ETL (Extract, Transform, Load) tools are software tools designed for extracting, transforming, and loading data from various data sources to a data warehouse or data lake in the cloud. These tools are essential for analyzing data and creating reports, dashboards, and AI models.
Development and evolution of ETL Tools
ETL tools for data extraction originated from the need to manage large volumes of data from disparate sources and transform it into usable formats for analysis and reporting. The concept of ETL was developed in the context of traditional data warehousing, where companies began to recognize the value of keeping historical and operational data in one central location for analysis and reporting.
Therefore it can be said that the first development process of ETL tools coincided with the birth of data warehousing. In the 1980s and 1990s, as business operations grew more complex and more powerful computing technologies became available, organizations began developing data warehouses to integrate, store, and analyze data from multiple sources. However, acquiring and preparing data for the data warehouse were often manual and labor-intensive processes.
To automate and simplify the data acquisition and preparation process, ETL tools have emerged. These tools are designed to simplify data extraction, transformation, and loading tasks by allowing developers to define workflows that automatically manage these processes.
In fact, in the early 2000s we already had a package of specialized applications for data extraction on the market. From this first generation, we have some examples of ETL tools that have reached the present day, starting from the first approaches up to evolving into the present day as important tools for data processing:
- Informatica PowerCenter: Informatica PowerCenter is one of the first and best-known ETL tools. It was introduced by Informatica Corporation and has become one of the favorite tools for extracting, transforming and loading data in many companies.
- IBM InfoSphere DataStage: IBM InfoSphere DataStage is another widely used ETL tool, developed by IBM. It offers comprehensive data extraction, transformation and loading capabilities, as well as integration capabilities with other IBM solutions.
- Microsoft SQL Server Integration Services (SSIS): SSIS is a component of Microsoft’s SQL Server, introduced with SQL Server 2005. It has been used by many companies to create ETL packages for extracting, transforming, and loading data.
- Oracle Data Integrator (ODI): Oracle Data Integrator was developed by Oracle Corporation for data integration. It offers advanced ETL capabilities, including parallel processing, metadata management, and data quality management.
- SAP BusinessObjects Data Services (BODS): SAP BusinessObjects Data Services, formerly known as SAP Data Integrator, is an ETL tool developed by SAP for extracting, transforming, and loading data from various sources.
In fact, over the years, ETL tools have undergone significant evolutions to adapt to technological changes and ever-evolving business needs. With the advent of cloud computing and the vast amounts of data generated by social media, connected devices, and other sources, ETL tools have evolved to handle these challenges, offering scalability, flexibility, and high performance. Many new application realities have been added to the existing ones:
- AWS Glue: AWS Glue is a fully managed ETL tool offered by Amazon Web Services (AWS). It allows you to extract, transform and load data in a scalable way, using cloud services such as Amazon S3 and Amazon Redshift. Glue also offers data cataloging and automatic ETL code generation capabilities.
- Google Cloud Dataflow: Google Cloud Dataflow is a fully managed data processing and ETL service offered by Google Cloud Platform. Use the Apache Beam programming model to define and execute data processing workflows in a scalable and reliable manner.
- Azure Data Factory: Azure Data Factory is a managed ETL tool offered by Microsoft Azure. It enables you to create data integration workflows that can extract, transform and load data from a wide range of sources and destinations, both in the cloud and on-premises.
- Talend Cloud: Talend offers a suite of data integration products, including Talend Cloud, a cloud-based ETL platform. Talend Cloud offers data integration capabilities, including tools for data extraction, transformation, and loading, as well as metadata management and cloud collaboration capabilities.
- Matillion: Matillion is an ETL platform built for cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake. It offers an intuitive block-based interface for designing ETL and f workflows
The three main phases of the ETL process
The Extract, Transform, Load (ETL) process is a systems approach for acquiring, transforming, and loading data from one or more data sources to a data warehouse, data lake, or other data storage destination.
- Extract: This phase involves extracting data from one or more data sources. Data sources can be relational databases, flat files, web services, APIs, Software as a Service (SaaS) applications, or any other resource that contains relevant data. ETL tools in Cloud Computing offer solutions to connect to these sources and retrieve data efficiently.
- Transform: After data extraction, it is often necessary to apply transformations to prepare the data for analysis. These transformations can include data cleansing, normalization, enriching the data with additional information, creating new columns, or combining data from multiple sources. ETL tools in Cloud Computing provide functionality to perform these transformations in an efficient and scalable manner, often using programming languages or visual interfaces for creating data transformation workflows.
- Load: Once transformed, the data is loaded into the data warehouse or data lake in the cloud, where it can be used for analysis and reporting. ETL tools in Cloud Computing manage the data loading process, ensuring data consistency and integrity during transfer, and storing data in a way optimized for query performance.
First ETL Phase: extraction from data sources
The first phase of the ETL process consists in extracting data from various sources. Over the years these sources have varied in both content and form. The first data sources were and still are relational databases (MySQL, PostgreSQL, SQL Server, Oracle and others). These are very widespread and are therefore a very common source of data: databases have in fact been used for decades to collect and store data. The data they contain is in relational and highly structured and ordered form, and therefore can be extracted in the form of tables, viewed through SQL queries.
Another very common form of data source is the one enclosed under the name of “Flat File”. These are files containing data in an ordered and structured form, such as CSV (Comma-Separated Values) files, Excel files or XML files. These have been used for many years, are very common and are so simple to use so much so that it was (and largely still is) the most common practice of collecting data by any person. In this case, data is extracted from files using specific parsers for each format.
In recent years, business technologies have changed and with the advent of the web and new business tools, data collection and storage has changed significantly compared to the two previous cases. Many organizations today use distributed SaaS (Software as a Service) applications that provide valid data collection, management and analysis platforms such as Salesforce, HubSpot, Google Analytics and many others. Data can be extracted from these applications using specific APIs or connectors provided by ETL tools.
But data collection is no longer primarily limited to data collection through human activity, and many of them have become fully automated and introduced into the reality outside of companies. With the advent of the Internet of Things (IoT), the entire world around us is littered with sensors and devices that continuously record data. This continuous and generalized data flow requires a particular approach, capable of selecting, extracting and analyzing raw data and converting it into higher-level information. It is clear that at this level the ETL process becomes more complex requiring the contribution of analysis and reporting tools, thus leading to a further evolution of the functionality offered by the ETL tools available today.
The latest evolution of the data source is Data Lakes. These are centralized repositories of raw, unstructured and semi-structured data. These are enormous amounts of data, often uncorrelated with each other, often without any correlation. Also in this case, ETL tools are able, thanks to new innovations in Big Data processing, to process and extract ordered and structured information from these masses of data which can then be processed for further analysis.
Second ETL Phase: data transformation
After collecting the data, we move on to the second phase: the ETL data transformation. In this phase, a variety of tools and techniques are used to manipulate and prepare data for analysis. In fact, the modalities are multiple and can be generalized into various groups. First, programming languages, in particular Python, Perl and R are used to implement complex data manipulations, programmatically. These languages offer flexibility and complete control over how data is transformed.
A more classic approach to data transformation, but much more limited than the previous case, is the use of SQL (Structured Query Language), specialized in performing data transformation operations from relational databases. In fact, SQL queries are able to filter, aggregate, merge and transform data directly during data extraction from databases.
Another more recent approach is to use visual ETL tools. In fact, many ETL tools offer among their services a graphical interface that allows users to design data transformation workflows using a visual representation of the transformation operations. These tools allow users to drag and drop components to perform transformation operations such as column mappings, filters, aggregations, and more.
Regarding Big Data, it is necessary to use specific data transformation tools. In recent years, a set of frameworks specialized in distributed data processing have established themselves. Apache Spark, Apache Hadoop, or Apache Flink provide powerful distributed processing capabilities for data transformation on large datasets. These frameworks allow you to scale data processing across clusters of machines and offer a variety of capabilities for data transformation and analysis.
Other methods of data processing and transformation that are becoming increasingly popular in the corporate world in recent years are business intelligence (BI) tools. Some BI tools offer capabilities for transforming data during the reporting and dashboard creation process. These tools let you apply filters, aggregations, calculations, and other transformations to your data to create meaningful and informative visualizations.
At the end of the transformation phase in ETL, the data will take on different structures depending on the specific needs of the company and the requirements of the application. Generally there are tabular and relational structures, or hierarchical formats such as JSON and XML. In more complex cases this data can be organized into more complex structures such as graph models, where data elements are represented as nodes and relationships between data are represented as edges. This format is useful for representing highly connected data, such as social relationships or infrastructure networks.
Furthermore, data can undergo aggregation or synthesis transformations, in which their complexity is gradually reduced to improve performance and focus the data towards certain objectives. Or in the opposite sense, the data can be integrated and enriched with additional information from other sources.
Third Phase: data loading
In the data loading phase in the ETL, the transformed data is loaded into a storage destination. This destination can be a data warehouse, a data lake, a relational database, a cloud storage system, or any other platform designed to store and manage large volumes of data. The choice of destination depends on the specific needs of the company and the type of analysis or applications that will be performed on the data.
However, lately traditional methods of data warehousing and relational databases are being integrated and replaced with the more modern services provided by Cloud Storage Systems: With the widespread adoption of cloud computing, many organizations use cloud storage services such as Amazon S3, Google Cloud Storage or Azure Blob Storage to store their data. These systems offer scalability, reliability and high performance for loading and storing data.