Hadoop vs Kafka – What’s the Difference ? (Pros and Cons)
Hadoop vs Kafka – What’s the Difference ? (Pros and Cons). First of all, analysing big data has led to development of a variety of big data frameworks such as Hadoop. In this article, we introduce Hadoop and Kafka. Firstly, Hadoop is an open source software platform written in Java for distributed storage and processing of large data sets using clusters of computers. On the other side we have Kafka- a message queue and message broker, also an open source software. Written in the Scala and the aim of the project is to enable handling of real time data from multiple nodes.
This article introduces Hadoop and Kafka, their main features, altogether with pros and cons. At the end we present the most notable differences between them.
Shall we start with Hadoop vs Kafka – What’s the Difference ? (Pros and Cons).
What is Hadoop?
Firstly, Apache Hadoop software library is a platform that enables distributed processing of large data sets across clusters of computers using simple programming models. Designed to scale from individual servers to thousands of systems, each providing local computing and storage. Instead of relying on hardware for high availability, the library itself is designed to detect and handle errors at the application layer, providing high availability services on top of clusters of systems that are each subject to failure.
How does Hadoop work?
All in all, Hadoop works by distributing large data sets across a cluster of computers and processing the data in parallel on each node. The core components of Hadoop include Hadoop Distributed File System (HDFS) and MapReduce.
A). HDFS a storage system for Hadoop, designed to store very large data sets with redundancy on a shared hardware cluster. When data is stored in HDFS, it is split into smaller blocks and replicated to multiple nodes in the cluster. As a result this provides redundancy and ensures that data remains available, even if one or more nodes in the cluster fail.
B). MapReduce is the main programming model for data processing in Hadoop. In nutshell, it works by breaking large data processing tasks into smaller, independent tasks that run in parallel on a cluster. The operation is divided into two phases: the map phase and the reduce phase. In the mapping stage, data is processed in parallel on each node in the cluster and then passed to the reduction stage where the results are combined and processed.
When a MapReduce job is submitted to a Hadoop cluster, the job is automatically split and assigned to nodes in the cluster. The results of each operation are combined in a reduction step to produce the final result.
Benefits of using Hadoop
Data processing – Secondly, it provides a powerful data processing framework which enables processing of huge amounts of data in a short amount of time, which is ideal for data intensive applications such as fraud detection, customer behavior analysis, and predictive analytics.
Fault tolerance – Fourthly, it provides a high level of fault tolerance by storing multiple replicas of data on the nodes of the cluster. This means that data is still available even if one or more nodes in the cluster fail.
Pros of Hadoop
- Redundant tasks like data wrangling, data processing, and cleaning are more efficient and faster in Hadoop.
- Easy to install and deploy.
- The system contains a file system known as HDFS (Hadoop Distributed File System) which processes components and programs.
- The parallel processing tool is a huge benefit.
Cons of Hadoop
- Slow processing speed.
- Support for batch processing only.
- Lengthy line of code.
- Overall complexity and challenging learning curve.
Up next with Hadoop vs Kafka – What’s the Difference ? we introduce Kafka.
What is Kafka?
Second data tool we have is a stream processing platform, called Kafka. Hence, it gives high throughput, low latency, and is a scalable platform for processing real time data streams. Used for a variety of applications including real time analytics, event driven architectures, and streaming data pipelines.
Equally, Kafka is designed to handle large, high speed data streams and provides a publish subscribe model for data collection and distribution. Well, producers write data to topics in Kafka, and consumers read data by subscribing to one or more topics.
How does Kafka work?
Evidently, Kafka is a distributed streaming platform that combines the features of two messaging models queuing and subscription publishing.
Queuing
Well, the queuing model the messages are processed in the order in which they are received into the system. After, each message is stored in a partition, a unit of scalability. Therefore, the consumer reads the messages in the order in which they are stored, and the system guarantees that each message is read exactly once.
Publish Subscribe
Subscription publishing model allows consumers to subscribe to selected topics and read messages from multiple sources simultaneously. Each consumer in a group reads from a separate partition, and this allows the system to handle multiple subscriptions in parallel.
Features of Kafka
A). Integration with other systems – offers easy integration with other systems such as Apache Spark, Hadoop and other streaming platforms. Further, it makes it a versatile and flexible solution for processing large data.
B). Stream processing – Enables streaming processing where data gets processed as it passes through the system. That enables real time analysis, monitoring and decision making.
C). Durability and fault tolerance -provides a reliable, fault tolerant messaging system by storing messages on disk and replicating them across multiple brokers. This ensures that messages are not lost if the broker fails.
D). Distributed – designed for distributed use, allowing multiple servers to process large amounts of data.
Kafka Pros
- Work seamlessly during high data load.
- An abundance of options for managing and maintaining queues.
- Data retention for reprocessing.
- Handles large amount of data simultaneously leading to scalable application.
- Resistant to node failure within the cluster.
- The same Kafka setup can be used for messaging, storage system or a log aggregator. Furthermore, it makes it easy to maintain as one system feeds multiple applications.
- High volume/performance throughput environments.
Kafka Cons
- It does take some initial time to setup and deploy.
- Requires dashboards to monitor the performance.
- Does not support wild card topic selection.
- Does not have complete set of monitoring tools.
We have arrived at the main part of the article Hadoop vs Kafka – What’s the Difference ?
