What is ETL?

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ETL, which is short for extract, transform, and load, is a workflow designed to shift data from a source to a destination while undergoing changes through the process to yield actionable business intelligence. Most commonly, the destination is a data warehouse while the sources could be an industry or a company.

Why is there a need for ETL?

Corporations, companies, and other entities have to deal with an extensive gold mine of user data, and not only from a single source. At any given time, a single entity may have to combine data from several decoupled services to be able to run a useful analysis. 

For instance, an online food delivery service could require data for analysis from restaurants, delivery personnel, and end customers and which might not be stored on the same database. Data warehouses act as a single point of integration for business intelligence, delivering a common view of enterprise data. Thus supporting swift data-driven decision making. 

Architecture details for ETL

ETL involves three main processes i.e extraction from a source, transformation, and loading of data into a destination:

  1. Extraction: Data is extracted from sources such as an enterprise/ corporation’s Microsoft SQL, MySQL, IBM DB, and Oracle databases. The data can be stored as heterogeneous or homogenous. 

    In a homogenous distributed database system, the data is divided into servers but all of them run the same Database Management System (DBMS) software. In contrast, heterogeneous distributed databases are for dissimilar sites operating on different DBMSs.
  1. Transformation: Data transformation includes a number of processes which include:
  • Data formatting: Data could be stored in any form e.g. XML, JSON, or a CSV file but might need to be transformed from one form to another to serve analytical needs.
  • Data correction: Checking data that was declared erroneous 
  • Data cleaning: Automated database examination, detecting and correcting missing, incorrect, or duplicate values. Transformation serves not only the destination storage requirements and helps with analytics, but also adds efficiency to the extraction process.
  1. Loading: This involves the loading of data into the destination. In most cases, this happens to be a data warehouse. When the warehouse is inaugurated, the first action is data loaded in bulk, afterwards, it is added incrementally while the warehouse is operational.

ETL in stream processing

However, carrying out an isolated ETL process might not be very convenient for continuous data streams and a dynamic marketplace. A stream ETL process could be a superior alternative as it allows real-time data analysis built into a streaming pipeline. 

Macrometa’s stream processing allows continuous, real-time collections, tailing files and business insights. Data manipulation functions can be deployed to quickly identify patterns which allows one to stay on top of market trends.

Closing thoughts

An ETL workflow or a pipeline consists of data extraction from a source such as a website, tool, or any business service to a data warehouse which acts as a single integration point for the distributed and/or bulk data. The data undergoes transformation processes to achieve   the end goal of data-driven decision making. A stream ETL can be used for real-time data analytics so value can be extracted promptly.

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