The significance of ELT in an organization is closely linked to the organization’s reliance on data warehousing. ELT tools gather, read, and transfer large amounts of raw data from various sources and across different platforms. It loads it into a single database or data warehouse for easy access. These tools then process the data to make it more meaningful by performing tasks such as sorting, joining, reformatting, filtering, merging, and aggregation. There are multiple benefits of ELT. They make it faster and easier to move data with a graphical interface, instead of using traditional methods that require manual coding.
By breaking down data silos and providing easy access to data for data scientists, ELT tools enable the transformation of data into valuable business intelligence. In this article, we will learn about the multiple benefits of ELT and some top ELT tools available.
Table of Contents
What is ELT?
ELT stands for “Extract, Load, Transform.” This is a method of integrating data from different sources into a single system, typically a data warehouse or a data lake.
The process begins with the “Extract” step, where data is extracted from various sources, such as databases, files, or APIs.
In the “Load” step, the extracted data is loaded into the target system, often into a staging area.
Finally, in the “Transform” step, the data is transformed and cleaned, so it is in a format that can be easily queried and analyzed. This can include tasks such as removing duplicates, changing data types, and applying calculations or aggregations.
In contrast, ETL (Extract, Transform, Load) approach typically the data is transformed before loading into the target system. In ELT approach, the heavy data transformation is done after loading into the target system, leveraging the power of the target system(often data warehousing system) to perform the transformation.
Benefits of ELT
There are several benefits of using the ELT approach in data integration:
- Improved Performance: By performing the data transformation step after loading the data into the target system, ELT leverages the power of the target system, which is often a data warehouse or a data lake, to perform the transformations. This can greatly improve the performance and scalability of the data integration process, especially for large and complex data sets.
- Increased Flexibility: ELT allows for greater flexibility in the data transformation step, as the data is already loaded into the target system. This allows for more complex and dynamic transformations to be performed, as well as the ability to take advantage of built-in functions and capabilities of the target system.
- Reduced Data Movement: ELT reduces the amount of data movement required, as the data is only extracted from the source system and loaded into the target system once. This can save significant time and resources, especially for large data sets.
- Better Data Governance: ELT allows for better data governance, as the data is loaded into the target system before being transformed. This allows for more accurate tracking and auditing of the data, as well as the ability to easily rollback any changes made during the transformation step.
- Cost-Effective: ELT is more cost-effective than ETL, as it leverages the power of the target system to perform the transformations. This reduces the need for expensive and complex transformation tools, and allows for a simpler and more efficient data integration process.
In summary, ELT is a powerful approach for data integration, providing better performance, increased flexibility, reduced data movement, better data governance, and cost-effectiveness. It is particularly useful for big data scenarios and modern data architectures like data lakes and data warehouses.
Real-World ELT Use Cases
Here are a few use cases where ELT can be used:
- Large Data Volumes: Adopting an ELT strategy can significantly benefit data generated from sources such as sensors and system logs. ELT approach is effective because it allows to handle the high volume and speed of this data, which can make traditional transformation methods difficult, slow and costly. By first replicating this raw data to a warehouse or object storage, changes can then be applied using specialized tools that are designed to handle large data volumes stored in a database system.
- Managing Unstructured Data: Unstructured data can be effectively handled with the use of ELT and systems like Hadoop and MapReduce, which allow for the storage and processing of large amounts of unstructured data using commodity hardware. These systems involve extracting data from its source, and then loading or replicating it directly to a distributed storage cluster. The data is then processed and transformed at a later time, and in some cases, loaded into another database for reporting.
- Schema on Reading: An ELT approach can be beneficial for analysts who need a flexible schema, allowing them to use SQL views on top of raw data tables in the warehouse. Unlike traditional ETL models, ELT follows a schema on read approach which provides more flexibility for data exploration and experimentation. This is because a schema on read approach eliminates the limitations imposed by a pre-defined schema.
An ELT procedure is essential for your company for a number of reasons. There are a variety of ELT solutions out there to suit your needs and preferences as a business. An effective ETL process may enhance your data analytics, provide you with a competitive edge, and help you make better decisions.
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