Kafka vs. Kinesis: A Deep Dive Comparison
Data comes at businesses today at a relentless pace – and it never stops. It’s a good thing too. The data-driven enterprise is more likely to succeed. According to McKinsey, “companies with the greatest overall growth in revenue and earnings receive a significant proportion of that boost from data and analytics.” But there’s a secret to fueling those analytics: data ingest frameworks that help deliver data in real-time across a business. This is where the Kafka vs. Kinesis discussion begins.
Both Apache Kafka and Amazon Kinesis handle real-time data feeds. Both are capable of ingesting thousands of data feeds simultaneously to support high-speed data processing. Whether to support machine learning, artificial intelligence, big data, IoT, or general stream processing, today’s business is hyper-focused on investing in data stream processing solutions, facilitated by these message brokering services.
Introduction to Event Streaming Platforms
As modern business needs have evolved, the monolithic app and singular database paradigm is quickly being replaced by a microservices architectural approach. The concept of microservices is to create a larger architectural ecosystem through stitching together many individual programs or systems, each of which can be patched and reworked all on their own.
This architectural evolution to microservices requires a new approach to facilitate near-instantaneous communication between these interconnected microservices. Enter message brokering from event streaming platforms like Apache Kafka and Amazon Kinesis.
Apache Kafka vs. Amazon Kinesis
Kafka and Kinesis are both very important components to facilitating data processing in modern data pipelines. And although both of these solutions are widely used in today’s business, they do offer some stark differences that every business should know about.
To better understand these event streaming platforms, we’ve put together a deep dive comparison analyzing the similarities and differences of Kafka and Kinesis.
Specifically, in this piece, we’ll look at how Kafka and Kinesis vary regarding performance, cost, scalability, and ease of use. At that, let’s dig in to a deep dive comparison between Kafka and Kinesis.
What is Kafka?
Apache Kafka is an open-source distributed event streaming platform (also known as a “pub/sub” messaging system) that brokers communication between bare-metal servers, virtual machines, and cloud-native services.
At a high level, Apache Kafka is a distributed system of servers and clients that communicate through a publish/subscribe messaging model. Streaming data is published (written to) and subscribed to (read from) these distributed servers and clients. Just like Kinesis, this asynchronous service-to-service communication model allows subscribers to a topic to immediately receive any message published to a topic.
Kafka has been a long-time favorite for on-premises data lakes. Used by thousands of Fortune 100 companies, Kafka has become a go-to open-source distributed event streaming platform to support high-performance streaming data processing. Here, streaming data is defined as continuously generated data from thousands of data sources. It’s Kafka’s responsibility to ingest all of these data sources in real-time and process and store data in the order it’s received. This attribute of the Kafka event streaming platform enables businesses to build high-performance data pipelines, streaming analytics tools, data integration applications, and an array of other mission-critical applications.
What is Kinesis?
Amazon Kinesis is an Amazon proprietary service that enables real-time data streaming. It collects, processes, and analyzes real-time streaming data within AWS (Amazon Web Services). As a replacement of the common SNS-SQS messaging queue, AWS Kinesis enables organizations to run critical applications and support baseline business processes in real-time rather than waiting until all the data is collected and cataloged, which could take hours to days.
As a cost-effective AWS-native service for collecting, processing, and analyzing streaming data at scale, Kinesis is designed to seamlessly integrate with a host of AWS-native services such as AWS Lambda and Redshift via Amazon Kinesis Data Stream APIs for stream processing. In doing so, Amazon Kinesis can ingest, catalog, and analyze incoming data for data analytics, sensor metrics, machine learning, artificial intelligence, and other modern-day applications.
Further, as a cloud-native solution, Kinesis is fault-tolerant by default, supports auto-scaling, and integrates seamlessly with AWS dashboards designed to monitor key metrics.
Kafka vs. Kinesis Comparison
Kafka is more highly configurable compared to Kinesis. With Kafka, it’s possible to write data to a single server. On the other hand, Kinesis is designed to write simultaneously to three servers – a constraint that makes Kafka a better performing solution.
Kinesis’ configurability is limited in how it must write to three servers synchronously within AWS. This requirement adds additional overhead to the Kinesis platform leading to degradation in performance.
Kafka requires more engineering hours for implementation and maintenance leading to a higher total cost of ownership (TCO).
As an AWS cloud-native service, Kinesis supports a pay-as-you-go model leading to lower costs to achieve the same outcome.
Kafka’s scalability is determined by brokers and partitions. A standard configuration of Kafka can reach a throughput of 30k messages per second. Kafka requires manual configuration for cross-replication
Kinesis scalability is determined by shards. A shard provides a write capacity of 1MB, or 1,000 records per second, and a read capacity of 2MB, or 5 transactions per second. Kinesis has built-in cross-replication between geo-locations.
Kafka requires a heavy amount of engineering to implement for its on-premises deployment, leading to unforeseen misconfigurations, vulnerabilities, and bugs.
