Now let’s discuss the technological features of Apache Spark:
Spark’s core technical feature is applying machine learning to big data. Spark’s Machine Learning MLlib library is used for scalable machine learning.
The secret to Spark’s speed is RDDs. RDD acts as a container that loads and distributes data to computers. This phenomenon allows parallelism.
To process the live data and perform analytics, we use Spark Streaming. Here, incremental batch processing allows faster streaming.
As discussed earlier, key performance indicators of Apache Spark and Apache Kafka are speed, fault tolerance, and scalability.
Kafka is built to handle many messages each second. It’s perfect for quickly taking in real-time data. For example, PayPal receives 400 billion messages a day. Kafka features a message delivery lag of milliseconds. It makes Kafka suitable for real-time applications that require no delay.
Scheduling tasks at worker nodes is the key performance indicator in Spark. Each machine learning process consists of a series of transformations. Spark also facilitates graph processing with GraphX.
For example, nodes refer to products and users in a recommendation engine. GraphX finds the relationship between nodes based on interactions. It then recommends to the user the product he’s most likely interested in.
In the scenario of real-time data at high volume and velocity, scalability becomes essential. Kafka achieves scalability by distributing the load across multiple brokers in a cluster. Each broker is responsible for a particular portion of the data. Kafka further divides the incoming data into partitions inside a broker. This distributed architecture allows for faster processing of incoming data. It handles incoming streams while making sure that data loss is minimal.
Instead of relying on a single computer to transform data in Spark, we distribute the data processing tasks to multiple computers that form the cluster. Each computer in a cluster is called a worker or node. Following this distributed computing model, Spark can quickly apply data transformation on large volumes of data.
The pricing for the Kafka.m5.large broker instance at MSK, which provides two vCPUs and 8 GiB of memory, is $0.21 per hour. As the requirements for vCPUs and memory increase, the pricing scales accordingly. For example, for a broker instance with 96 vCPUs and 384 GiB of memory, the price would be approximately $10.08 per hour, assuming pricing doubles with each doubling of vCPU and memory resources.
Apache Spark’s pricing structure combines software and EC2 instance costs. For example, the t2.small EC2 instance has a software cost of $0.05 per hour and an EC2 cost of $0.023 per hour, totaling $0.073 per hour. Similarly, the t2.2xlarge instance with software costs of $0.05 per hour and EC2 costs of $0.371 per hour totals $0.421 per hour.
Let’s discuss the features of Apache Kafka first:
- The distributed messaging system is the most prominent feature of Apache Kafka. This is a centralized system where producers send messages to topics, and consumers can read those messages.
For example, on Twitter, a person can post a tweet with a hashtag. This tweet will reach the consumers who subscribed to that hashtag.
- Another essential feature of Apache Kafka is Real Time Streaming. Real-Time Streaming enables Kafka to process data with low latency. Gaming is the most critical example of the importance of real-time streaming.
Many things happen simultaneously in multiplayer games, such as player movements or interactions. These actions must be communicated to all players quickly. Kafka shines in such scenarios.
The Spark features are as follows:
Spark performs in-memory computation, and this allows Spark to work at a faster speed as compared to traditional systems like MapReduce.
One key differentiating feature of Spark is its ability to connect with multiple data sources. It can connect with Hadoop’s HDFS, Apache Hive, Cassandra, etc.
Ease of use
Kafka provides APIs and client libraries in multiple programming languages like Java and Python. In Kafka, data producers send data to topics, and data consumers retrieve this data from the topics. This decoupled technique allows for scalable solutions. But, setting up Kafka clusters might be challenging for beginners.
And tuning Kafka for optimal performance requires technical expertise. Although Kafka allows basic stream processing, Kafka works with Spark for more complex tasks.
Spark’s in-built APIs for Java, Scala, Python, and Spark SQL make it accessible to many users. Its simple building blocks allow the writing of user-defined functions. Plus, the interactive mode allows immediate feedback. Extensive online resources for both of these technologies also enhance their ease of use.
Support and services
Apache Software Foundation has provided detailed documentation for Apache Spark and Apache Kafka. Users can read about concepts and get a practical head start. Moreover, various platforms provide support for setting up these technologies.
For Example, DoublCloud offers Kafka as a fully managed service. Moreover, both of these software have active contributors who provide continuous improvements.
Apache Kafka and Apache Spark have a rich community that connects through multiple channels, with JIRA being one of them. JIRA is an issue-tracking system developed by Atlassian that both Apache Spark and Kafka can use. Developers and programmers can interact through these communities to enhance the platform and open-source project.
Moreover, Kafka and Spark have a mailing list as well. Users can ask questions, report issues, and contribute to the project through the mailing list. Kafka and Spark have GitHub repositories as well. Lastly, Spark has an active subreddit community where developers and users engage with each other, while Apache Kafka doesn’t have one.
Kafka and Spark offer various integrations through modules and frameworks. Details of integration are as follows:
Apache Spark provides native integration with Apache Kafka through its Spark Streaming module. It allows Spark Streaming applications to consume data directly from Kafka topics.
Spark provides various integration to read and write data from multiple sources, such as dataframe, HDFS, and Cassandra.
Spark allows multi-faceted integration, as shown in the following image: