Data and Information produced globally on a daily basis are growing at a very fast rate. Statistics from 2023 says that about:
3.5 quintillion bytes of data is created per day,
5 billion Snapchat videos and photos are shared per day,
333.2 billion emails are sent per day,
Skype has 3 billion minutes of calls per day
This goes to show the vast amount of data being generated by billions of internet users everyday. And the custodians of the majority of this data such as social media companies, and big tech corporations need to find efficient and effective ways to move these data from one point to another, without breaking the system. ETL, in full, Extract, Transform, and Load, is a process through which this can be achieved.
In this article, we will explore ETL, especially what goes on at each stage of the process. Additionally, we will review why ETL is needed, where it can be used, and the most popular tools used by individuals and corporation to carry out this process. Let’s begin!
What is ETL?
Extraction, Transformation, and Load is the process of moving data from one source to a target source, such as a data warehouse. This data can be extracted from various sources, including but not limited to flat-file and SQL databases, and transformed into a desired format before being loaded into a data warehouse.
The extraction process in ETL entails gathering information from various sources. There are three major ways to obtain this data, namely:
Partial Extraction — without update notification: In this case, the source of the data fails to send an update when changes are made to the data, but it keeps track of what was changed and provides this information when requested. This is also known as incremental extraction.
Partial Extraction — with Update Notification: When a change is made, this data source sends an update notification that describes the change. It is the quickest way to obtain the information.
Full Extraction: Because the data source does not know what changes have been made, a full extraction of the data is required, and a copy of the previous extracted data will be kept to find out what changed.
Transformation occurs after extraction. The extracted data is loaded onto a staging area at this point. The staging area is important because it serves as a point of convergence of data from various data sources before it is loaded into the target area.
Various operations take place here, such as filtering out the columns not needed cleaning the data by removing null values and duplicates, and engineering new features from the existing data to ensure the data serves its required purpose. The transformation stage, in essence, prepares the data for the purpose for which it was extracted.
The final stage or process in ETL is the loading phase. Here, the cleaned and transformed data is loaded onto the target source. This procedure consists of several steps, the first of which is determining the target source or destination. A relational database management system, a data warehouse, or any other data storage solution can be used.
The schema creation process follows, which involves creating the structure of the database tables that will hold the data. This also includes the columns and data types that will be used to store the data. This is followed by mapping the schema to the target database, after which the actual loading can begin. If errors occur during this process, they can be corrected and the data reloaded, or they can be logged and reviewed later.
After the data has been loaded, it can be verified that it has been loaded correctly, which includes data validation to ensure that the data is accurate and complete. Following this procedure will ensure that the transformed data is properly stored and accessible to other applications and end users.
Why Use ETL?
ETL is important for the following reasons:
1. Data Migration: ETL can help organizations move or migrate data from one platform to another. This is particularly useful when a company wants to move data from an on-premise location to the cloud. ETL is needed to ensure that the data uploaded is correct.
2. Data Quality: With varying and different data in various data sources, ETL provides the benefit of preserving or ensuring data quality by removing duplicates, correcting errors, and standardizing data across the board.
3. Legacy System Integration: Legacy systems are obsolete forms of hardware or software that cannot interact with modern applications but are still useful for day-to-day operations. In this case, ETL can assist in migrating data from these systems to more modern applications or databases for more efficient use.
4. Business Intelligence: Bringing data from various sources and transforming it correctly can provide executives and business leaders with consistent stream of insights, particularly when the process is automated.
Where ETL Can Be Used?
ETL can be used in industries or sectors where data integration from multiple sources is critical. Some of these sectors are:
1. Healthcare: ETL can be used in healthcare to integrate data from various sources such as electronic health records (EHRs), medical devices, and other sources in order to provide healthcare practitioners and providers with a comprehensive view of their patients’ health and improve their outcomes.
2. Retail: Due to the large number of retail stores and outlets, both offline and online, which can be formal or informal, the retail industry is extremely competitive. ETL assists in the integration of data from POS systems, e commerce platforms, and customer relationship management (CRM) systems in order to gain insights into customer actions, optimize inventory management, and improve sales performance.
3. Education: This sector is critical to the development of every nation, and by using ETL to bring data from various sources such as student records, learning management systems (LMS), and assessment tools, we can gain insights into students’ performance, identify areas for improvement, and provide personalized learning experiences.
4. Transportation and Logistics: Timeliness is a very important characteristics in the transportation industry, and by integrating data from disparate sources, such as GPS tracking, supply chain management systems, and shipping schedules, you enable businesses to optimize shipping routes, improve delivery times, reduce costs, and improve overall customer experiences.
Many ETL tools are available on the market, but the following are some of the most popular and widely used:
Informatica PowerCenter is a data integration software. Informatica PowerCenter is a well-known ETL tool that allows users to extract data from a variety of sources, transform it, and load it into a target system. It has functions like data profiling, data mapping, and workflow management. It also includes connectors to various storage solutions, data warehouses, and lakes, such as Google Cloud, AWS, and Azure.
Talend: Talend is an open-source data integration tool that offers a complete set of tools for data integration, data quality, and data management. Talend provides a comprehensive set of features and functionalities that enable users to extract data from a variety of sources, transform the data using a visual interface or custom code, and load it into a target system. It is available in both open-source and commercial versions, with the commercial version having more features.
Amazon Redshift: Amazon Redshift is an AWS (Amazon Web Services) cloud-based data warehouse service that allows users to store and analyze large amounts of data in a cost-effective manner. Amazon Redshift includes a built-in ETL tool called Amazon Redshift Spectrum in addition to data storage. Amazon Redshift Spectrum enables users to extract data from a variety of sources, including Amazon S3, transform it using SQL, and load it into a target system. It also has features like automatic compression, columnar storage, and backups. CSV, Parquet, ORC, and Avro are among the data formats supported by Amazon Redshift Spectrum.
IBM InfoSphere DataStage: IBM InfoSphere DataStage is a high-end ETL tool that offers a comprehensive set of tools and functionalities for data integration, transformation, and management. It is intended to handle large amounts of data ranging from gigabytes to terabytes and can process data from a variety of sources such as databases, cloud-based services, and flat files. It is available in both on-premise and cloud versions, with the cloud offering scalability and elasticity.
To summarize, ETL is a critical process in data management and engineering that every beginner should understand. In this guide, we covered the fundamentals of ETL, such as what it is, why it is important, and how it is used. We also talked about the various tools available to help with ETL, such as Amazon Redshift, Talend, and IBM InfoSphere DataStage.
Understanding ETL is critical to success in the world of data engineering, whether you are a beginner or an experienced data professional. We hope that this guide proves insightful as you begin your journey with ETL and advance your data engineering skills.
That’s all for now. Stay tuned for more data hacks to come!