What Is ETL And How Do You Build An ETL Pipeline?
How do you build an ETL pipeline? What if you are not a programmer and need to understand what an ETL pipeline is, even if you will not be coding the solution yourself?
Amazon published a very good introduction here, but let’s try discussing the subject in a way that makes sense for a non-technical manager or executive that needs to consider some of the proposals coming from a technical team.
Extract, Transform, and Load (ETL) is a process used in database management and data processing. It’s like a data journey with three major stops: extraction, transformation, and loading. It really describes the process of locating your data sources and extracting it, then changing or transforming it and then loading it to a new location – potentially for use by a different application.
To think about this without worrying about the technology, try imagining the business as a chef and data as ingredients from various sources: customer feedback, sales records, market trends, etc.
The first step, extraction, is like sourcing ingredients from different places. The business collects data from various sources, which could be databases, spreadsheets, or online services. This data is often raw and unstructured, just like ingredients that haven’t been prepped for cooking. It’s important to remember that this can be a mix of structured and unstructured data. You are merely locating the data and extracting it all at this point.
Next is the transformation step, like preparing and cooking the ingredients. This is where raw data is cleaned and organized. It involves sorting, filtering, and converting the data into a format that’s consistent and usable. Just as a chef chops, seasons, and cooks ingredients to make them suitable for a dish, transformation makes data ready for analysis.
The transformation step is crucial because it ensures that the data is accurate, relevant, and in the right shape for what the business needs. This is really the process of collecting all the disparate data that you plan to use and formatting it in a way that makes it easier to start the analysis and searching process.
Finally, there’s the loading step, like serving the prepared dish. Here, the transformed data is moved into a new system — usually a database or data warehouse — where it can be easily accessed, analyzed, and used for decision-making. This is where the business gets to ‘eat’ – or use – the data in its final, usable form.
All the data from all the various structured and unstructured sources will now be loaded into this new data warehouse and will now be in a format that can be easily scanned or searched. It has been transformed into a format that is now easier to query and use.
Why is ETL so vital for modern organizations?
Data Integration: It allows businesses to combine data from different sources, giving a more complete view of the business landscape. This is important because not all data is in an easily usable structured format.
Data Quality: Through transformation, ETL ensures the accuracy and relevance of data, which is critical for making informed decisions. This creates a level playing field so all the information from various sources can be used.
Efficiency: Automating the ETL process saves time and reduces errors compared to manual data handling. In many cases, it would often be impossible to manage this process manually for a large number of data sources, all in different formats.
Business Intelligence: Processed data helps in generating insights and supports business intelligence activities, leading to better strategies and competitive advantage. This is usually the goal – gather data from across the business in various formats, and then create insight.
Compliance: ETL can help in maintaining data consistency and accuracy, which is essential for regulatory compliance in many industries.
I used the analogy of a chef following a recipe and collecting together different ingredients before transforming them into a new complex meal. This is a simple way to think about the entire ETL process. Imagine the dozens of different ingredients that go into a simple Indian curry dish — garlic, chillies, onions, and half a dozen spices in addition to vegetables. All different ingredients blended to create something entirely different – that is what we are doing with the data.
ETL is really like the process of preparing a complex dish. It involves gathering various ingredients (data), preparing them through a series of steps (transformation), and finally serving them in a way that’s useful and enjoyable for the business. This process is essential in today’s data-driven world, where making quick, accurate decisions based on comprehensive and reliable data is key to staying competitive.
IBA Group has a tool called Visual Flow. This is a low-code solution for developers looking to leverage the ETL benefits of Spark without learning a programming language. For more information, please click here.