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ELT (Extract, Load, Transform) is a data integration process where raw data is loaded first and transformation happens after. Learn more about ELT, the key benefits, and how it differs from ETL.
ELT is a three-step data integration process that stands for Extract, Load, and Transform. This process moves data from multiple sources, stores the data (load) into a new destination, and preprocesses the data into a format required for analysis (Transformation). Thus, unlike ELT, this process moves the transformation task to the target data warehouse.
Unlike ETL, this process started with the emergence of cloud computing technologies like DataBricks, Confluent, and Snowflake in the 2000s and is ideal for processing structured, semi-structured, and unstructured data.
Raw data is read and exported in various forms (structured or unstructured formats) from multiple data sources, such as vector databases, Cloud SaaS platforms, APIs, social media platforms, flat files, CRM, and SQL or NoSQL relational databases.
Exported data is moved into a data storage area. This data store can be a data warehouse, data lake, NoSQL database, or cloud.
Various transformations are applied to the data before analysis using a schema-on-write approach.
Just like ETL, ELT is best when it comes to the following use cases:
ELT works best for real-time analysis since the data is received directly from the source and loaded immediately into the storage. This way, organizations can instantly access the data and push it for various business intelligence reports for real-time data processing and reporting.
For some analytics projects, real-time data processing and analytics are necessary. ELT is the way to go for these options because of its low latency, ability to handle large volumes of data, and various data formats.
Since transformation occurs after loading into the database, you can transform it based on the use case requirement. This removes the required upfront transformation, as transformation does not need to be fully defined as data extraction.
Most ELT technologies leverage parallel data transformation and distributed architectures like Hadoop, thus making it a scalable data integration process. Also, since the ELT process delays transformations till after loading, latency is reduced, and data is processed faster.
ELT data integration process favors big data because the raw data is directly loaded into the warehouse. Thus, it is a more simplified and efficient process.
Some decades ago, developers used ETL for processing and integrating with data sources. However, since the emergence of cloud computing technologies like DataBricks, Confluent, and Snowflake, there has been a shift to ELT. This shift has been due to all of the benefits of ELT:
Reduced costs
Ability to scale
Data volume
Flexibility in the transformation process
Diverse data format
But will ELT replace ETL? The decision between ETL and ELT depends more on your use case and business requirements. For instance, cloud-native projects may opt for ELT because it is more cost effective. However, ETL will be more suitable for projects with predefined complex transformation logic.
While ELT has gained popularity with the rise of cloud-based data storage, there are situations where ETL may be the better choice. The ETL process ensures consistency, quality, data security, and performance optimization because your data is transformed and encrypted before it is loaded into the target environment.
Thus, if you have primary legacy infrastructure or monolithic setup where batch processing is adequate, then keep it simple and stick with ETL because it is often simpler and more reliable. Why? Because the data is transformed before loading and this is ideal for structured data environments.
Likewise, if you’re dealing with a massive amount of real-time data streams, have distributed systems, or need to leverage stream processing or analytics, then a real-time ETL pipeline may be the way to go. This will allow data to be brought in quickly as soon as the transformation is ready.
However, you should consider ELT when your transformation process can’t keep up with all the source data coming in.
While ELT allows for rapid data loading, especially in a cloud data warehouse, it is always best to pick a data integration method that aligns best with your goals and technical requirements. Thus a “Shift Left” back to an ETL model might offer you greater control and flexibility, especially in complex data environments.
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