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    What is ETL | Meaning, Basics, and Processes


    ETL has become the staple of any business that stores and manages data. As the amount of data grows in an exponential manner, the need to make sense of data grows equally. ETL, which is the process by which businesses extract data from multiple sources, transform them into appropriate formats, and load to target destinations, has become more important than ever.

    Table of Contents

    1. What is ETL

    2. How does ETL work

    3. The History of ETL

    4. What is ETL Pipeline

    5. ETL vs. ELT

    6. Benefits of ETL

    7. Building your ETL Strategy


    What is ETL?

    ETL stands for extract, transform, and load. It’s a data integration process, where data is extracted from multiple sources, then transformed into a usable resource, and finally loaded to a system that people who need them can access.

    The process of ETL can be summarized as below:

    • Extract data from multiple systems and sources
    • Cleanse and transform data to improve data quality
    • Load the data into the target database

    Based on a sequence of rules, ETL helps cleanse and organize data to support business. The most basic use case is monthly reporting. But more than that, ETL is the foundation of the so-called advanced data analytics and machine learning. Modern enterprises rely on ETL to automate back-office processes, forecast future trends, and improve customer experience.

    How Does ETL Work?

    As its name suggests, ETL is composed of 3 steps: Extract, Transform, and Load.


    The first step of ETL is Extract. In this, data engineers would extract data from a variety of sources, whether they are structured or unstructured. Some typical data sources are ERP or CRM systems, APIs, marketing tools, transaction databases, and many more. 

    3 typical Data Extraction methods are:

    1. Partial Extraction  the source system can automatically send notification any time a record has been changed
    2. Partial Extraction with update notification  The source system cannot notify of new updates, but is able to point to those records and provide an extract of those records.
    3. Full extract  The source system cannot identify which data has been changed. If this is the case, a full extract is the only way to extract data out of the system.


    Transform means transforming the data that was extracted into a format that can be accessed by different users and systems. In Transform, data engineers would cleanse, map, and transform data, often to a specific schema, to serve specific operational needs. This second step of ETL involves several data transformation methods so as to ensure data quality and integrity.

    • Cleansing — resolving inconsistencies and missing values in the data. 
    • Standardization — applying formatting rules to the data set.
    • Deduplication — discarding redundant data.
    • Verification — removing unusable data and flagging anomalies.
    • Sorting — organizing data according to type.
    • Other tasks — applying more rules if needed to improve data quality.


    Finally, the data that has been transformed will be converted to a target database. But before that, the data typically is uploaded to a staging database in case something does not go as planned. The load can either be simple or complex depending on the nature of the applications.

    There are typically two approaches to loading in ETL.

    • Full loading — everything that comes from transformation assembly goes into new, unique records in the data warehouse. Even though sometimes full loading can be useful, especially for research purposes, mostly it unnecessarily produces more data sets that are difficult to maintain.
    • Incremental loading — Incremental loading This architecture compares incoming data with what’s already available. Incremental loading only produces additional records if there is new and unique information. This type of loading is less comprehensive, thus is easier to main and manage.

    The history of ETL

    ETL became popular in the 1970s when enterprises have more and more data repositories. There’s an urgent need to integrate data across all these disparate databases. Thus, ETL quickly becomes the sought-after method for gathering data from multiple sources, transforming them for consistency, and loading them to a target destination.

    It was not until the late 1980s and 1990s that the use of data warehouses started to gain ground. As a type of database, a data warehouse allows multiple systems to access the same source of data. But different departments were using different ETL tools for different data warehouses that were not integrated with each other. 

    Today, there are myriad data formats, data sources, and data systems. ETL is now just one of many methods that enterprises use to gather, cleanse, and process data.

    What is ETL Pipeline

    We can talk about ETL without talking about ETL pipeline.

    An ETL pipeline is the manner in which ETL occurs as we know it. In other words, an ETL pipeline involves a set of tools and activities for migrating data from one system to another where it can be used and managed in a different way.

    An ETL pipeline can automate the extraction of data from disparate sources, then transform and consolidate that data in one high-performing data storage.

    ETL vs ELT

    Obviously, ETL differs from ELT (Extract, Load, and Transform) in the order of processes. Similar to ETL, ELT also starts with Extract, where the data is copied or exported from a data source. But after that ELT loads the raw data to the target data storage, instead of the staging environment for transformation. Finally, ELT would transform the data in the target data storage.

    Another similarity is that both ETL and ELT can deal with numerous data repositories, such as databases, data lakes, and data warehouses. But each of them has different use cases.

    The traditional ELT is more ideal when it comes to high-volume and unstructured datasets because it loads data directly from the original source. Meanwhile, ELT is more popular for big data projects as it does not require much planning in terms of data extraction and data storage. 

    On the contrary, ETL needs a lot of planning at the beginning. Data points need to be identified for extraction across disparate data sources. After that, there’s still a need to construct business rules for data transformation.

    And even though ETL has been used more widespread thanks to the increasing adoption of the cloud, it’s still a new method. This means that there are currently no best practices for ELT.

    Benefits of ETL

    The data sources are more numerous, and the data itself is becoming more complex than ever before. ETL makes it possible for businesses to make sense of that vast volume of data.

    Just imagine the amount of data that an e-commerce site has to work with. Or data of millions of transactions a bank processes every day. This is not to mention the data coming from marketing, sales, logistics,... Just a day goes by and all that data can become obsolete. They must be extracted, transformed, and loaded into a new destination for analysis. 

    ETL is essential to today’s businesses because of the following reasons

    ETL provides a single point-of-view

    It takes long hours and a squad of data professionals to manage multiple data sets. Even that,  inefficiencies and delays are sometimes inevitable. ETL consolidates disparate data sources and various data formats into a single view. Thanks to this, businesses can easily analyze, visualize, and makes sense of a large amount of data.

    ETL provides historical context

    With ETL, businesses can combine historical data with new data from other platforms and applications. This offers a historical understanding of the data, where older data sets can be analyzed in parallel with the latest knowledge.

    ETL improves efficiency and productivity

    There are specialized tools that can automate, or at least fast-track the traditional code-intensive ETL process. Data engineers can spend more time on strategic works, and less time on writing code to move and format data.

    Building your ETL strategy

    ETL can be done in either of these two ways: by human developers or specialized tools.

    In the first case, businesses may employ data professionals who are dedicated to building ETL. However, this approach can be time-consuming, error-prone, and expensive.

    More companies are making ETL an integral part of their data processes. In most cases, ETL tools are faster, more reliable, cheaper than human engineers. In addition, they are designed to work seamlessly with a broad range of systems and applications. ETL tools also have built-in data quality and data governance features.

    There are many factors you want to consider when evaluating ETL tools. The first is about the number and categories of connectors your business will need. The second is who will use the tool: your engineers or your business users? Another important factor is the type of tool. Most of today's ETL solutions are either open-source or commercial. While open-source tools are free, they often come with a steep learning curve, ideally for developers with scripting knowledge. On the other hand, commercial ETL tools are easy-to-use and can be customized to fit your business' needs.

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