Introduction

Due to the increase in the volume of data, the demand for Stream processing is on the rise. Just processing the data wouldn’t be enough, it should be done quickly too so that organizations can react to changing market conditions on a real-time basis. This is where Stream processing comes into play as it processes data continuously on a real-time basis. In this tutorial, we will be discussing in detail about Apache Kafka and Apache Spark: two of the most prominent data processing frameworks.

Apache Spark

It is a data processing platform that can perform the processing of large volumes of data in a short time. It can also distribute these data processing tasks amongst multiple computers, either on its own or using other computing tools. These capabilities of Spark are what made it a giant in the data processing platform.

According to Apache org., Spark is a “lightning-fast unified analytics engine” designed for processing huge volumes of Big Data. Today, it is one of the largest open-source platforms for Big Data. It has an active community too, which has resulted in the development of the platform overall.

Spark is also versatile in the ways in which it can be deployed. Even though it’s written in Scala and Java primarily, it also provides in-built support for other programming languages such as Python, R, etc. This is because Python and R are more popular amongst data scientists compared to Scala and Java.

Architecture

Master Node: It is responsible for coordinating workers and managing the tasks. It schedules jobs to be executed on the executors in clusters. Spark contexts monitor the job working in a specific cluster. These Spark contexts are created by the driver program.

Executors: They are responsible for executing the scheduled jobs and storing the data. They have a number of time slots to run the application concurrently. It runs the task when the data is loaded and is removed in idle mode.

Cluster Manager: It provides all the resources required by the worker nodes to function and operates all nodes as per need.

Worker Node: These nodes process the tasks assigned to them and return the results back to the Spark context. So basically, the master node assigns tasks to the spark context and these are executed by the worker nodes.

Features

1. Apache Spark is a distributed and in-memory stream processing engine. This means that it can receive the data from a source system and can process the data in memory in a distributed format. This would result in a drastic increase in the computing speed of any data processing application

2. Spark has a machine learning framework within and also supports the graph processing of data. Therefore, we will be able to use machine learning models for data processing. Also, this framework uses complex algorithms for data processing.

3. Spark framework also supports receiving data from other source systems such as Kafka, Kinesis, Flume, etc.

4. Spark provides the functionality of transforming data. Data can be transformed using high-level functions such as map, reduce, join, window, and watermarking.

5. Spark provides immense scalability when it comes to carrying out EDA (Exploratory Data Analysis) on data.

Apache Kafka

Apache Kafka is a reputed open stream event streaming platform. It is used by organizations for its high-performance data pipelines, streaming analytics, and data integration. Industries such as manufacturing, telecom, insurance, banks, etc use Kafka for various purposes. The majority of the giants in these fields are reported to use Kafka.

It is a distributed publish-subscribe messaging system that receives data from source systems and makes it available to target systems. It facilitates smooth data exchange between various applications and servers. Compared to other messaging systems, Kafka doesn’t target customer behavior, so the expenses of running it are relatively low.

Kafka operates in clusters i.e it runs on one or more servers. Each node in a cluster is called a broker. The broker helps to write data to topics and read data from topics. Kafka manages these topics by dividing them into partitions.

Architecture

Features

1. Kafka has a high throughput. It can deliver messages at network limited throughput using a cluster of machines

2. Apache Kafka processes data event by event with low latency. It means that the processing is done in a chronological manner.

3. It supports stateful processing including distributed joins and aggregation. This means that we can process the data by storing the state of each and every event.

4. Apache Kafka has its own convenient domain-specific language (DSL). So, the Kafka streams application can be implemented using popular languages such as Java/Scala.

5. This framework also provides us with the option of processing data events using Windowing.

6. It has distributed processing and fault tolerance with fast failover. Also, it has no downtime in rolling deployments.

Apache Kafka vs Apache Spark: Feature Comparison

Latency: On comparing Apache Spark and Apache Kafka, it’s observed that Kafka has significantly lower latencies (in the range of milliseconds). Therefore, if you focus on processing data with low latency levels, Kafka is the better choice.

Fault tolerance: Kafka provides superior fault tolerance capabilities compared to Spark, as it processes data event by event.

Compatibility: As we know, the Spark framework is robust when it comes to receiving data from other source streams. The same can’t be said about Kafka as compatibility issues may rise. Therefore Spark is the better option in terms of compatibility.

Data Processing: Spark carries out data processing as a batch i.e the incoming data stream is divided into micro-batches. On the other hand, Kafka carries out data processing on an event basis.

Scalability: Apache Spark has been observed to be the better framework when it comes to scalability. It enables us to carry out analytics on petabyte-scale data without resorting to downsampling

Conclusion

So, after looking at the features of both platforms in detail, we have observed that both the platforms are best in their own ways. Both platforms are well renowned and are used and trusted by several prominent companies. It depends on the user and his/her requirements to decide which platform would be a better option.

Hope that this article was informative and worth your time.

Happy Reading!