Learn how ETL facilitates the organized collection and transformation of data.
The acronym ETL stands for “Extract, Transform, Load.” It is a crucial process in the world of information technology and data management. ETL is used to collect data from various sources, transform them into a suitable format, and load them into a target system, such as a data warehouse or a database, so that this data can be analyzed and used for decision-making.
What is ETL?
ETL is a fundamental process in the field of Data Analytics and database management, especially when dealing with large volumes of information from various sources. The goal is to prepare the data in a way that it becomes useful and reliable for subsequent use.
ETL can be manual, semi-automated, or fully automated, depending on the complexity of the data and the available tools. Currently, there are specialized tools called ETL tools that facilitate many aspects of this process.
The ETL process is crucial to ensure that the data used in analysis, reporting, and decision-making is clean, consistent, and ready for use. This is especially important when dealing with large volumes of information from various sources, as the process enables organizations to gain meaningful insights from this data, providing a solid foundation for strategies and actions based on reliable information.
The ETL steps
ETL is divided into three main steps, let’s understand each of them:
- Extract: In this stage, data is collected from various sources, which can include databases, spreadsheets, application systems, CSV files, APIs, and others. Extraction involves retrieving relevant information from the original sources and preparing it for the next phase.
- Transform: After extracting the data, it often needs to be cleaned, organized, and transformed into a consistent and suitable format for analysis. This may include removing duplicate data, correcting errors, standardizing formats, converting units, aggregating, and creating new derived attributes. Transformation aims to prepare the data for better understanding and interpretation.
- Load: Here, the transformed data is loaded into a target repository, which can be a data warehouse, a relational database, or another storage infrastructure. The loading process ensures the integrity and consistency of the data in the target system. Depending on the requirements, data can be loaded incrementally (only with new or changed data) or completely.
ELT and ETL: understand the difference
In essence, ELT and ETL are approaches related to data integration and processing, but they differ in the sequence of steps and how data is handled. Let’s understand the difference between the two processes:
ETL (Extract, Transform, Load)
In the ETL process, as we saw above, data is first extracted from the source(s) to an intermediate location, often referred to as the “staging area.” After the extraction step, we move on to transformation. Now, the extracted data is cleaned, enriched, and prepared to meet analysis requirements. This transformation takes place at the intermediate stage. Then, it’s time for loading when the data is loaded into its final destination, which is typically a data warehouse or a database optimized for analysis.
ELT (Extract, Load, Transform)
Just as in ETL, the process begins with extraction: data is extracted from the source(s). However, we now move to the intermediate stage. Instead of transforming the data immediately after extraction, the extracted data is loaded directly into the final destination, which is typically a data lake or a data warehouse. The transformation step occurs after the data has been loaded into the destination. The data is transformed and prepared for analysis within the storage environment.
In other words, the main difference between ETL and ELT lies in the sequence of transformation and loading steps.
ETL or ELT, which one to choose?
The choice between ETL and ELT depends on the organization’s needs, data volume, the technologies involved, and the characteristics of the data analysis workflow.
ELT is often used when dealing with large data volumes and when the final destination supports efficient transformations, as in modern data warehouses. On the other hand, ETL is traditionally used in scenarios where complex transformations need to be performed before loading data into a system optimized for analysis.
However, with the advancement of data storage and processing technologies, the distinction between ELT and ETL may eventually become less rigid, with overlapping approaches.
ETL in the cloud: how does it work?
As technology in ETL has evolved, both the diversity of data types and the multitude of sources have exponentially increased. The rise of cloud computing has enabled the creation of extensive data repositories, also known as data collectors, capable of receiving information from multiple sources.
As a result, ETL tools have become more sophisticated, operating in conjunction with these modern data collectors. This allows the processes to convert older legacy data formats into contemporary formats, utilizing modern data storage, as shown in the following examples:
Data warehouses
A data warehouse is a system designed to work with data prepared for analysis and is capable of storing historical and accumulated data. It can integrate data from various sources, and being a non-volatile storage solution, it is an excellent choice for companies looking to work with data analysis, studying changes and trends in a historical series. Currently, there are cloud-based data warehouse solutions that offer excellent performance and provide more agility and scalability in processes.
Data lakes
With a data lake, it becomes possible to store structured or unstructured data in a centralized repository, accommodating any necessary scale. Unlike a data warehouse, data can be preserved in its original format, without the need for structuring based on potential future queries. Data lakes also provide the capability to perform various types of data analysis, including SQL queries, Big Data analytics, full-text searches, real-time analytics, and Machine Learning (ML), aimed at guiding more informed decisions.
Use cases
The ETL process plays a crucial role in consolidating all relevant information into a single point, making it ready for analysis. This empowers executives, managers, and other stakeholders to base their business decisions on solid data.
But how is this process used in the day-to-day operations of businesses? ETL is typically employed to fulfill the following functions:
Data storage
Data storage encompasses gathering data from various sources into a unified repository, enabling joint analysis for business purposes. ETL is commonly used to transfer this data to a storage repository.
Machine Learning and Artificial Intelligence
Machine Learning is an approach that imparts meaning to data without requiring explicit programming of analytical models. Instead, the system extracts insights from the data through Artificial Intelligence techniques. ETL finds application in centralizing data for Machine Learning purposes.
Marketing Data Integration
The consolidation of marketing information involves unifying data from various sources such as customers, social media, and web analytics metrics into a single environment. This enables analysis and the formulation of future plans, and ETL plays a crucial role in collecting and preparing this data.
Data integration in the Internet of Things (IoT)
The Internet of Things (IoT) encompasses the interconnection of devices capable of collecting and transmitting data through embedded sensors. These devices include industrial equipment, network servers, smartphones, and a wide range of other machines, including wearables and embedded devices. ETL is employed to centralize information from multiple IoT sources, enabling its analysis.
Database replication
Database replication involves extracting data from source databases, such as Oracle, Cloud SQL for MySQL, Microsoft SQL Server, Cloud SQL for PostgreSQL, MongoDB, among others, and then transferring this data to cloud storage. This process can be one-time or continuous as data is updated, with ETL being used to perform this replication.
Migration to the cloud
Companies have been migrating their data and applications to cloud environments to optimize costs, scalability, and data security. ETL also plays a crucial role in facilitating these migrations.
In an increasingly data-driven world, ETL becomes a fundamental piece for extracting significant value from diverse information.
The benefits of the process are impactful in the current data analysis and decision-making landscape. The ability to transform raw data into valuable insights is an essential competitive advantage, enabling organizations to thrive in a dynamic and constantly evolving business environment.
