Introduction
Data is the new gold. Data drives business in organizations. As the amount of data, data sources and data types in organizations grow, there is importance in making use of this data in analytics to derive business insights. This lead to the emergence of data engineers who process the raw messy data into clean, fresh and reliable data ready for business use.
What is ETL?
ETL stands for Extract, Transform and Load.
This is a process that data engineers use to extract data from from different sources, transform the data into a usable and trusted resource and load that data into the systems end where users can access and use downstream to derive insights and make business decisions.
How does an ETL work?
Extract
The first step is to extract data from different systems such as business systems, APIs, sensor data, databases and others. Different systems have differently structured outputs. There are different ways to perform extraction:
1. Partial Extraction: This is where source systems notify you of when a record has been changed and thus only extract that new record.
2. Full extraction: There are source systems that are not able to identify which data has been changed at all. In this case, a full extract is done and utilizing a copy of the last extract in the same format, changes that have been made can be identified.
Transform
The second step consists of transforming the raw data that has been extracted from sources into a format that can be used by different applications. Data is cleansed, mapped and transformed often to a specific/standardized schema to meet operational needs. At this stage, the data goes through several types of transformations to ensure quality and integrity of the data.
Load
After data quality an integrity has been met, it is then loaded into a data storage facility eg. Database where it can be accessed by other users such as applications, analysts or management.
Example: A retail stores with multiple stores across different regions collects daily sales data and the end of the day. The company extracts raw/messy transaction data from the point-of-sale (POS) system which includes details such as products, quantities sold and store locations. After extracting this data from the POS system, the company transforms the data by cleaning and standardizing it by doing activities such as removing duplicate records, correcting missing values and converting all timestamps into uniform format. Finally the cleaned data is then loaded into a data storage system where it can be accessed by other users such as analysts and use it to generate reports and dashboards to show daily revenue, top-selling products or regional performance. With this, the company can make better decisions.
What is ELT?
ELT stands for Extract Load and Transform.
In ELT, data is first extracted from the source systems, loaded directly into a data storage systems such as a database and then transformations are done inside the data storage systems.
How does ELT work?
Extract
Similar to an ETL, data is pulled from source systems(extraction).
Load
Instead of going to a staging server/storage, the raw data is loaded directly into the target data storage system.
Transform
Once the raw data is inside the data storage system, it is transformed using SQL or specialized tools. The raw data is often preserved, while transformed versions are created for analysis.
Example: A financial company utilizing mobile applications, ATMs and online banking systems collects all this large volumes of transactions data and instead of cleaning the data immediately, they first extract all the raw transactions logs as they are and store them (load) in a data storage systems such as a cloud server. Once the raw data is stored centrally, the company performs transformations the data in storage. This can be in activities such as data cleaning by removing and fixing missing values, standardization of formats such as currencies and timestamps and joining with other data sets such as fraud watch lists. Since transformation happens after loading, the company is able to retain the original raw data.
ETL vs ELT
So what is the difference between an ETL and ELT?
The principal difference is in the order of operations. ETL stands for Extract, Transform, Load, meaning that it involves extracting data from the source first, transforming it into a usable format in a staging area and then transferring the usable data into a storage system where it can be accessed for analysis.
Well this has been the standard in data processing for decades. With modern data storage capabilities, ELT as a processing option was introduced. ELT stands for Extract, Load, Transform, meaning that data is loaded into a storage systems as soon as it extracted and then transformed into a usable format as needed directly from the data storage system.
Key differences between ETL and ELT and how they affect data processing?
1. Availability: In an ETL, data must be defined and transformed before storage meaning that only selected processed data is available while in an ELT, all raw data is stored first making it fully available for later use.
2. Flexibility: With ETL, changes require redefining the transformation logic while with ELT, data can be processes in different ways anytime as the original raw data is always available.
3. Scalability: With ETL its is harder to scale due to the upfront transformation costs while with ELT, especially in cloud environments, scalability is easy allowing ELT to handle large and growing data volumes efficiently.
4. Speed: ETLs are slower at start and faster for analysis due to the transformation of data before storage while ELTs are fast during extraction but slower when doing analysis as the data needs further processing.
5. Storage: ETLs consume less storage space as they only store processed data while ELTs require more storage as all the raw data is being stored.
Real-World Use Cases
Choosing between ETL and ELT often depends on aspects such as the specific industry an organization is in, data volume, and regulatory environment.
Below are common real-world applications for each:
ETL Use Cases
1. Retail Inventory Consolidation
A large retail chain extracts inventory data from multiple in-store systems and supplier databases. The ETL process standardizes product codes, removes duplicates, and reconciles stock discrepancies before loading the cleaned data into a central inventory system. This ensures accurate stock levels and prevents overstocking or stock outages across locations.
2. Government Census Data Processing
Government agencies extract raw census data from surveys and regional databases. ETL processes validate entries, standardize formats (e.g addresses, demographics), and remove inconsistencies before loading the data into official statistical systems. This guarantees high data quality for policy-making and public reporting.
3. Healthcare Data
Healthcare providers often extract patient records from fragmented Electronic Health Record (EHR) systems. Before loading this data into a centralized data storage system for clinical research, transformation pipelines must anonymize patient names and other Personal identifiable information (PII).
ELT use cases
1. IoT Sensor Data Processing
A manufacturing company collects continuous streams of sensor data from various machines (temperature, pressure, vibration etc). Using ELT, all raw sensor data is loaded into a data storage system first. Engineers then transform and analyze it later to detect anomalies, predict equipment failures, and optimize maintenance schedules.
