Extract Transform and Load


In this article, I’m going to present a key part of Business Intelligence (BI) process: Extract, Transform and Load (ETL).

ETL is the ordered sequence of steps which retrieves data from production database, prepare it to be useful for analysis and finally store it in a separated database for decision making tasks.

Motivation and Description

The competition for market share between companies has increased, which demands improvements on business processes and strategies.

Without it, the strive to survive will be useless, hence the company has a considerable chance of bankrupt. One manner to achieve this improvement is by the introduction of information systems, which automates business processes. For example, instead of having a physical store to sell clothes, a company might develop its e-commerce to do it at internet. Through it, the customer can buy clothes, share his purchases with friends in social networks, therefore spreading the company’s name and market value too.

In the beginning of these systems, information was viewed as a result of the productive process and had the only purpose to support it. In the e-commerce example, the clothes purchased by a customer were only used by the logistic system to deliver them. Hence it hadn’t any value for business planning.

This conception has changed in the recent years and information is now an asset and should be treated and used as such.

Returning to our e-commerce example, the registered purchases can now be used to analyse customers preferences, thereby developing specific services, like special offers, focused always on customers needs. The use of information for business planning, is known as BI.

However, this objective of using information as a guide for decision making, depends on having the proper systems to consume this information obtained from productive process and manipulate it, generating valuable knowledge, which can be studied by the business’s planners.

In this context, comes up Data Analytics as a relatively new field of study, which aims to extract useful knowledge for decision making from the huge amount of data produced by daily productive process. Nonetheless, first we need retrieve the data from somewhere, clean it and store in the data warehouse which is a separate database for the use of the data in BI.

Extract, Transform and Load

Most of the time, we need adapt our resources to our needs, this is not different in Information Technology related fields.

In this way, database are commonly made to perform well in the daily tasks processed by information systems: maintain consistency in data, avoid redundancy to save disk space etc. The most important kind or arrangement to achieve it is the well known Online Transaction Processing (OLTP) model. Basically, a database which follows OLTP, is very good to execute a huge amount of queries that return few registers, mainly find by primary key.

Meanwhile, in BI we’re looking for execution of few queries which returns a huge amount of registers. The generally considered standard model for this kind of task is Online Analytical Processing (OLAP).

Therefore, we need to translate the OLTP data made in production process to the OLAP data necessary to business planning process. Here, arises the ETL, which consists on three sequential steps:

  1. Extract — Fetch data from production database, applying filters based on the project needs

For the ETL, there are some tools which help in the process, one of them is the Pentaho Data Integration (Kettle) [2] and it’s the chosen one for illustrate this article.


For this example, I intend to introduce the Kettle as a tool for ETL, but to keep it simple, the example will not interact with any relational database. Instead, it’ll fetch data from a Comma Separated Value (CSV) file, filter it, aggregate and write the result in another CSV file.

I’ve tested this example using the Kettle at version 4.2.0-stable, but you can try it in a more recent version and make the necessary modifications.

The scenario is:

We have a CSV of the amount of sales made by some sellers represented by codes, these sales are separated by years. Our goal is to summarise the amount of sales by year for the sellers A and B. This scenario easily scales for a BI typical task which is consolidate huge amount of sales data by period in dashboards that permit drill down in more granular periods like months or weeks.

Kettle is basically a Java tool that executes parallel processing of data.

Firstly, we have a transformation which is the group of elements built for the data processing.

Secondly, the data flow like a wind flux from the beginning of the transformation until its end.

Finally, every element in Kettle is one of two possible kinds, they’re: steps and hops. Steps are the ones responsible for processing the incoming data flow and producing another data flow as output and two, or more, steps are connected with each other by hops.

We have many built-in steps, like filters, database lookup, group by, sort and in addition, a simple way to customize the transformation behaviour adding Java or JavaScript code inside custom steps.

In the example, we have the transformation made by the steps:

  1. INPUT — Reads the input CSV file of sales information


In this article we’ve learned what ETL is, its importance in business and we used Kettle as a tool for ETL.

The full example containing the input, transformation and output can be obtained here.


[1] GROSSMANN, W. and RINDERLE-MA, S. Fundamentals of Business Intelligence. 1E. Published By Springer-Verlag Berlin Heidelberg.

Software Engineer interested in C++, Rust, Haskell, Scala, Go, C, Python , Linux, functional programming, system programming, tooling, IoT, cloud, math, etc.