• Navid Nassiri

Why is ETL required?


We talk about ETL a lot with our customers and in our content, but why is ETL so important, and where is it applied? In simple terms, as data becomes an increasingly valuable commodity, ETL is one method by which we can tame the beast, as it were, and start to extract more meaningful insights for the different teams across an organization.


What is ETL and why is it important?


The term ‘ETL’ stands for ‘Extract, Transform, and Load,' and refers to the process of extracting raw data (usually from multiple sources), transforming this into foundational data, then loading it into a target destination (usually a data warehouse). ETL may sound simple, but is actually highly complex in real-world applications.


The concept is sometimes alternatively referred to as ‘ELT’ - ‘Extract, Load, and Transform.’ So rather than move the large volumes of extracted data into an interim location for transformations, ELT transfers them directly into the target destination, where the transformations are applied. You can read more about ELT vs. ETL pros and cons in our article ‘ETL vs. ELT: Key Differences’.


The most important role of ETL is in business intelligence, where companies develop strategies for data analysis and the management of information for commercial benefit. Here are some critical benefits of using ETL:


  • Unifies disparate data into a single repository.

  • Provides a means to verify data aggregations and transformations.

  • Enables sample data to be compared to the source and target data.

  • Moves data from legacy systems to modern platforms.

  • Provides a foundation for deeper data analysis.


What is an ETL process in data analysis?


Let’s say you're looking to analyze traffic for a website to find out the geographic locations of visitors. The stages of an ETL process in data analytics could be something like the following:


  1. Extract data from a web log, such as a CLF (Common Log Format) TXT file, which contains IP addresses, timestamps, and URLs visits.

  2. Clean and pre-process the data, for example by completing or deleting incomplete records, and removing duplicates.

  3. Transform the data by mapping IP address to regions, then aggregating URL visits by region.

  4. Load the transformed data into an SQL (Structured Query Language) database.

  5. Analyze the results to understand the locales in which the website is most popular, and use this information to better tailor its content to these markets.


Why do we need ETL testing?


An ETL process requires testing to validate its output dataset. This is to ensure that each stage has been performed correctly, as well as to gauge the performance of the ETL pipeline itself. As with most big data manipulation tasks, ETL testing requires coding to carry out its operations. These involve tasks such as counting records to confirm they were all transferred, running test cases, and documenting issues.


As you can see, the web logging ETL example uses more stages than simply 'extract', 'transform', and 'load'. In fact, most pipelines involve at least five stages, with testing being carried out in the final stage.


You can read more in our blog post, Five steps of the ETL process, but here's a summary:


  1. Extract – Pull data from disparate sources into interim storage.

  2. Clean – Certify that the data is of sufficient quality prior to transformation. This is usually carried out using a series of standardization and normalization steps, eliminating any invalid or incomplete records.

  3. Transform – Apply processing rules to the data, and convert it into the desired format for the target destination.

  4. Load – Move the completed data to the target destination, such as a data warehouse.

  5. Analyze – Test the pipeline for errors and bottlenecks, then evaluate the output to draw conclusions.


What is ETL in SQL?


SQL is a popular computer language used to query and manipulate databases. Since most data pipelines involve extensive probing of different data sources, SQL has cemented its place in the ETL toolkit.


However, SQL cannot access databases in different systems, so other ETL software is required to first transfer data into a warehouse. SQL is part of the ETL process, but doesn't take care of the entire procedure alone.


As you can see, ETL, and the optimization of its process, are critically important to data-driven companies. You can read our ultimate guide to ETL here. Switchboard offers the platform and expertise you need to reap the benefits of ETL, so get in touch with our data experts today.

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