What Is ETL and How Does It Work?

Key takeaways

ETL extracts, cleans, and loads data from sources, providing organizations with accurate data for analysis and supporting reliable data pipelines.

• ETL is central to modern data integration, and demand is rising alongside the growth of big data and real‑time analytics. 

• Effective ETL pipelines improve data quality by cleansing, validating, and standardizing information before it reaches analytical systems.

• You can use ETL to consolidate data from multiple sources, enabling clearer insights for decisions across marketing, machine learning (ML), Internet of Things (IoT), and cloud environments.

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What is ETL?

Extract, transform, load is a three-step process that retrieves data, transforms and improves it, and delivers the consolidated data into a data warehouse. The ETL process consolidates data, enabling businesses to analyze it effectively and derive insights. This helps your organization use various data sets to make data-driven decisions. This could include understanding consumer behavior, tracking inventory, or making product or sales decisions. Essentially, the ETL approach to data can hasten your comprehension of the information because, according to Forbes, “The most efficient method for extracting data is a process called ETL . . . The only alternative to ETL is manual data entry—which can take literal months, even with an enterprise amount of manpower. Save yourself the trouble by getting a grip on the ETL process” [1].

How does ETL work?

ETL commonly moves your data from source systems to a data warehouse or repository for analysis, reporting, and business intelligence purposes. This section explains each step in the ETL process.

Extract

This step collects data from various source systems, including databases, spreadsheets, cloud-based platforms, and files. The data is in its raw form, usually in large batches or portions.

Transform

Next, the data transforms to ensure it is clean, consistent, and usable. Transformations involve tasks such as:

• Data cleansing

• De-duplicating data

• Data validation and authentication

• Enriching data by performing calculations or translations

• Summarizing raw data in standard formats for consistency

Load

After extracting and transforming the data, the process loads it into a target system, such as a data warehouse or a database optimized for analytical queries. This generally occurs during hours of low traffic. Loading occurs incrementally, during regular intervals, or all at once during a full load. A full load tends to happen when data is initially loaded into a data warehouse. 

Why is ETL important?

The ETL process is fundamental in data integration, especially when dealing with large volumes of data from various sources. It enables your organization to consolidate data from disparate systems, clean and prepare it for analysis, and make it available for reporting and visualization.

With the growth of big data, real-time data processing, and advanced analytics, ETL is crucial in enabling organizations to utilize their data for business insights and decision-making processes effectively.

Uses of ETL

With the advancement of technologies and the increasing need for real-time data processing, ETL processes help meet the demands of modern data-driven applications. ETL is vital in enabling data integration, quality improvement, and efficient data processing.

Data warehousing

ETL plays an essential role in data warehousing by gathering data from multiple sources for your organization, transforming it into a consistent format, and loading it into a central repository (the data warehouse). The ETL process delivers clean, standardized, and integrated data required for accurate and comprehensive business insights.

Machine learning (ML) and artificial intelligence (AI) 

In ML and AI, ETL is crucial in preparing and preprocessing data for model training and evaluation. The raw data can come from diverse sources and in various formats. ETL processes clean, preprocess, and transform this data into quality data, which can help make more accurate and reliable AI and ML algorithms.

Marketing and Internet of Things (IoT) data integration

Marketing campaigns often involve data from various sources, such as social media, customer interactions, and web analytics. ETL processes enable marketers to consolidate and analyze this data to gain insights into customer behavior, preferences, and campaign effectiveness.

Similarly, in the IoT context, ETL collects, processes, and integrates data from a wide range of connected devices for monitoring, analysis, and decision-making.

Database replication

When data needs synchronization across multiple databases, ETL tools keep the data consistent. This helps ensure data integrity and disaster recovery while countering latency issues.

Cloud migration

When organizations migrate their data and applications to the cloud, ETL processes often extract data from on-premises systems, transform it, and load it into cloud-based databases or data warehouses. This helps maintain data integrity and usability.

Is Excel an ETL tool?

Excel can serve as an ETL tool, particularly with features such as Power Query. It can pull data from multiple sources, clean and reshape it through repeatable steps, and load it into other systems. For some teams, Excel offers an accessible, familiar way to manage end‑to‑end ETL tasks.

ETL vs. ELT

ETL (extract, transform, load) and ELT (extract, load, transform) both move and process data from source systems to target systems like data warehouses or databases. However, they differ in the sequence of their main steps and the focus of their processing.

ETL performs transformations before loading data into the target system. ELT loads data first and then performs transformations within the target system. This means ELT can offer more flexibility in handling raw and unstructured data, as it does not require you to build additional infrastructure for efficient transformation. This also gives ELT a scalability advantage. Finally, although transformations occur faster with ELT, you might find that your ability to query and analyze the data is slower because the transformation stage is taking place within the target system.

Read more: What Is Metadata Management?

Future uses of ETL

The need for efficient access to real-time and reliable data grows because of the insights that information can provide your organization. This leads to many potential future uses for ETL processes.

ETL processes could support real-time data processing and analytics from continuous data streams. Certain applications need to function in real-time, and ETL built for a steady influx of information can assist with real-time payment processing, streaming analytics, and edge computing.

ETL could become more user-friendly and accessible to business users, allowing them to create and manage their own ETL processes without extensive technical knowledge. This could see data owners within an organization defining and managing their data pipelines.

You may also see more ETL processes tailored to cloud environments or designed for hybrid (on-premises and cloud) resources. Finally, because the sheer amount of data available continues to expand, the ETL process will most likely become even more automated so that organizations can sort and analyze data faster.

Explore our free resources for data-driven professionals Join Career Chat on LinkedIn to stay current with the latest trends in your career field. You can also continue your learning journey with data analytics with our other free digital resources:

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