Hey,
It’s Sarvar Nadaf again, a senior developer at Luxoft. I worked on several technologies like Cloud Ops (Azure and AWS), Data Ops, Serverless Analytics, and Dev Ops for various clients across the globe.
Let’s discuss Kafka!
What is Kafka?
The Java and Scala-based open source project Kafka is from the Apache Foundation. Because it is open source, it is effectively free to use and has a sizable user and developer community that contributes to updating new features, and provides assistance for new users. widely used as a platform for distributed event stores and stream processing.
Real-time streaming data pipelines and applications that can adapt to the data streams are typically built using Kafka. To enable the storage and analysis of both historical and real-time data, it mixes communications, storage, and stream processing.
Why Kafka?
It is built to be fault-tolerant, resilient, and distributed. One of Kafka’s most significant benefits is its ability to scale horizontally. It can scale to support millions of messages per second and hundreds of clusters. It makes high-performance real-time data streaming possible, which is essential for big data applications.
Kafka accurately stores data records and is very fault-tolerant. It has the capacity to process large amounts of data quickly. Without experiencing any performance concerns, it can accept and process trillions of data records per day.
Where is Kafka mainly used?
Kafka is frequently used in the big data world as a secure technique to ingest and sends a huge amount of data streams very quickly due to its fault tolerance and scalability. Let’s examine some specific usage situations where Kafka is the first option.
1. Stream Process -
The term “stream processing using Kafka” describes the immediate processing of a continuous stream of data. It performs real-time streaming data analysis. When the size of the data is unknowable, limitless, and continuous, stream processing is performed. Data processing takes a few milliseconds or seconds. Data output rates in stream processing are equal to data input rates. Data is processed by a Kafka stream processor in a few passes. Stream processing is used when a data stream is continuous and demands a quick response.
2. Website Activity Tracking -
This indicates that site activity including page views, searches, click stream, and other user activities is shared to core themes, with one topic for each category of activity. For a variety of use cases, including real-time processing, monitoring, and loading into Hadoop or offline data warehousing systems for offline processing and reporting, these feeds are available for subscription.
3. log Aggregation -
Log aggregation often gathers actual log files from servers and stores them in one location for analysis. Kafka delivers a cleaner abstraction of log or event data as a stream of messages by abstracting away the specifics of files. As a result, processing with lower latency is possible, and it is simpler to accommodate various data sources and remote data consumption.
4. Messaging -
Kafka is a suitable option for large-scale message processing applications because it provides superior throughput, built-in partitioning, replication, and fault tolerance than the majority of messaging systems.
Who is using Kafka?
Several leading companies in this area use Kafka for real-time data processing. Here is an example of their use of Kafka.
- LinkedIn —
LinkedIn uses Kafka to prevent spam on their platform, collect user interactions and make better connection recommendations. All of that is in real-time.
2. Netflix
Distributed governance is adopted by the Netflix Studio Productions and Finance Team as a method of system architecture. They prefer working with events, which are an immutable means to record and deduce system states, on the Kafka platform. They have been able to decouple their infrastructure and increase visibility with the help of Kafka as they scale up their operations naturally. The architecture of Netflix Studio is being transformed by it, and the movie business as a whole.
3. Uber
The Apache Kafka® deployment at Uber is among the biggest in the world. Many real-time activities at Uber are made possible by it, including pub-sub message buses for transmitting event data from the rider and driver apps as well as financial transaction events between the backend systems.
4. Pinterest
The foundation of Pinterest’s data transit layer is Apache Kafka®. Over the years, Kafka has processed an increasing volume of data. To ensure that data transportation operates as smoothly as possible, we must occasionally deal with and plan for operational issues brought on by this increase. This post explains how Kafka is managed at Pinterest and covers some of the difficulties we’ve encountered and solutions we’ve come up with.
5. Airbnb
Kafka as a Foundation for Highly Reliable Logging at Airbnb. Kafka plays a central role in the data ecosystem at Airbnb. We operate multiple clusters powering use cases such as analytics, change data capture, and inter-service communication.
Conclusion -
Thousands of businesses, including more than 80% of the Fortune 100, use Kafka today. With event streaming architecture, Kafka, the reliable tool for empowering and innovating businesses, enables firms to upgrade their data strategies. Kafka is one of the most popular real-time data streaming technologies. Kafka is the dominant real-time data streaming tool in the market today.
Im going to share my knowledge with you in order to make it easier for you to grasp Kafka. I’ll be publishing an article on the subject of Kafka Architecture soon. I hope you enjoy it.
happy studying!
