ETL vs data preparation is a dilemma that almost every organisation faces today while trying to choose the right data handling and analysis tool for their business. Though ETL has been around for several decades, the legacy technology is now being incorporated with artificial intelligence to streamline, automate, and monitor ETL processes in order to make them error-free. Data preparation is a comparatively new technology which is mainly used in machine learning projects to prepare data before it is fed to ML models. ETL tools are more technical in nature with a high learning curve and they can mainly be used by IT users only. On the other hand, data preparation tools are based on machine learning which allows even business users to analyse and visualise data successfully.  In this article, we will explore the major differences between ETL and data preparation.

ETL vs Data Preparation: A Brief Overview 

Before we start comparing the differences between ETL and data preparation, let’s first understand each individually.

What is ETL? 

ETL is short for Extract, Transform, and Load. The ETL process includes extracting data from different source systems, joining data as needed, transforming data according to the pre-decided business rules, and loading data into the target system. ETL helps transform unstructured data into structured data which is then used for analysis. Though it’s important to note that advanced big data technologies these days already have a built-in for structuring data, so the ETL process isn’t needed in that case.

ETL processes data in multiple stages and uses different physical layers for each stage including stage database, operational database, data marks, and data warehouses. Moreover, ETL is majorly used for loading processed data into flat files or relational databases. Developed in the 1970s, Extract, Transform, Load (ETL) has been the de facto way of moving and transforming data within data warehouses for a long time.

What is Data Preparation?

Data preparation also called as data wrangling is the process of preparing the data for reporting and analysis. Though it is similar to ETL, you don’t need to be an IT professional to use a data preparation tool. Usually, these tools offer a visual, easy to use, and self-service interface which allows business users to prepare data with little to no training and minimal help from the IT team. It helps in cleaning and unifying all the complex and unorganised datasets for better accessibility and faster data analysis. With the high volumes of data which is only growing at a rapid rate across organisations, data preparation has become more important than ever. 

The main steps to data preparation include:

  • Access and assess data.
  • Transform data though processes like granularity, temporality, and manipulating the structure.
  • Publishing the transformed data so that it can be used for data analysis and visualisation.

The Key Differences

While data preparation and ETL may seem similar based on their definitions, there are some key differences between the two:

1. ETL vs Data Preparation: Target users 

While data preparation is made for business analysts, ETL tools are aimed towards IT professionals. Data preparation tools are based on the idea that those who know data the best (analysts) should be the ones prepping it too. Organisations can’t expect to gain accurate analytics if data preparation is handled by only a few highly technical employees.

2. ETL vs Data Preparation: Mapping-Based process vs visualisation 

ETL tools are designed for IT teams to effectively handle well-defined data wrangling and business intelligence processes. But these mapping-based processes make it difficult to manage iterative and agile data preparation as well as exploration.

On the contrary, data wrangling or data preparation is powered by machine learning and HCI (human-computer interaction) which allows business users to seamlessly explore and prepare data. Data preparation solutions also offer powerful visualisations to make it easier for users to identify hidden patterns in data and make accurate business decisions.

3. ETL vs Data Preparation: Support for complex data

As the amount and complexity of data grow, there is a need for more sophisticated tools that can keep up with the complex nature of data. An ETL system is only effective when the data you have is structured, regularly updated, and batch-oriented. ETL systems start faltering when they are handling time-sensitive streaming data unless you can modify the system with custom programming. But even after tweaks, an ETL system can struggle to maintain a high availability and low latency.

While there are many commercially viable ETL tools with the capability of handling complex data, they still need longer learning curves and extra process implementations in order to make the data usable before it can be loaded. Moreover, it’s also important to note that ETL technology was never designed to be put into the hands of business analysts, it was for IT professionals.

Data preparation tools can not only handle complex data seamlessly with no extra tweaks, but its short learning curve and easy to use interface allows business users to prep and analyse data easily. While there are clear differences between ETL and data preparation tools, the right choice between them will depend on your business’s unique requirements and end-users. Just because ETL is a rather old technology doesn’t mean you should be abandoning it and embracing data preparation. Instead, you should identify how you can move your legacy system to successfully integrate data preparation tools and also decide where ETL tools fit in this new landscape. 

Both ETL and data preparation tools are used for data analysis in order to identify patterns, anomalies and trends in data. To help you build your knowledge about data analytics, Springboard offers a 6-month comprehensive data analytics career track program with 1:1 mentorship, project-driven approach and comes along with a job guarantee. Take a look at the syllabus and apply today!