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Top 10 Benefits of Apache Kafka

What is Apache Kafka?

Kafka is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system used to manage big volumes of data. Due to high efficiency, reliability and replication characteristics, Kafka is applicable for systems like tracking service calls (tracks every call), instant messaging or tracking IoT sensor data where a traditional technology might not be considered. Kafka can work with different frameworks for real-time ingesting, analysis, and processing of streaming data. Kafka is also a data stream used to feed Hadoop Big data lakes.

What are the benefits of Apache Kafka?

  1. Acts as a buffer: Apache Kafka solves data transformations process by acting as an intermediary, receiving data from source systems and then making this data available to target systems in real-time. During this process, systems will not crash because Apache Kafka is on its own separate set of servers called an Apache Kafka cluster.
  2. Highly scalable: Kafka is a distributed system, which can be scaled quickly and easily without incurring any downtime.
  3. Highly reliable: Kafka replicates data and can support multiple subscribers. Additionally, it automatically balances consumers in the event of failure. That means that it is more reliable than similar messaging services available.
  4. Low latency: Apache Kafka decouples the message which lets the consumer to consume message anytime. This leads to low latency value, up to 10 milliseconds.
  5. Offers high performance: Due to low latency, Kafka can handle a huge number of messages of high volume and high velocity. Delivers high throughput for both publishing and subscribing, utilizing disk structures that are offering constant levels of performance, even when dealing with many terabytes of stored messages.
  6. Fault tolerance: Kafka has an essential feature to provide resistant to node/machine failure within the cluster.
  7. Reduces the need for multiple integrations: All the data that a producer writes go through Kafka. Therefore, you just need to create one integration with Kafka, which automatically integrates all users with each producing and consuming system.
  8. Easily accessible: As all our data gets stored in Kafka, it becomes easily accessible to anyone.
  9. Distributed system: Apache Kafka contains a distributed architecture which makes it scalable. Partitioning and replication are the two capabilities under the distributed system.
  10. Real-time handling: Apache Kafka can handle real-time data pipeline. Building a real-time data pipeline includes processors, analytics, storage, etc.

Overview of Apache Kafka

Originally designed as a messaging system, Apache Kafka has evolved to be a full-fledged event streaming platform. It is a distributed event streaming platform that can handle trillions of events every day. This platform is the best option on the market for any business or industry that is considering highly scalable, real-time data solutions to build and manage data pipelines. With low downtime issues and huge data storage, makes it easier and more stable to handle a huge volume of data.

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Overview of why Kafka matters


How Apache Kafka works?

In structure, Kafka has publishers, topics, and subscribers. More deeply, it can also partition topics and enable massively parallel consumption. All messages written to Kafka are persisted and replicated to peer brokers for fault tolerance, and those messages stay around for a configurable period (i.e., 7 days, 30 days, etc.).

The key to Kafka is the log data structure. The log is simply a time-ordered, append-only sequence of data inserts where the data can be anything (in Kafka, it is just an array of bytes). In baseline, it is very similar to the basic data structure upon which a database is built. Specifically, simplicity is also one of main Kafka values.

Databases write change events to a log and derive the value of columns from that log. In Kafka, messages are written to a topic, which maintains this log (or multiple logs — one for each partition) from which subscribers can read and derive their own representations of the data. 

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What is Kafka used for in businesses

What is Apache Kafka used for?

Apache Kafka is the most popular open-source stream-processing software for collecting, processing, storing, and analyzing data at scale. It is applicable for all industries which are dealing with huge amount of data in real-time is required.

1. Data transformation: From batch to real-time system

Unlike the batch processing of data in legacy architecture, Apache Kafka allows real-time processing. It acts as an intermediary for receiving and sending it to the target system in real-time.

2. Microservices architecture

Microservices are way out of the deadlock with complex monolithic systems. Apache Kafka enables the introduction of microservice can handle a massive volume of data. This allows businesses to scale up their data processing capabilities parallel to their data flow increase.

3. Integration

It is very easy with Kafka to integrate different applications and systems for data transmission. The developers need to create only one integration for each producing and consuming system.

Apache Kafka use cases

Anomaly Detection for IoT

With the increasing usage of IoT in every industry, network security threats, malicious control, wrong setups are also becoming common in these domains. That is the reason why anomalies in IoT (connected car infrastructure, smart cities and smart homes, smart retail, and customer 360, intelligent manufacturing) or fraud transaction detection, especially in industries like banking or insurance, require swift reactions. Using Kafka as a messaging platform it is possible to analyze data from multiple IoT services in real-time. It triggers alerts in case of detection of anomalies.

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Detection of anomalies in the IoT industry

Big Data Volumes for Telecom

Telecom is one of the biggest data consuming industries. Using Kafka-based real-time data processing systems can provide Telco’s tremendous benefits in terms of better decision making and revenue generation. For example, from the call detail records (CDRs) alone, huge volumes of data are transacted every day. A Telco needs to process these CDRs real-time on a day-to-day basis.

Website Activity Tracking

The original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds. This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type. Activity tracking is often very high volume as many activity messages are generated for each user page view.

Costumer 360 in Banking

Kafka messaging can offer innovation in the banking industry in different aspects. Building a new microservices architecture based on Kafka, the banking industry can offer new real-time customer experiences such as Customer Change Events, Customer Notification Systems, Digital Re-platforming/Digital Modernization, Next Best Offer, Personal Banking Cross-Sell/Upsell or Fraud Detection.

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Kafka in the banking industry

Stream Processing

Many users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and then aggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing. Such processing pipelines create graphs of real-time data flows based on the individual topics.

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Who uses Kafka?

Who uses Apache Kafka besides LinkedIn?

Kafka is an open-source platform developed by LinkedIn developers back in 2010 and it currently handles more than 1.4 trillion messages per day across over 1400 brokers. Over years many companies recognized benefits of Kafka and implemented this platform to own IT infrastructure – Yahoo, Twitter, Netflix, Spotify, Pinterest, Uber, Goldman Sachs, PayPal, Airbnb, Cisco, Coursera, Oracle, Trivago and many others.


For similar articles, please read further:

5 Things To Know About Event-Driven APIs And Apache Kafka

Real-Time Processing And Aggregation Of Call Detail Records (CDRs) Using Kafka And Flink

The Rise Of Event Stream Processing

Failure Detection And Alert From Real-Time Data Flow Using Kafka And KSQL











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