Today, every organization is generating and storing billions of rows of data regularly. Considering the massive volumes of data, producing relevant and actionable insights poses a challenge. This data needs to be collected from different sources and given a proper form and structure before it can be processed. It is then organized and stored in a unified place. ETL is a way of integrating data and plays a crucial role in helping organizations to manage their data efficiently.
ETL stands for Extract, Transform and Load and follows the same order to integrate data from multiple stores into a single warehouse. This has become an important step for most organizations wanting to cleanse and store their data and streamline workflows. Analysis on large amounts of data that has not been integrated can be difficult and time consuming.
What is ETL?
ETL is the process of Extracting, Transforming and Loading data into a unified data storage. Data is first extracted from various data stores that an organization maintains, then transformed into a format suitable for reporting and analysis and is finally loaded into a singular warehouse. These three stages are repeated every time new data is added to the warehouse. These processes essentially make sure that the data is finally stored in a central data warehouse, which is devoid of faults and is up to date.
- Extract: Collecting Data from Diverse Sources
Firstly, the data is ‘extracted’ from the many data sources and warehouses and copied or exported to a staging area, which is an intermediate destination. The data could be in various formats like SQL or No SQL, XML, or flat files; therefore, it cannot be stored directly in the data warehouse. The staging area is necessary to allow room for the data to be pulled at different intervals of time to allow the data sources to function without overwhelming them.
- Transform: Changing data for Insights
Data transformation is the second step in the ETL process. When transforming the data, cleansing and validation of the data, as well as some other aggregations are applied and thus, it is converted into a single standard format. This process ensures that the data is consistent, reliable and standardized for the entire organization.
- Load: Dispatching Data to the Target
The final step in ETL is loading, where the data is delivered to the data warehouse. After the extraction and transformation processes are completed, the data taken from the staging area is loaded onto the target destination for storage or analytical uses. At the onset, all the data is loaded into the data warehouse in one go. Given that the entire process is automated into a pipeline, as new data is updated, it is incrementally added.
Benefits of ETL
With the increasing complexity and volume of data, the importance of ETL is gaining recognition. Here are some key benefits of the use of ETL,
- Improved Data Integration – ETL tools allow its users to collect and sort through all their data from varied sources like data warehouses, cloud platforms and other sources to bring it together into a consolidated view. This helps organizations manage and analyze their data more easily and leads to better-informed decision-making.
- Improved Data Quality – Companies are aware of the dangers of raw and unstructured data to the results of the application of analytics. The insights would be ambiguous and misleading and ETL can assist in rectifying this situation. The data is cleansed, standardized and validated to enhance the overall quality of the data, which makes the business insights gained more accurate and reliable.
- Automation – Through ETL tools, processes that are repetitive can be automated in turn increasing operational efficiency. Companies can now focus on the more important tasks and save valuable time.
- Enhanced Data Management – ETL offers a better management method for companies struggling with vast amounts of data. Companies can easily process large volumes of data and transform it into a standard format and as it is loaded into a singular repository, it can be quickly accessed.
Challenges of ETL
Since the 1970s, ETL has been in use and has transformed the way data is handled by enterprises. However, in a progressively cloud-based business environment, the magnitude, diversity and velocity of data are ever-increasing. This has made ETL increasingly unscalable in several ways.
In the modern business environment, ETL tools play a significant role in integrating scattered data. Management of data becomes easier with the use of the ETL process as the data is now validated, presented in a unified view and provides higher processing speed. ETL automates the process and makes it usable for further analytics. Even so, ETL comes with its own sets of challenges and limitations and enterprises must take into account the costs and benefits before its implementation.