The extract, load, transform (ELT) process is a way of handling data in a database. In the database world, it’s called data warehousing or data mining. ELT is used to transform data from one format to another so that it can be used in a different way. The main purpose of ELT is to “clean” the raw data before it’s stored in a database. In this blog, you will learn what ELT is and how it works in data warehouses.

Concepts

Data Warehouse: It is a storage system for a large set of data from different sources. It stores the current and previous data versions used for data analysis and reporting.

Extract, Transform and Load (ETL): It is a process of collecting data from the legacy systems, transforming it into a single consistent data, and loading it to the data warehouse. The staging area is used for the transformation process.

Extract Load and Transform (ELT): It is a replacement for the Legacy ETL system. Here the order of operation is changed. After extracting data, data is directly loaded into the target data store instead of loading into the staging area. All the transformations are performed within the data warehouse, and then the data is loaded into the target tables.

The Traditional Approach ETL

In this approach, data from various databases is collected, transformed, and loaded into the data warehouse.

The steps in this process are as below:

• Extract: It is a process of extracting raw data from the source into a staging area. The data source can be SQL Servers, Files, data warehouses, mobile devices, etc. It can be done manually or with the help of ETL Tools.
• Transform: In this step series of rules are applied to the extracted data. It involves data cleaning, removing duplicate data(deduplication), verification, sorting, and other tasks to improve data quality.
• Load: In this final step, the transformed clean data is loaded into the data warehouse either as full or incremental load in scheduled interval
  

Benefits of ETL:

1. ETL Tools are useful for data movement in bulk, and when there are complex rules and transformations.
2. It is more secure as it performs transformations before loading data into the warehouse, thereby securing private data.

Introduction to ELT

In the ETL process data, teams have to wait, and cannot start their analysis unless the data is transformed and loaded into the warehouse. Another disadvantage of ETL is that ETL Tools aren’t useful for real-time or on-demand data access. In today’s world, data organizations possess a huge amount of cloud-based data that needs to be available across various platforms and environments. With such a limitless volume of data coming in at such faster speed, ELT i.e., Extract Load and transfer have become another option for data warehouse, as it offers benefits that are not available in ETL, especially when speed is a critical factor.

ELT is a modern approach where data cleaning and transformation happen after the data is loaded to the data warehouse.

This approach is beneficial because it is:

1. Agile: data is directly loaded to the data warehouse and is available for use.
2. Simple: Transformations are usually written in SQL which is an easily understandable language by all.
3. Fixing bugs: If there are any errors found in the transformation, we can fix the bug and rerun just the transformation to fix the data. Also, as the analysts have visibility of the data, they can help the engineering team fix the bugs.
4. Transform only required data: Since transformation is done after data is loaded into the data warehouse, users can transform only specific data which is required for analysis.

The main advantage of ELT is you can move all raw data from multiple sources to a single repository and have unlimited access to all data at any time.

ETL VS ELT

Working of ELT on Data Warehouse

In ELT data is extracted from single or multiple locations and loaded into a single location for transformation and analysis.

The ELT involves three steps:

1. Extract: This is a common step in ETL And ELT. Here the raw data is extracted from different sources. Extraction also involves data validation, and the data is accepted or rejected based on the validation.
2. Load: In this step, the data is directly sent to the data warehouse.
3. Transform: In this step, the set of rules is applied to the loaded data and it is transformed to the required format and is then available for analysis.

ELT is implemented using separate tools for each of the above steps. Some of the ELT Tool providers are: IBM, Talend, Informatica, Oracle, and Microsoft.

Conclusion

• ELT seems to be the future of data integration, offering several advantages over ETL.
• With the increasing volume of data in the organizations, the ETL approach is a slow process and cannot handle such huge volumes effectively.
• ELT requires less maintenance and is cost-effective as well.