Data surround us. There is a flood of data flowing in from social media, financial trading floors, and geolocation services. Collecting, storing, and analyzing this type of high flow data assists firms in staying in touch with customers, but it necessitates complicated infrastructure that can be costly to administer.
Two of the most popular messaging queue systems are Apache Kafka and Amazon Kinesis. However, many enterprises debate utilizing open-source Kafka or Amazon’s managed Kinesis service as data streaming platforms for stream processing. Here in this article, we will discuss the similarities and differences between Apache Kafka and Amazon Kinesis.
What is Amazon Kinesis?
Amazon Kinesis is used for the real-time processing of large amounts of data. The ability to process hundreds of terabytes of high-volume data streams per hour is a fundamental characteristic of Kinesis. This data may come from various places, including operational logs, websites, financial transactions, social media feeds, user behaviors, etc. Its advantage over prior technology is its capacity to make the building of specific apps more straightforward.
Netflix, for example, utilizes Amazon Kinesis Data Streams to centralize flow logs for its in-house solution — Dredge, which reads data in real-time from Amazon’s Kinesis Data Streams and provides a full view of the networking environment by supplementing IP addresses with application metadata.
The Netflix program then combines the flow logs with application information to index it without a database, avoiding various complications. According to Netflix, Amazon’s Kinesis Data Streams-based solution has proved to be very scalable, processing billions of traffic flows per day. As a result, Netflix can now uncover new methods to enhance its apps by utilizing Amazon Kinesis Data Streams.
What is Apache Kafka?
Apache Kafka is a data repository for streaming data. This open-source is used to design real-time streaming data pipelines and high-performance, fault-tolerant, and scalable applications. In addition, it separates applications that create streaming data (producers) from apps that receive streaming data (consumers) in its data store.
Apache Kafka’s distributed nature allows it to scale up and provide high availability in node failure. Organizations employ Apache Kafka as a data source for applications that analyze and respond to streaming data.
Pinterest, for example, utilizes the Kafka Streams API to monitor its in-flight expenditure data and send it to thousands of ad servers in seconds. Because of its millisecond latency and lightweight characteristics, Pinterest chose Kafka Streams over Apache Spark and Flink. There are no external dependencies in Kafka, which reduces maintenance expenses.
Amazon Kinesis vs Kafka: Main Difference
The main difference between Amazon Kinesis and Apache Kafka is their architecture. Amazon Kinesis comprises shards and Apache architecture producers, and consumers have a significant role in Kafka’s work.
Client applications that write events to Kafka are known as producers. These events are read and processed by consumers. To achieve scalability, Kafka separates producers and consumers. An event is first created and stored in the topic. Then, these topics are divided into many buckets, each hosted on a different Kafka broker. When a new event is posted to a topic, it is associated with one of the topic’s partitions.
In Amazon Kinesis, a shard is a one-of-a-kind collection of data records in a stream that can handle up to 5 transactions per second. To determine which shard a data record belongs to, Kinesis employs a key called partition, which is associated with each data record. A partition key should be specified whenever a program injects data into a stream. The number of shards determines the stream’s capacity.
Amazon Kinesis vs Kafka: Setup and Management
Depending on your team’s skills, setting up a full-fledged production-ready infrastructure using Apache Kafka might take weeks. For fault tolerance and high availability, an open-source distributed system needs its cluster, many nodes (brokers), replications, and partitions.
Setting up a Kafka cluster necessitates mastering distributed systems engineering practice, cluster administration, provisioning, auto-scaling, load-balancing, and many distributed DevOps, among other things.
On the other hand, Kinesis is quicker to set up compared to Apache Kafka, and a production-ready stream processing system may be put up in as little as a couple of hours. In addition, AWS provides the infrastructure, storage, networking, and settings required to stream data on your behalf because it is a managed service. Furthermore, Amazon Kinesis manages the provisioning, deployment, and ongoing maintenance of hardware, software, and other data stream services for you.
Amazon Kinesis vs Apache Kafka: Retention
The retention period refers to how long different data records can be accessed after being introduced to the stream. The default retention time in Apache Kafka is seven days. This period can also be changed.
The default retention time for Amazon Kinesis is 24 hours after the creation. The retention period can be extended up to 365 days. They can also reduce the retention time to as little as 24 hours.
Amazon Kinesis vs Apache Kafka: Pricing
Apache Kafka is a data streaming platform that is free to use and does not charge any fees. You pay for “shard hours” and “PUT payload units” with Kinesis, which are two units that reflect throughput and data transferred within a stream. You’ll pay extra if you want a higher throughput or send more data.
You also have to pay for data transfer, which adds to the uncertainty. The cost of transferring data out of AWS is the same for all three services; however, replication costs differ. You’ll replicate data across many AZs in a production service for redundancy. If you run Kafka on EC2, you’ll have to pay extra.
Amazon Kinesis vs Apache Kafka: Scalability
The “shard” is the unit of scaling in a Kinesis stream. Each shard has a 1MB write capacity, 1,000 records per second, and a 2MB read capacity or 5 transactions per second. You continue to add shards until you reach the desired capacity.
In Kafka, there are two scales – partition and broker. The underlying server in your Kafka cluster is the broker. Choosing the appropriate instance type and the number of brokers is more difficult than counting Kinesis shards.
Amazon Kinesis vs Apache Kafka: SDK support
Apache Kafka provides a Java API for stream processing called Kafka Streams. A Kafka Streams application is any Java or Scala application that uses the Kafka Streams library.
If an application is developed in Scala, developers may utilize the Kafka Streams DSL for the Scala library instead of working directly with the Java DSL, which avoids a lot of the Java/Scala compatibility boilerplate.
Amazon SDKs support kinesis Data Streams for Python, Golang, PHP, Java, JavaScript, .NET, Node.js, and Ruby. Furthermore, the Kinesis Client Library (KCL) provides a simple programming paradigm for data processing, allowing users to quickly start with Kinesis Data Streams in Java, Node.js,.NET, Python, and Ruby.
Wrapping up
Both AWS Kinesis and Apache Kafka are viable options for real-time data streaming solutions. For example, Apache Kafka should be your choice if you need to hold messages for more than 7 days with no limit on message size. Apache Kafka, on the other hand, takes additional effort to set up, administer, and support.
If your company lacks Apache Kafka experts and human assistance, opting for a fully managed AWS Kinesis solution will allow you to concentrate on development. In addition, AWS Kinesis is catching up in terms of throughput and event processing in terms of overall performance.
That’s it for this article.
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Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Machine Learning frameworks like PyTorch and Tensorflow is a testament to his versatility and commitment to the craft.