Kinesis leverages more automated cloud-native services, removing the human element mitigating the risk of unforeseen misconfigurations, vulnerabilities, and bugs.
Ease of Use
Kafka requires a heavy lift for implementation, making it a more challenging solution to implement, use and maintain.
Kinesis is designed for easy implementation. Spinning up Kinesis within AWS can be done with just a few clicks, making it a much easier service to spin up, use and maintain.
When considering a larger data ecosystem, performance is a major concern. Businesses need to know that their data stream processing architecture and associated message brokering service will keep up with their stream processing requirements. That said, when looking at Kafka vs. Kinesis, there are some stark differences that influence performance.
One of the major considerations is how these tools are designed to operate. By design, Kinesis will synchronously broker data streams and write and replicate ingested data into three different AWS machines. This replication cannot be reconfigured, influencing resource overhead such as throughput and latency.
Kafka gives more control to the operator in its configurability than Kinesis. It allows operators to configure the data publishing process to as little as one machine, removing some of the overhead seen with Kinesis. Here, Kafka is the clear winner.
Amazon’s Kinesis follows the typical cloud pricing structure: pay-as-you-go removing the requirement for on-premise data centers. Amazon’s Kinesis requires no upfront costs to set up (unless an organization seeks third-party services to configure their Kinesis environment). Amazon Kinesis also has no minimum fees, and businesses can pay only for the resources they require. Kinesis Data Streams can be purchased via two capacity modes – on-demand and provisioned.
When we look at Kafka, whether in an on-premises or cloud deployment, cost is measured more in data engineering time. It takes significant technical resources to implement the solution fully and keep it running efficiently. For this reason, Kinesis is generally more cost-effective than Kafka.
Although Kafka and Kinesis are highly configurable to meet the scale required of a data streaming environment, these two services offer that configurability in distinctly different ways.
For Kinesis, scaling is enabled by an abstraction of the Kinesis framework known as a Shard.
A shard is the base throughput unit of a Kinesis data ingestion stream. By definition, a shard provides a write capacity of 1MB, or 1,000 records per second, and a read capacity of 2MB, or 5 transactions per second. Further, one given shard can support up to 1000 PUT records per second.
With Kafka, scalability is highly configurable by the end-user providing both benefits and challenges. There are two primary components of the Kafka architecture at a high level that influence throughput, known as Kafka brokers and the Kafka partitions. When first configuring a Kafka environment, one starts by configuring a Kafka cluster and defining the broker as the underlying server to the Kafka cluster. Here, choosing the right instance type for the Kafka cluster and the number of brokers will profoundly impact throughput.
Unfortunately, selecting an instance type and the number of brokers isn’t entirely straightforward. Typically this comes down to some fine-tuning on the fly. Following Amazon’s sizing guide can help, but most organizations will reconfigure the instance type and number of brokers according to the throughput needs as the scale.
Comparable to Kinesis, Kafka partition offers the same functionality as a Kinesis shard. Much like the Kinesis shard, the more Kafka partitions configured within a Kafka cluster, the more simultaneous reads and writes Kafka can perform. And if you’re wondering how this all boils down to throughput capabilities for Kafka, as a quick rule of thumb, Kafka can reach a throughput of 30k messages per second.
Aside from some of the scaling nuances between Kafka and Kinesis mentioned above, cross replication is a major concern for those looking to replicate streaming data. By default, Amazon Kinesis offers built-in cross replication between geo-locations; Kafka requires replication configuration to be done manually – a major consideration regarding scalability.
Kafka and Kinesis are similarly positioned when it comes to security, with a couple of key differences.
First on the list is immutability. Both Kafka and Kinesis support immutability in how they write to their respective databases. The immutability functionality disallows any user or service to change an entry once it’s written. This promotes a high degree of dependability and data durability both by Kafka and Kinesis and greatly mitigates the risk of data destruction or security vulnerabilities.
We also come to a draw when it comes to the security inherent to the cloud vs. the higher configuarability of security available in Kafka. Here, arguments for and against could be made on both sides, and it’s largely a matter of preference.
However, the human element (or lack thereof) is where Amazon Kinesis may gain an edge over Kafka regarding security. Since Kafka requires such a substantial heavy lift during implementation compared to Kinesis, it inherently introduces risk into the equation. Anytime, a large number of engineering resource hours are required for implementation, it also introduces the chance of bugs, misconfigurations, and vulnerabilities.
Ease of Use
Lastly, let’s address ease of use. Since we’ve hit on this quite a bit in this piece, we’re sure you can guess the winner here. Right? Yep. Amazon Kinesis.
Since Amazon Kinesis is a cloud-native pay-as-you-go service, it can be spun up easily and preconfigured to integrate with other AWS cloud-native services on the fly. On the flip side, Kafka typically requires physical on-premises self-managed infrastructure – lots of engineering hours and even third-party managed services to get it up and running.
StreamSets supports Apache Kafka as a source, broker, and destination allowing you to build complex Kafka pipelines with message brokering at every stage, and has supported stages for Kinesis too. To learn more, contact us today or get started building pipelines for free.