Hadoop vs Kafka - Comparison
Importantly Hadoop and Kafka are the two popular open source distributed systems that are often used together in big data processing pipelines. Importantly though, they serve different purposes. But both are important tools for building scalable, fault tolerant, and high performance data processing systems. Below are the main differences between Hadoop and Kafka are:
Data Consistency Differences
On one hand, data processing model in Hadoop is based on batch processing using the MapReduce programming model. Hence, data is stored in Hadoop Distributed File System (HDFS), which provides data replication and fault tolerance. Henceforth, Hadoop guarantees data consistency at the end of each batch. This means data is processed sequentially and consistency is guaranteed after the batch ends. Indeed with Hadoop, data consistency is high, but processing can be slow due to the batching model.
Interestingly Kafka is designed for streaming and real time data processing. Processing model is based on eventual consistency. That is, there may be a delay between data creation and consumption. In Kafka, data is stored in memory or on disk for a short period of time before being consumed and processed by downstream applications. For essence, Kafka provides a compromise between strong consistency and low latency. Data consistency is ultimately guaranteed in Kafka, but it can become inconsistent during the processing pipeline.
Throughtput and Latency Differences
In this example data is stored in HDFS (Hadoop Distributed File System) and then batched. Hadoop’s batch processing approach provides high throughput for large and complex data processing tasks, but suffers from high latency. Latency in Hadoop is significant due to the time it takes to write and read data from HDFS and the time it takes to schedule and run MapReduce jobs.
On the other hand, Kafka is designed to provide low latency and high throughput for real time data processing. Moreover, Kafka messaging system is designed to handle millions of messages per second with low latency, making it a good choice for high throughput, low latency applications. Importantly, Kafka’s architecture is optimized for real time data processing by storing data in memory or disk for a short period of time before using and processing the data in subsequent applications.
Language and support Comparison
Language support with Hadoop is thorough. It supports multiple programming languages such as Java, Python, Scala and R. Java being the most widely used language for developing Hadoop applications. Other languages are also available through libraries or APIs (Hadoop Streaming API for Python and R), allowing developers to write Hadoop applications in those languages. Nonetheless, Hadoop is also integrated with popular data processing platforms such as Apache Spark and Apache Flink that support additional programming languages.
Well, Kafka also has excellent language support and is used with several programming languages including Java, Scala, Python and C++. Kafka provides client libraries for a variety of programming languages, making it easy to develop Kafka applications in the language of your choice. Likewise, Kafka also provides APIs for various programming languages to interact with Kafka clusters, including the Kafka Connect API for integrating with data sources and sinks, and the Kafka Streams API for building real time streaming applications.
Processing speed Differences
Hadoop’s processing speed is determined by the size of the data set, the complexity of the algorithms being applied, and the number of machines being used for processing. Therefore, Hadoop can be very efficient in processing large data sets, but its batch processing model means that it is not well suited for real time data processing scenarios.
Kafka’s processing speed is determined by the size of the data stream and the complexity of the processing being done. Kafka can be very efficient in processing real time data streams, but its streaming model means that it is not well suited for batch processing scenarios.
Data storage Comparison
Provides a distributed file system called HDFS (Hadoop Distributed File System), which is used to store large amounts of data across a cluster of machines. In effect, HDFS breaks large data sets into smaller chunks and distributes them across multiple nodes in the cluster for parallel processing. This approach provides fault tolerance and allows Hadoop to process large amounts of data efficiently.
Instead with Kafka, it provides a messaging system for real time data streaming and processing. Additionally Kafka stores data in topics, which are similar to message queues, but are also used for stream processing. The data is stored for a limited time before being processed or analysed, and Kafka is optimized for handling large volumes of data in real time.
Purpose and Use Cases
Last feature to compare with Hadoop vs Kafka – What’s the Difference ? is a purpose. While Hadoop is primarily used for distributed storage and batch processing of large data sets. Hadoop is designed to handle large data sets that are too large to fit in a single system, and is often used for tasks such as log processing, data storage, and data analysis. Top use cases for Hadoop are: Network traffic analytics, eCommerce, fraud prevention and predictive maintenance on their infrastructure.
Oppositely, Kafka is a distributed streaming platform designed for real time processing and messaging of large amounts of data. This allows real time data to be sent and received between multiple nodes in the cluster. Also Kafka is often used for tasks such as streaming, real time analytics, and event driven architectures. Top use cases for Kafka are: Data streams of scalable nature, dataflow middleware, hybrid messaging database and analysis of a complex nature. Also best suited for website activity tracking.
Thank you for reading Hadoop vs Kafka – What’s the Difference ? (Pros and Cons). We shall conclude the article.
Hadoop vs Kafka – What’s the Difference ? Conclusion
In general, the choice between Hadoop and Kafka will depend on the specific use case and project requirements. Summary of those tools means that Kafka is not a rival to Hadoop. In general, Hadoop is ideal for batch processing of large data sets, while Kafka is ideal for real time processing and streaming. However, it is not uncommon for organisations to use both Hadoop and Kafka in combination to meet their big data processing needs.
Additionally, the choice between Hadoop and Kafka depends on the company’s specific use case and requirements. Both technologies have their strengths and weaknesses, and companies need to carefully assess their needs before deciding which one to use.
Read more about Kafka in our blog by clicking here.
