Do you think IIT Guwahati certified course can help you in your career?
Introduction
American software developer Robert McCool developed the open-source Apache Web Server and released it in 1995. A significant number of Apache employees and developers and contributions from users worldwide keep Apache updated and developed. Apache Software Foundation developed stream-processing frameworks like Kafka and Spark to process data in real-time. Despite having a lot of similar features, they also have some differences.
This article will cover the difference between Kafka and Spark. We will discuss what Kafka and Spark are and then move to the differences.
What is Apache Kafka?
Apache Kafka is an open-source distributed event-streaming platform for processing data in real-time. Event stream processing (ESP) technology can process a continuous data flow when an event or change occurs. Kafka was initially developed to manage real-time data feeds by LinkedIn. Since then, it has emerged as the industry standard for large-scale messaging systems used by some of the top internet companies in the world. For example, Netflix, Uber, Amazon etc.
Uses of Apache Kafka
Some of the critical uses of Apache Spark are as follows:
It is used for mission-critical applications and the creation of real-time streaming applications, such as those for fraud detection.
It is used for streaming analytics to streamline access to critical insights.
It is used for high-performance data pipelines.
IT professionals can better understand their systems by collecting and analysing logs from web servers and databases using Kafka.
Advantages of Apache Kafka
Some of the advantages of Apache Kafka are as follows:
Low latency
Kafka ensures that messages are delivered immediately.
Reliability
Kafka is built to be dependable, with automated replication and failover features that guarantee its continuous functioning.
High throughput
It enables processing millions of messages per second with a cluster of thousands of brokers.
Scalability
It is also highly scalable. One can add or remove a broker as per the user’s demand.
No Data Loss
All data logs are maintained within a specified time without deleting any data. As a result, there is less chance of data loss.
Efficiency
For end users, it reduces buffering and increases stream efficiency.
Disadvantages of Apache Kafka
Some of the disadvantages of Apache Kafka are as follows:
Limited Message Paradigms
For various use situations, Kafka lacks certain message paradigms like point-to-point queues, request/reply, etc.
Performance Drops
Brokers and consumers who compress and decompress the data flow decrease Kafka's performance. This has an impact on both its throughput and performance.
Lack of Monitoring Tools
There aren't enough monitoring and management solutions available. Because of this, the enterprise support team was hesitant or afraid to choose Kafka and provide long-term support for it.
What is Apache Spark?
Apache Spark is an open-source and cost-free Cluster computing and distributed processing framework. In 2013, it was recognised as a project incubated by the Apache Software Foundation. It is a method of processing data that can handle enormous workloads and collections. Establishing direct access points, known as Resilient Distributed Datasets (RDDs), drastically reduces the time required to analyse real-time or streaming data.
Uses of Apache Spark
Some of the critical uses of Apache Spark are as follows:
It can process streaming data from various sources, such as social media feeds, blogs etc.
It can read data from many sources and change it into formats appropriate for processing data.
It is used for ETL operations(Extract, transform, and load) of pipelines in industries.
Using external data sources, Spark can swiftly enrich records.
Advantages of Apache Spark
Some of the advantages of Apache Spark are as follows:
Big Data Handling
Massive data quantities may be handled quickly, and jobs can be spread over multiple servers to lighten the workload.
Dynamic Nature
It enables you to customise its use case to meet your unique demands and expectations.
Real-time analysis
This opens up a world of opportunities for data analysis, from data mining methods to machine learning models and real-time predictive analytics.
Cost-effective
Spark's incredible speed makes it possible to process enormous datasets efficiently in a fraction of the time and deliver insights rapidly and affordably.
Disadvantages of Apache Spark
Some of the disadvantages of Apache Spark are as follows:
Manual Improvement
While using Spark, the work needs to be manually optimised. Manual control is required if we want Spark's partitioning and caching to be accurate. We can set a number of spark partitions on our own to work with specific datasets, if we wanted to create partitions.
High Latency
Apache Spark has a lower throughput and higher latency. Kafka has a faster throughput and lower latency when compared to Apache Spark.
Lack of a File Management System
Spark does not include a file management system of its own. It doesn't have a built-in file management mechanism. It needs to integrate with another cloud-based data platform for file management system.
Difference Between Kafka and Spark
The difference between Kafka and Spark is as follows:
Key Points
Kafka
Spark
Basic Feature
It is an open-source distributed event-streaming platform.
It is a Cluster computing and distributed processing framework.
Speed
It has decent speed.
It is faster than Kafka.
Clusters
There is no need for a separate processing cluster.
A separate processing cluster is needed.
Data Streaming Process
It uses a real-time window processing mechanism.
It uses real-time batch processing.
Interactivity
There is no interactive mode.
It has various interactive modes.
Recovery
Fault-tolerant or Replication is used for recovery.
It allows partition recovery utilising Cache and RDD.
Data Storage
Kafka keeps data in Topics or a memory buffer.
Spark employs RDD to distribute data storage (i.e., cache, local space).
Difficulty
It is easy to configure.
Because of the high-level modules, it is simple to learn.
Languages
The primary language that Apache Kafka supports is Java.
It supports various languages, including Python, R, Scala, and Java.
Frequently Asked Questions
What is the key difference between Kafka and Spark?
Kafka is made to handle data from various sources, whereas Spark is made to handle data from a single source. With batch processing and SQL query capability, Spark focuses more on data processing than Kafka, which concentrates on messaging (publishing/subscribing).
What is RDD?
RDD is a distributed collection of your data's elements that are kept in the memory or on the discs of various machines in a cluster. It can be used in parallel with a low-level API that supports actions and transformations. Multiple logical partitions can be created from a single RDD.
Is Kafka quicker than Spark?
No, Spark is quicker than Kafka. Spark streaming is superior for processing groups of rows (using groups, by, window functions, etc.). Kafka is the best option, nevertheless, if latency is a severe issue and real-time processing with time frames shorter than milliseconds is needed.
Which conditions favour Kafka over Spark?
Kafka is appropriate for real-time data streaming applications like clickstream analysis, fraud detection, and real-time analytics. However, Data processing use cases involving advanced analytics, graph processing, and machine learning are appropriate for Spark Streaming.
Conclusion
In this article, we extensively discussed the difference between Kafka and Spark. Apache Kafka is an open-source distributed event-streaming platform for processing data in real-time. Apache Spark is an open-source and cost-free Cluster computing and distributed processing framework.
We hope this article helps you. You can visit more articles.
19+ registered 
