ETL: what is it and what are the most commonly used tools?

ETL is a term we find we hear frequently in the world of data management and analytics. Data management has become a basic pillar of business operations and decision making. And, in this context, the ETL becomes a fundamental process. A model that has revolutionised the way in which companies around the world obtain data and manage information to make informed decisions. strategic decisions. But do you know what ETL is for and what it really is?

In this article, we explain in detail how the ETL system works and which tools are most commonly used for process automation, data integration and the creation of efficient workflows.

ETL: what is it and what does it mean?

The acronym ETLwhich stands for Extract, Transform and Load, refers to a critical process in data management. A systematic approach used to move data from different sources, transform it into a useful format and load it into a centralised data warehouse, such as a data warehouse or data warehouse. The aim is to ensure that it is accessible, consistent and ready for analysis.

The extraction involves collecting data from a variety of sources, which may include databases, file management systems, applications, web services and more. An essential step to ensure that the most important data is collected efficiently.

What is the ETL process for?

You know what ETL means and what it is, but do you know what it is for? The ETL processes act as a vital bridge between various data sources and business decision making, facilitating data management, data cleansing and data preparation to maximise the value of information. As a result, the following benefits are achieved:

• Integration of data from multiple sources.
• Data cleaning and structuring.
• Preparation for analysis, eliminating irrelevant data and aggregating information.
• Improve query performance.
• It automates processes, saving time and reducing errors.
• It facilitates decision making based on sound data.

ETL processes: examples

As mentioned above, the close relationship between ETL and data facilitates decision making and efficient information management, which has a positive impact on the business.

There are several ETL examples which show how companies and organisations use the ETL model to collect data from different sources, transform it into useful formats and load it into centralised data warehouses. From data migration to the integration of dispersed information.

• Data migration: One ETL case that we may encounter frequently is data migration. For example, when a company decides to change management systems, data may need to be extracted from the old system, adapted to the new system and loaded onto the new platform.
• Data integration: Often, data is scattered across different systems. In these cases, ETL development can provide us with a database The use of a unified system to integrate them into a single warehouse considerably facilitates analysis and decision making.
• Processing of sales records: an online shop collects sales records across different platforms. By using ETL processes, we can have all data in one format, which makes it easier to analyse sales trends, for example.

What are the most commonly used ETL tools?

In a world where companies are constantly receiving information from multiple sources, the tools for ETL have become a fundamental pillar for managing data, implementing procedures efficiently and making agile decisions. In this way, we can be sure that data is efficiently collected, transformed and loaded into unified databases.

Today, a wide range of ETL tools can be found, ranging from open source platforms to data visualisation tools. The choice will depend on the specific needs of each project and the technological infrastructure available. Some of the most commonly used are the following:

• Apache NiFi: an open source tool that enables the automation of data flows between systems. Very popular for data collection and transformation.
• Talend: an ETL tool that is very useful for data extraction, transformation and loading. Highly valued for its integration and automation capabilities.
• Apache Spark: although primarily used for Big Data processing, it can also function as an ETL tool for large-scale data transformation.
• Informatica PowerCenter: offers interesting features for data integration and data transformation.
• Microsoft SQL Server Integration Services (SSIS): integrates natively with the Microsoft SQL Server environment. Widely used in Windows environments.
• Pentaho: an open source application with a wide range of extraction, transformation and loading options. Highly appreciated for its high level of customisation.
• AWS Glue: a service fully managed by Amazon Web Services that facilitates the creation and management of ETL workflows in the cloud.
• Google Cloud Dataflow: an online Google Cloud service that enables the creation of large-scale data transformation workflows.

Become an expert in Data Science thanks to IMMUNE

As we have seen, the ETL processes play a fundamental role in data management and analysis. From extracting data from various sources to transforming and loading it into a centralised database, these tools are essential to ensure that data is always available and ready to be analysed. If you are interested in the world of Big Data and want to dedicate yourself to it or continue your training to climb up the career ladder, our Data Analysis, Mining and Visualisation Techniques in Data Science Course will enable you to know and understand the essential steps to extract relevant information to make informed, evidence-based decisions. Give your career a boost and become an expert in Data Science!

If you are looking for technology training fill in the form for more information.