2. E-commerce Customer Behavior Analytics
An e-commerce platform extracts and loads raw clickstream data, search queries, and browsing history directly into a data storage system. Analysts later transform this data to study user behavior, build recommendation systems, and personalize shopping experiences without losing any original interaction data.
3. Social Media Sentiment Analysis
A marketing firm gathers raw social media posts, comments, and engagement metrics from multiple platforms. With ELT, this unstructured data is stored as-is in a central data storage facility. Analysts later transform it to extract sentiment, trends, and brand perception insights.
Tools
Considerations when choosing ETL/ELT tools
1. Extent of data integration: ETL/ELT tools can connect to a wide range of data sources and destinations. Data teams should opt for tools that offer a wide range of integrations.
2.Customizability: organizations should choose their ETL/ELT tools based on their requirements for customization and technical expertise of their teams.
3.Cost structure: When choosing an ETL/ELT tool, organizations should consider not only the cost of the tool itself but also the cost of the infrastructure and human resources needed to maintain the solution over a long term. Some tools have a higher upfront cost but lower downtime and maintenance requirements making them more cost-effective.
Tools used in an ETL
What are ETL tools?
They are a set of tools that are used to extract, transform and load data from one or more sources into a target system or database. ETL tools are designed to automate and simplify the process of extracting data from various sources, transform it into a consistent and clean format, and load it into a target system in a timely and efficient manner.
Some of the best and commonly used ETL tools include:
1. Apache Airflow
Apache airflow is an open-source platform to programmatically author, schedule and monitor workflows. The platform features a web-based user interface and a commmand-line interface for managing and triggering workflows. Workflows are defined using directed acyclic graphs (DAGs), which allow for clear visualization and management of tasks and dependencies. Airflow has the ability to integrate to other tools such as Apache Spark and Pandas.
2. Databricks Delta Live Tables (DLT)
It is an ETL framework built on top of Apache Spark that automates data pipelines (creating and managing them) allowing data teams to build reliable, maintainable and declarative pipelines with minimal effort. Delta Live Tables simplifies ELT by using a declarative approach where user define the what (the transformations and dependencies) and the system handles the how (execution, optimization and recovery).
3. Oracle Data Integrator
Oracle data integrator is an ETL tool that helps users build, deploy and manage complex data warehouses. It comes with out-of-the-box connectors for many databases, including Hadoop, EREPs, CRMs, XML, JSON, LDAP, JDBC, and ODBC. Oracle Data Integrator includes Data Integrator Studio, which provides business users and developers with access to multiple artifacts through a graphical user interface. These artifacts offer all the elements of data integration, from data movement to synchronization, quality, and management.
4. Hadoop
Hadoop is an open-source framework for processing and storing big data in clusters of computer servers. It is considered the foundation of big data and enables the storage and processing of large amounts of data. It consists of several modules, including the Hadoop Distributed File System (HDFS) for storing data, MapReduce for reading and transforming data, and YARN for resource management. Hadoop is costly due to the high computing power required.
5. AWS Glue
It is a serverless ETL tools offered by Amazon. It discovers, prepares, integrates and transforms data from multiple sources for analytics use cases. When interacting with AWS Glue, users can use a drag and drop GUI, a Jupyter notebook or a python/Scala code.
6. Azure Data Factory
It is a cloud based ETL service offered by Microsoft used to create workflows that move and transform data at scale. It comprises of series of interconnected systems that allow engineers to not only ingest and transform data but also design, schedule and monitor data pipelines.
Tools used in ELT
Some of the best and commonly used ELT tools include:
1. Airbyte
It is an open-source ELT platform that provides hundreds of connectors for databases and SaaS apps. It is widely used for flexibility and the ability to build or customize their own integrations.
2. Fivetran
known for its automated, fully managed connectors, Fivetran ensures continuous data flow into cloud warehouses with minimal maintenance. It is often chosen by teams that value reliability and offloading connector maintenance.
3. dbt (data built tool)
Rather than transforming data mid-flight during extraction, dbt transforms data inside the data warehouse using SQL. dbt lets data engineers and analytics engineers write modular, version-controlled, and tested SQL models. Every model is documented, dependencies are tracked, and every run produces a data lineage graph showing exactly how data flows from raw sources to final tables. It integrates with all major cloud data warehouses such as snowflake, Redshift and Databricks offering flexibility.
4. Matilion
It is is a cloud-native ELT platform that integrates seamlessly with major data warehouses, including Snowflake, BigQuery, and Redshift. Its visual interface makes it easy for users to design workflows through a drag-and-drop environment, while more advanced users can leverage SQL-based transformations to handle complex data tasks.
5. Hevo
It comes with over 150 connectors for extracting data from multiple sources. It is a low-code tool, making it easy for users to design data pipelines without needing extensive coding experience. Hevo offers a range of features and benefits, including real-time data integration, automatic schema detection, and the ability to handle large volumes of data.
Conclusion
ETL and ELT each play a critical role in modern data workflows, differing primarily in where and how data transformation occurs. While ETL emphasizes preprocessing before storage, ELT leverages the scalability of modern data platforms to transform data after it is loaded.
By 2026, ELT has become the dominant trend due to cheaper cloud storage and more powerful compute, enabling organizations to transform data within modern data warehouses for greater flexibility, faster insights, and reduced pipeline complexity. However, ETL remains important in environments with strict security, privacy, or compliance requirements, where data must be transformed before storage, making both approaches relevant depending on organizational needs.
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