Data pipeline is the process of transferring data from its primary source into a data store from where the user can access it directly for analytics. Along the way, the data undergoes processing to ensure that it can be used to generate insights. Within this processing, the initial steps are most crucial because the correct data sources must be accessed and then the data can be extracted. The data thus presented to the user should show a complete picture of all operations of the business and finally make it ready for manipulation by BI and analytics tools. ETL and ELT are types of data handling procedures where E stands for Extract, T stands for Transform and L stands for Load, with the difference between the two being the order of these steps.
ETL
ETL is the original process that became a standard procedure and has been refined multiple times since its inception. The three distinct steps of this process help in systematic data integration by synthesizing data from multiple sources into a single data repository.
Extraction is the first step that connects all the different data sources and commences data extraction. Before the data can be analyzed, it needs to be identified and copied before being moved to the centralized repository. The data available might be in varied formats and structures but through extraction, it is consolidated and made available for transformation.
Transformation is the next step where the data is cleaned and aggregated. Once the data has been collected, it needs to be processed to ensure that the integrity of the data is maintained. Transformation includes filtering out, standardization, verification and aggregation. This step can be automated, and alerts can be put in place in case of inconsistencies.
Loading is the final step where the transformed data is loaded into a target database. This is the central data repository from where the data can be further used for analytics and reporting. The loading process for most organizations is automated and incremental.
Challenges of ETL
The main problem with ETL is that while dealing with multiple data sources and formats, data loading into the central storage unit is delayed. Real-time access to data is not possible due to the time it takes from data generation to its availability. The ETL process needs to minimize this lag time.
The distance that separates a user from the data is another consequence of the processes in ETL. Users will need to devise extra processing methods if they find that they need to compute additional statistics or delve deeper than what is available to them in the organized and processed data output from the ETL process. If the user is not the same person who maintains the ETL process, the gap becomes more difficult to bridge.
ELT
In the ELT approach to data integration, the data is extracted, loaded into the central data store and then it is transformed to make it suitable for analytics. With new advances in technology and methods of data storage, data integration needed to evolve in order to accommodate the demands for faster data processing of even larger quantities of data. With the new approach to handling data, the extraction step remains the same, but instead of transformation before the initial load, the data in its raw form is directly copied into the target data store with minor cleansing. ELT leverages the capacity and scalability of cloud-based storage to speed up data processing by transforming the data in the target repository after it has been loaded.
Precautions to be taken with ELT
Loading before the data is transformed has its own set of challenges. As accessibility to data increases, it necessitates more work to be done to make the data usable. The data that needs filtering increases in quantity due to the prior loading process. Another concern that arises is data privacy. Since there is negligible processing before the data is loaded, organizations may need to place regulations to handle sensitive data and identify the people who may have immediate access to all organizational data.
Additionally, ELT minimizes the self-service functionality as transformations performed in the data store poses limitations on the extent of data that the users can process. Support from data scientists may be required to successfully carry out complicated transformations.
Conclusion
Both ETL and ELT have their own set of benefits and drawbacks, which makes both processes relevant. Therefore, a combined procedure where both ETL and ELT are performed in multiple stages may prove to be advantageous. As per the given scenario and the requirements of the business, both ETL or ELT can be leveraged interchangeably or in tandem. The preparation of data differs with the use of different processes through a combination of loading and transformation in multiple stages of data processing. The requirements of the organization should be kept in mind when constructing a data pipeline to ensure the correct choice of tools.