Today, data plays a vital role in the operation of businesses, and data processing and integration processes are becoming extremely important. One of the essential tools used in these processes is ETL. Let’s look at ETL, why it is so critical to a business environment, and its main components and tools.
ETL is an acronym for Extract, Transform, Load, which in Polish means extract, transform, and load. It is a process that converts data from different sources into one consistent database or data warehouse.
- extract - extraction of data from various sources, such as databases, text files, APIs or applications,
- transform - data transformation involves cleaning, transforming, and enriching data to make it conform to the target data model or structure,
- load - loading the transformed data into the target database or data warehouse.
Why are ETL processes critical?
ETL processes ensure data quality, consistency, and access to up-to-date information. They enable:
- integrate data from different sources in one place,
- cleaning and standardizing data, making it easier to analyze and interpret,
- updating data in real-time or at specified intervals.
Components of the ETL process
The ETL process consists of three main steps:
- extraction (Extract)
The first step in the ETL process is extracting data from various sources. These sources include relational databases, text files, XML documents, web APIs, and spreadsheets. Extraction involves passing data from these sources to the transformation process. In this step, it is decided what data is needed and how it is extracted – whether in whole or only in part, based on specific criteria such as date or logical condition.
- transformation (Transform)
Another important step is data transformation. Data is subjected to various transformation operations in this process, including cleaning, normalizing, enriching, aggregating, or combining with other data. Transformation aims to ensure data consistency, correctness, and usability. For example, duplicates may be removed, dates formatted, text values converted to numeric values, or new columns created from existing data.
- loading
The final step in the ETL process is to load the processed data into the target database, data warehouse, or data store. This step stores the data in the target structure and formatting while maintaining consistency and integrity. The loading process can involve creating new tables, updating existing data, or generating reports on the results of the ETL process.
How does an ETL tool work? (Working with data using tools)
ETL tools enable the automation and management of ETL processes. With them, users can:
- define data sources and specify extraction rules,
- configure data transformations, define cleaning, normalization, and enrichment rules,
- specify target databases, data warehouses or stores, and load rules.
These processes are often visually modeled in graphical interfaces, making them easy to configure and manage.
Tools and technologies used in the ETL process
Various tools and technologies are used in the ETL process, such as:
- databases: Oracle, SQL Server, MySQL, PostgreSQL,
- ETL tools: Apache NiFi, Talend, Informatica PowerCenter, Microsoft SSIS,
- data processing platforms: Hadoop, Spark, Flink,
- cloud computing: AWS Glue, Google Dataflow, Azure Data Factory.
Advantages of using "ETL" tools
The use of ETL tools brings many benefits, such as:
- automation and acceleration of data processing and integration,
- improving data quality through data cleansing, normalization and standardization,
- increasing the efficiency and effectiveness of data analysis,
- enabling easier management and monitoring of ETL processes.
The most popular platforms/tools for data processing and integration
Numerous tools and platforms for data processing and integration are available on the market. Some of the most popular are:
- Apache NiFi: an open-source tool for data processing and distribution,
- Talend: a comprehensive ETL tool offering a graphical interface and support for various data sources,
- Informatica PowerCenter: an advanced ETL tool supporting many data sources and platforms.
ETL is a critical process in data processing and integration, enabling the effective management, analysis, and use of information in a business environment. Choosing the right ETL tools and technologies and using them skillfully can contribute significantly to a company’s success and competitiveness in a dynamic data world.
