What is ELT?

ELT and cloud-based data warehouses and data lakes are the modern alternative to the traditional ETL pipeline and on-premises hardware approach to data integration. ELT and cloud-based repositories are more scalable, more flexible, and allow you to move faster.

The ELT process is broken out as follows:

• Extract. A data extraction tool pulls data from a source or sources such as SQL or NoSQL databases, cloud platforms or XML files. This extracted data is often stored temporarily in a staging area in a database to confirm data integrity and to apply any necessary business rules.

• Load. The second step involves placing the data into the target system, typically a cloud data warehouse, where it is ready to be analyzed by BI tools or data analytics tools.

• Transform. Data transformation refers to converting the structure or format of a data set to match that of the target system. Examples of transformations include data mapping, replacing codes with values and applying concatenations or calculations.

The main difference between the two processes is how, when and where data transformation occurs. The ELT process is most appropriate for larger data sets and when timeliness is important. The ETL process is more appropriate for small data sets which require complex transformations.

Today, your business has to process many types of data and a massive volume of data. This can be a significant challenge for the traditional ETL pipeline and on premises data warehouses.

Extract > Load > Transform (ELT)

In the ELT process, data transformation is performed on an as-needed basis within the target system. This means that this process takes less time. But if there is not sufficient processing power in the cloud solution, transformation can slow down the querying and analysis processes. This is why this process is more appropriate for large data sets and when timeliness is important.

Extract > Transform > Load (ETL)

In the ETL process, transformation is performed in a staging area outside of the data warehouse and before loading it into the data warehouse. The entire data set must be transformed before loading, so transforming large data sets can take a lot of time up front. The benefit is that analysis can take place immediately once the data is loaded. This is why this process is appropriate for small data sets which require complex transformations.

Building and maintaining a data warehouse can require hundreds or thousands of ETL tool programs. As a result, building data warehouses with ETL tools can be time-consuming, cumbersome, and error-prone — introducing delays and unnecessary risk into BI projects that require the most up-to-date data, and the agility to react quickly to changing business demands.

More resources:

• Learn more about the ETL process.

• See a side-by-side review of 10 key areas in the ETL vs ELT Comparison Matrix.

ELT makes the process of managing your big data and cloud data warehouse more efficient and effective. This lets you focus on analyzing your data to uncover actionable insights. Specifically, the key benefits are:

• Flexibility. The ELT process allows you to replicate all of your raw data and thereby quickly transform your raw data on demand as needed for data analytics, BI or other systems.

• Scalability. Modern cloud data warehouses, data lakes and data lakehouses allow you scale to leverage all your raw data.

• Speed. The ELT process typically involves loading all your data into a cloud repository. These cloud services allow you to quickly transform and process massive data sets.

• Lower cost. Unlike ETL pipelines which require an investment in on-premise hardware, cloud-based ELT usually offers you a lower total cost of ownership given that it is priced on a pay-as-use service model.

New ELT tools are known as data warehouse automation and transformation platforms, which automate the repetitive, labor-intensive tasks associated with integration and data warehousing.

Data warehouse automation eliminates error-prone manual coding and automates the entire data warehousing lifecycle from design and development to impact analysis and change management. These automation tools automatically generate the commands, data warehouse structures, and documentation necessary for designing, building, and maintaining your data warehouse program, helping you save time, reduce cost, and reduce project risk.

Seamless integration with a real-time event capture and data integration solution enables real-time ELT by combining real-time source data integration with automated ETL and SQL generation—and supports a wide ecosystem of heterogeneous data sources including relational, legacy, and NoSQL data stores.