be streaming from source applications into a staging area and loading the results in batch, end-to-end streaming or both. Each target can have specialized ways of loading as well for performance and scalability. After all, the right connections matter.
ETL, as well as defining the data warehouse target schema, usually weeks or months to develop, test and deploy. So the teams using this method needed to “batch up” all changes needed for the next few months with each project. As a result, data teams and their customers usually had to wait months to get new data. If you asked for project timelines you might get a response like “if you touch the warehouse, it takes a month just to test it.” .
What Is ELT?
ELT stands for extract, load, transform. It originally started with specialized data warehouses, such as Teradata, which could perform certain types of transformations much faster inside the data warehouse.
Unlike ETL, ELT extracts and loads the data into the target first, where it runs transformations, often using SQL. The target is most commonly a data warehouse or staging areas like a database, Spark, or Hadoop.
Some of the more modern ELT technologies have been built using a combination of replication and SQL-based transformation tools such as DBT (Data Build Tool). Because modern cloud architectures are highly scalable, it allows organizations to easily perform the cleansing and other transformations needed. It also makes data pipelines more flexible.
Data analysts can do all the work on their own in SQL without having to rely on separate teams to get at new data.
ELT is not only different from ETL because it puts the T at the end, within the target. ELT is also well suited for the next-generation staging area, the data lake. Data integration teams might use ETL to extract, transform and load raw data into a data lake, where the transformations ensure the data is high quality. Data engineers can then use ELT to extract and load data directly from the data lake into a data warehouse and then transform it using SQL to quickly get the results they need for new reports or dashboards in hours or days.
ETL Vs. ELT: Pros And Cons
Both of these data integration methods offer unique advantages and disadvantages. Listed below are some of the notable pros and cons of ETL (extract, transform, load).
- Lower overhead: Since the ETL process provides output so that only relevant information is extracted and processed into the data warehouse, it helps organizations lower computing and administration costs.
- Minimal resource consumption: The highly targeted load process of the ETL data integration method ensures that the storage server only contains information that is needed. This ensures reduced resource consumption, which in turn lowers costs of administration.
- Supports multiple tools: ETL tools are more mature, which has resulted in broader and deeper support for a host of technologies.. It means you can spend less time on making technologies work together. .
- Stable and efficient: ETL may limit the amount of data you have, but less, more structured data does lead to faster queries.
- Compliance with data security standards: Another notable advantage of ETL tools being being more mature is that they have added features to support for various security and regulatory requirements. This is slowly disappearing as an advantage, and differences will vary across tools.
- Can be time-consuming: The highly targeted data transformation process can be a hurdle at times. If you need any new data that you didn’t include in the last project, you’ll need to wait for the next release, which is often months..
- Comparatively older technology: Experts say that ETL is outdated as it works with traditional infrastructure. It wasn’t designed to be a cloud service, or support newer data pipelines for streaming analytics, for example.
Here are some significant advantages and drawbacks of the ELT (extract, load, transform) process:
- Greater agility: ELT offers increased agility. You can directly load all of the (raw) data into the warehouse. With all of the data readily available, you get the option of structuring the data transformation model as and when needed.
- Increased flexibility: With ELT, all of your raw data is readily available in the data warehouse or data lake. It makes data more accesssible. .
- Increased scalability: With ELT, transformations can be deployed as SQL on a modern cloud data warehouse that has nearly unlimited scale.
- Comparatively new method: Although ELT has proven its worth, it is still a new method compared to ETL. As a result, it often faces limited availability of tools.
- Limited connectivity: Compared to ETL, you may not have as many connectors to legacy systems. This is becoming less of an issue over time as ELT products mature and some legacy systems are phased out. In addition, performing ETL to a data lake, and ELT from a data lake to a data warehouse eliminates this problem..
- Requires a data lake: ELT is best when you have easy access to the raw data via a data lake. But that does require companies to implement a data lake.
ETL Vs. ELT: Differences
The ETL method minimizes traditional data warehouses’ drawbacks, such as high computational and storage charges. With the exponential development and declining costs of cloud-based storage solutions, ETL is gradually becoming outdated. The underlying contrast in these two approaches lies in how the raw data is handled, loaded into the warehouse, and analyzed.
Here’s a detailed comparison of ETL and ELT based on the fundamental features:
The main reason to compare ETL and ELT is not to choose between one or the other, but to understand where you need to use each one in your data pipeline. As your pipeline evolves to support keeping your raw data in a data lake, and to support more agile analytics, you may come to realize what many other data driven companies have realized. Both ETL and ELT have their place: ETL can be ideal for moving data from source legacy systems into a data lake, while ELT can be better suited in supporting self-service analytics so that data engineers and analysts can add new data for new reports and dashboards at any time.