Introduction
In our previous post, we learned about “what is KAFKA and Why we need to use it”. In this article, we will dive into the details of ETL processes and will talk about the role of KAFKA in ETL. But first, let’s try to answer the question: What is ETL?
ETL stands for Extract, Transform, Load. İt is just a process of extracting data from different sources, Transforming them into the required format, and loading data into a Target.
Extract: the source of data extraction can be various source systems, which could include databases, applications, APIs, flat files, web services, and more. The goal is to gather the necessary data for analysis.
Transform: You can think about this step like your business layer in applications. Once the data is extracted, it often needs to be cleaned, validated, transformed, and structured into a consistent format suitable for analysis. This transformation can be:
- data cleansing (removing duplicates, correcting errors)
- data enrichment (adding missing information)
- data normalization (converting data to a common format)
- data aggregation
Note. Other types of transformations (any type of calculation provided by the ETL tool)
Load: After the data has been transformed, it is ready for querying and reporting. Loading can be done mostly using 2 paths:
- Incremental loading (only loading new or changed data)
- Full loading (reloading all data).
Nowadays, in many businesses, the role of ETL is undeniable. ETL comes to the scene when there is already data in one or more systems, and we need to “move” them to another system. The process of moving can be just like a replication or data processing and transformation.
![Kafka]()
As we already know, we, as a developer, develop some systems, in most cases, for gathering information. This information can be user account information ( when registering), order information( when a customer orders something using our application), etc. Say, you’re developing an e-commerce application. The purpose here is to give you the ability to our customers to purchase items from our online store. So, we’re grabbing information from the outside of the application into the application. It is where everything starts. The gathering information is valuable from the business perspective, and sometimes it needs to be “moved” from one source to another destination.
You can transfer from multiple sources in one destination or 1-1 operation( 1 source – 1 target).
The final purpose of ETL is to provide a structured and reliable source of data that can be easily analyzed by business intelligence tools, data analysts, and data scientists. This processed data can then be used for various purposes, including generating reports, conducting trend analysis, making informed business decisions, identifying patterns, and discovering insights that can drive organizational strategies and optimizations.
ETL processes are done using ETL tools. ETL tools just simplify data management strategies and support data-driven platforms.
We can define ETL tools under 4 categories:
- Enterprise software ETL tools
- Open-source
- Cloud-based
- Custom ETL tools
Kafka and ETL (Extract, Transform, Load) are related in the context of data processing and data integration, specifically in the context of real-time data streaming.
Kafka is designed to handle large-scale, real-time data streams efficiently and reliably.
Kafka is based on a publish-subscribe messaging model, where data is produced by producers and consumed by consumers.
On the other hand, ETL is a data integration process that involves extracting data from various sources, transforming it to meet specific requirements, and loading it into a target database or data warehouse for analysis and reporting.
The relationship between Kafka and ETL comes into play when organizations need to process data in real-time or near-real-time, as opposed to traditional batch processing.
![Kafka]()
Here's how they work together:
Data Ingestion: Kafka serves as a central, highly scalable data ingestion platform.
It acts as a buffer that receives and stores data from different sources, such as application logs,
databases, sensors, or other streaming data sources. Producers send data to Kafka topics,
and the data is kept in the Kafka cluster for consumption.
Real-time Streaming: ETL processes can use Kafka as a data source to enable real-time or
near-real-time streaming data processing. ETL tools can consume data from Kafka topics and perform transformations on the fly before loading it into the target systems. This real-time streaming capability allows organizations to make quick and data-driven decisions as data arrives in real-time.
Decoupling Data Producers and Consumers: Kafka provides decoupling between data producers and consumers. Data producers can produce data without worrying about how it will be consumed,
and data consumers can consume data without affecting data producers.
This decoupling is beneficial when integrating data from multiple sources in ETL pipelines.
Scalability: KAFKA provides functionality to scale source and target easily. This functionality can be used by ETL systems to manage sources and targets dynamically. ETL processes can scale to handle large amounts of data by adding more Kafka brokers and consumers.
Fault-tolerance: Kafka's distributed architecture ensures data availability and fault tolerance, making it a robust choice for real-time data processing in ETL workflows. KAFKA can handle fault tolerance easily, thanks to cluster mirror-making and replication factors.
Overall, Kafka complements ETL by providing a reliable, scalable, and efficient way to handle real-time data streams.
ETL processes can leverage Kafka's capabilities to build real-time data pipelines, enabling organizations to perform data analysis and make informed decisions based on the most up-to-date information.
Summary
The collaboration between Kafka and ETL transcends traditional data processing methods, ushering in a new era of real-time data analysis and decision-making. This partnership empowers businesses with the tools they need to harness the potential of their data, guiding them toward innovation and success in a rapidly evolving landscape.