ETL and ELT refer to two patterns of data storage architecture within your data pipelines.
The letters in both acronyms stand for:
So both ETL (extract, transform, load) and ELT (extract, load, transform) processes help you collect data, transform it into a usable form and save it to permanent storage, where it can be accessed by data scientists and analysts to extract insights from the data.
What is the difference?
Well, the obvious distinction is in the order of operations: the ETL process transforms the data before committing it to a data storage, while the ELT design pattern prioritizes storage before the transformation logic is applied.
This tinny difference acts as a domino effect. Based on the architecture pattern you chose, your data pipelines offer different benefits and are optimized for different use cases.
In this article, we will dive deeper into understanding the ETL process, ELT process, how the two differ, and which one to chose for your operational data needs.
The ETL process collects the raw data from various data sources (your CRM, ad accounts, ERP, email servers, …) and saves them to the staging area.
Before data can be loaded in the target data warehouse or database of your choice, the data undergoes extensive transformations.
Depending on your business logic, you might mask sensitive personal information, remove outliers, or aggregate metrics to make your analysts’ life easier, before finally loading data into the data storage.
The ELT process is similar to the ETL one. There is also data extraction, data transformation, and data loading.
But unlike the ETL process, ELT loads all the data into a data lake. Only later, you can apply transformation logic to the data before moving it to a data warehouse.
To re-iterate - the ETL process extracts data to a staging area and carefully picks what data gets loaded further, while the ELT process extracts all data, and only later applies the needed transformations.
There are 11 crucial differences between ETL and ELT processes:
ETL processes store only structured - aka relational - data.
In contrast, the ELT process stores all types of data structured as they appear in the source data. That includes structured data, but also semi-structured and unstructured data like raw data files (XML, JSON), videos, images, audio clips, sensory telemetry, etc.
Keep in mind that both processes can extract unstructured or semi-structured data. But only ELT saves the data in an unstructured or semi-structured format.
ETL usually operates in the ranges of MB or GB, while ELT works with orders of magnitude higher data volumes (PB, TB). This is why ELT is the preferred choice for big data applications.
Because ETL needs to transform data before loading it, the entire process experiences higher latency than its ELT counterpart.
Transformations can be extremely time-consuming, especially if they require multiple passes over the data, such as in complex aggregate queries, or complex data cleansing, such as transforming unstructured data into structured data.
This makes ELT pipelines faster.
Engineers building ETL pipelines specify in advance the data constraints applied before loading the data into the system. This makes the ETL process less flexible.
The data in ELT, on the other hand, is loaded first, transformed later. This makes the ELT paradigm much more flexible. For example, if you realize your machine learning algorithm needs an additional field, you can just re-run a slightly modified pipeline from the loaded data. In contrast, the ETL process would not give you this option, since non-approved data was never loaded in the database, to begin with.
ETL scales slower than ELT since the transformation layer acts as a bottleneck. Simply saving data is fast. But if you transform it before loading it, you could delay data ingestion. This delay increases with sudden surges of data volumes or velocities, aka, whenever your system would need to scale.
ETL incurs higher implementation costs than ELT, because the design of the target data warehouse, as well as the necessary transformations, need to be set in place before you start importing data.
Maintenance costs can be high in both systems, but they occur at different stages of the process.
With ETL, a lot of the transformation logic is applied in the extract phase. You only collect data you will need downstream, to speed up the entire pipeline. This causes maintenance overhead since raw data sources change all the time, and engineers need to re-write extraction scripts.
On the other hand, maintenance of ELT processes happens in two locations.
The first load data storage can quickly become unmanageable because it acts as a dump. Regular cleanups and documentation efforts are set in place to tame the load beast.
Additionally, the same issues of changing raw data affect the ELT process. But maintenance of changing source data is handled by re-engineering transformation pipelines after loading the data. This gives your engineers more flexibility (no data is lost if a transformation script fails to adjust to the new incoming data structure), but it does not save them from the maintenance work.
Regulatory compliance - such as GDPR, HIPAA, or CCPA - sets high standards in place when working with personal or sensitive data.
The ETL pipeline is easier to keep compliant. Sensitive data is either never imported into the target data warehouse or is masked before importing (e.g. an IP mask occludes the last octet to prevent identification).
ELT is much more prone to issues surrounding data privacy. Because all data is loaded, irrespective of its sensitive nature, ELT architects and engineers need to work around to clock to secure the data from privy eyes. Roles-based access control and data security become even more important and time-consuming to guarantee similar levels of regulatory compliance as in the ETL counterpart.
ELT requires orders of magnitude more storage than the ETL process. Saving all the raw data in its non-filtered form increases the requirements for storage space.
The cost of storage is lower in ETL.
But with the advent of the cloud as a service, commodified storage is cheap, and the cost advantage of ETL over ELT is becoming lower by the day.
The technology underpinning ETL processes revolves around databases and online analytical processing (OLAP) cloud data warehouses. Notable examples of data warehouses include Amazon Redshift, Snowflake, and Google BigQuery.
On the other hand, ELT handles the fast and massive data loads, by relying on a new technology called the data lake. Data lakes are specialized data storages that can handle massive and fast loads. Examples of data lakes include Amazon S3, Apache Hadoop, and Microsoft Azure Data Lake Storage (ADLS).
(Dive deeper into the 9 differences between a data lake and data warehouse)
ETL has been around for multiple decades and is much more mature. From tried-and-tested architecture patterns to devoted ETL tools, the ETL process is much more mature than its ELT counterpart.
This carries two consequences:
Based on the 11 crucial differences, which paradigm should you choose?
ETL is best suited for fast analytics in smaller-to-medium data environments, where the source data and data operations are well-controlled and do not evolve constantly (do not need flexibility).
ELT, in contrast, is best suited for working with semi-structured or unstructured data, in big data environments, where the changing data operation requirements foresee a lot of needed flexibility.
Keboola is an end-to-end data platform that automates all of the processes in your data pipelines.
Whether you favor ETL or ELT, Keboola offers more than 250 integrations between sources and databases, data warehouses, and data lakes.
Try it out. Keboola offers a no-questions-asked, always-free tier, so you can play around and build your pipelines leading to the ETL or ELT architecture with a couple of clicks.