The role of ETL and ELT in data warehousing
When we say ETL in the context of Data engineering, we usually think about the three main processes involved in data integration:
Extract: It is the process of extracting data from various sources such as databases, flat files, spreadsheets, and other data sources.
Transform: This refers to the process of transforming the extracted data into a standardized format that can be easily integrated with
the data in the data warehouse. This involves data cleaning, data validation, data standardization, data enrichment, and data normalization.
Load: This refers to the process of loading the transformed data into the data warehouse. The loading process involves inserting the data
into the appropriate tables and ensuring that the data is organized in a way that allows for efficient querying and analysis.
Now, before getting started to understand exact role of ETL and ELT in Data warehousing, let’s quickly understand about Data warehousing concept in
an easy way.
Data warehousing is a process of collecting, organizing, and analyzing large amounts of data from different sources to support business intelligence (BI) activities.
In data warehousing, data is transformed from raw, operational data into a structured format that can be used for reporting and analysis.
The structured data is then stored in a central repository called a data warehouse, where it can be accessed and queried by analysts, managers, and other stakeholders.
Data warehousing involves a number of steps, including data extraction, transformation, and loading (ETL), data modeling, and data analysis.
In the ETL process, data is extracted from various sources, such as transactional databases, spreadsheets, and flat files.
The data is then transformed into a standardized format that can be loaded into the data warehouse. Once the data is loaded into the data warehouse,
it is organized into a structure that facilitates data analysis.
Let’s more focus on ETL and ELT part -
What is ETL :
A typical ETL process collects and refines different types of data, then delivers the data to a data lake or data warehouse such as Redshift, Azure or Big Query. ETL tools also makes it possible to migrate data between a variety of sources, destinations, and analysis tools.
The primary role of ETL in data warehousing is to ensure
that the data stored in the warehouse is accurate, consistent, and reliable for reporting and analysis.
Here are some of the key roles and responsibilities of ETL in data warehousing:
Extracting data: ETL processes are responsible for extracting data from various sources such as databases, flat files, spreadsheets,
and other data sources. This data is then transformed into a common format that can be easily integrated with the data in the data warehouse.
Transforming data: The extracted data is often in a raw or unstructured form, and needs to be transformed into a standardized format that
can be easily integrated with the data in the data warehouse. This involves data cleaning, data validation, data standardization, data enrichment,
and data normalization.
Loading data: Once the data has been extracted and transformed, it is loaded into the data warehouse. The loading process involves inserting the
data into the appropriate tables and ensuring that the data is organized in a way that allows for efficient querying and analysis.
Maintaining data quality: ETL processes also play a critical role in maintaining the quality of the data in the data warehouse.
This involves implementing data quality checks, data profiling, data cleansing, and data enrichment techniques to ensure that the data is accurate, complete and consistent.
Automating the process: ETL processes can be automated to ensure that data is regularly extracted, transformed, and loaded into the data warehouse. This helps to ensure that the data in the data warehouse is always up-to-date and accurate. In ETL data is transformed before loading into a data warehouse so, raw data is not available in the data warehouse but in ELT data is loaded into the warehouse then transformation applied on the stored data.
Staging areas are used for ELT and ETL, in the ETL staging area is incorporated in the ETL tool being used. In ELT staging area is in the database used in the warehouse.
What is ELT :
Extract/Load/Transform (ELT) is the process of extracting data from single or multiple sources. Extracted data will load into a data warehouse. The main advantage of ELT is it will not transform the data before it loading to the warehouse. The target system in ELT will transform the loaded data. This approach requires fewer remote resources than other techniques because it needs only raw or uninterrupted data.
ELT is a replacement of traditional ETL technology. In ELT transformation component is placed in the target database for better performance. This technique is very helpful to process large amounts of data for BI and analytics.
ELT comprises of 3 different operations performed on the data:
Extract: Extracting data is the technique of identifying data from one or more sources. The sources may be databases, files, ERP, CRM or any other useful source of data.
Load: Loading is the process of storing the extracted raw data in data warehouse or data lakes.
Transform: Data transformation is the process in which the raw data source is transformed to the target format required for analysis.
When it comes to data warehousing, ETL and ELT are two critical processes that work together to ensure that data is extracted, transformed, and loaded into a centralized repository in an efficient and effective manner.
It’s tempting to think a creating a Data warehouse is simply extracting data from multiple sources and loading into database of a Data warehouse. This is far from the truth and requires a complex ETL process. The ETL process requires active inputs from various stakeholders including developers, analysts, testers, top executives and is technically challenging.
In order to maintain its value as a tool for decision-makers, Data warehouse system needs to change with business changes. ETL is a recurring activity (daily, weekly, monthly) of a Data warehouse system and needs to be agile, automated, and well documented.
Conclusion
If you’re interested in data warehousing, then it’s important to understand the roles of ETL and ELT. These processes are essential for ensuring that data is accurate, consistent, and reliable, which is essential for making informed business decisions.
The success of a data warehousing project depends on the quality and accuracy of the data that is stored in the centralized repository.
This is why ETL and ELT are such important components of the data warehousing process.
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