ELT Processes

ELT Process in data warehouse

The ELT process is used in data warehousing to extract data from various sources, load it into a data warehouse, then transform it into a format that is suitable for reporting and analysis:

1. Extract: The first step in the ELT process is to extract data from various sources such as databases, files, or APIs. The data can be extracted in real-time or in batches.
2. Load: The extracted data is then loaded into the data warehouse. This step is known as "staging" and involves moving the data from the source system to the data warehouse.
3. Transform: After the data is loaded into the data warehouse, it is then transformed into a format that is suitable for reporting and analysis. This includes tasks such as cleaning, standardizing, and integrating the data. It also includes creating or modifying dimensions and facts, creating indexes and aggregations, and performing any other necessary data modeling.

ELT vs ETL processes

ELT (Extract, Load and Transform) and ETL (Extract, Transform and Load) are two different processes used in analytical data solutions. The primary difference between the two lies in the location where the data is transformed.

In ETL, data is extracted from various sources, transformed into a format that is suitable for reporting and analysis, and then loaded into the data warehouse. This process typically takes place outside of the data warehouse, using specialized ETL tools.

In ELT, data is extracted from various sources, loaded into the data warehouse in its raw form, and then transformed within the data warehouse. This process allows for more flexibility and scalability, as the data can be transformed on-demand, and does not require the use of specialized ETL tools.

ELT and ETL also differ in terms of the type of data that can be transformed. In ELT, the data is first loaded into the data warehouse and then transformed. This means that only the data that is stored in the data warehouse can be transformed. In ETL, the data is transformed before it is loaded into the data warehouse. This means that any type of data can be transformed, including unstructured and semi-structured data.

Another difference between ELT and ETL lies in the amount of data that can be handled. ELT is better suited for dealing with large amounts of data since the transformation process can be broken down into smaller tasks that can be handled more easily. ETL, on the other hand, is more effective for dealing with smaller amounts of data due to the more straightforward nature of the transformations.

The choice between using ELT or ETL depends on the specific requirement of the task at hand. Some of the scenarios in which ELT is preferred over ETL are:

• Large Data Volumes: ELT is better suited to handle large data volumes as the data transformation is done within the data warehouse which can handle large data sets more efficiently.
• Real-time Processing: ELT allows for real-time data transformation, which is more suitable for organizations that need to make real-time decisions based on their data.
• Flexibility: ELT allows for more flexibility as data can be transformed on-demand, and does not require the use of specialized ETL tools.
• Scalability: ELT allows for more scalability as data can be transformed on-demand, and does not require the use of specialized ETL tools.

However, ETL is better for scenarios where data quality and governance are essential, particularly in regulated industries, or where data lineage is important. Additionally, ETL is better when the data transformation required is complex or when the data warehouse is unable to handle the data transformation.

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