Data is all around us. There is a firehose of information coming from social networks, financial trading floors, and geospatial services. Collecting, storing, and analyzing this type of high throughput information helps organizations stay up-to-date with customers but requires complex infrastructure that can be expensive to manage. A surge in changing user preferences interwoven with data management complexity becomes strenuous for companies to be efficient while offering solutions. When it comes to the field of Data Streaming, the Amazon Kinesis vs Kafka choice can be a relatively tough one to make.
What may have started as a simple application that requires stateless transformation soon may evolve into an application that involves complex aggregation and metadata enrichment. One has to build frameworks to handle TimeWindows, late-arriving messages, out-of-order messages, lookup tables, aggregating by key, and more. Managing and debugging becomes increasingly difficult for companies while scaling to serve a larger userbase. This is where data streaming as technology was introduced for simplifying the generations of insights in real-time. It deals with capturing data from cloud services, sensors, mobile devices, and software applications in the form of streams of events to process information in real-time.
This article provides you with a comprehensive analysis of both Data Streaming Platforms and highlights the major differences between them to help you make the Amazon Kinesis vs Kafka decision with ease. It also provides you a brief overview of both tools. Read along to find out how you can choose the right Data Streaming Platform tool for your organization.
Table of Contents
- Understanding of real-time Data Analytics
Introduction to Amazon Kinesis
Amazon’s Kinesis Data Streams offers a scalable and durable real-time data streaming service capable of capturing GBs and TBs of data per second from multiple sources. Be it financial transactions, social media feeds, IT logs, and location-tracking events. With Kinesis, companies can harness the potential of data in milliseconds to enable real-time dashboards, real-time anomaly detection, dynamic pricing, and more. As new data arrives, Kinesis turns raw data into detailed, actionable information and can start running real-time analytics by incorporating the provided client library into your application and then auto-scale the computation using Amazon EC2.
For instance, popular video streaming platform Netflix uses Amazon Kinesis Data Streams to centralize flow logs for its in-house solution — Dredge, which reads the data in real-time from Amazon’s Kinesis Data Streams and gives a complete picture of the networking environment by enriching the IP addresses with application metadata.
Netflix’s application then joins the flow logs with application metadata to index it without using a database, thereby avoiding numerous complexities. According to Netflix, Amazon’s Kinesis Data Streams-based solution has proven to be highly scalable, processing billions of traffic flows every day. Typically, about 1,000 Amazon Kinesis shards work in parallel to process the data stream. Using Amazon Kinesis Data Streams, Netflix is now able to identify new ways to optimize its applications.
To learn more about Amazon Kinesis, click this link.
Introduction to Apache Kafka
Apache Kafka is an open-source distributed event streaming platform used for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Kafka Streams, especially, allows users to implement end-to-end event streaming. With Kafka as a data stream platform, users can write and read streams of events and even import/export data from other systems.
Apache Kafka is a distributed, highly scalable, elastic, fault-tolerant, and secure data stream platform that can be deployed on bare-metal hardware, VMs, and containers, on-premises, as well as in the cloud. Users can also choose between self-managing their Kafka environments and fully managed services offered by various vendors. According to the developers, Kafka is one of the five most active Apache Software Foundation projects and is trusted by more than 80% of the Fortune 100 companies.
For instance, Image sharing company Pinterest uses Kafka Streams API to monitor its inflight spend data to thousands of ad servers in mere seconds. Pinterest picked Kafka Streams over Apache Flink and Spark for its millisecond delay and lightweight features. Kafka has no external dependencies, which minimizes maintenance costs.
To learn more about Apache Kafka, click this link.
Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports Apache Kafka, along with 150+ data sources (including 30+ free data sources), and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
Get Started with Hevo for free
Check out why Hevo is the Best:
Sign up here for a 14-day Free Trial!
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Factors that Drive the Amazon Kinesis vs Kafka Decision
Now that you have a basic idea of both technologies, let us attempt to answer the Kinesis vs Kafka question. There is no one-size-fits-all answer here and the decision has to be taken based on the business requirements, budget, and parameters listed below. The following are the key factors that drive the Amazon Kinesis vs Kafka decision:
1) Amazon Kinesis vs Kafka: Architecture
Apache Kafka’s architecture has producers and consumers playing a pivotal role. Producers are those client applications that “write” events to Kafka, and consumers are those that “read and process” these events. To achieve scalability, Kafka has decoupled producers and consumers and is agnostic of each other. The architecture of Apache Kafka is shown below.
As shown above, an event is organized and durably stored in topics (ex: payments). The number of producers in a topic can range from zero to many, and the same goes for consumers that subscribe to these events. In Kafka, these topics are partitioned into several “buckets” located on different Kafka brokers. Such distributed placement of data is critical for scalability. It allows client applications to both reads and writes period the data from/to many brokers simultaneously.
Whenever a new event is published on a topic, it is appended to one of the topic’s partitions. Unlike traditional messaging systems, events in a topic can be read as often as needed. Kafka’s configurations are customized for topics, and consumers’ data retention can be prolonged or shortened based on applications.
On the other hand, the architecture of Amazon Kinesis can be thought of as a collection of shards. A shard is a unique collection of data records in a stream and can support up to 5 transactions per second for reads and up to 1,000 records per second for writes.
Kinesis uses a partition key associated with each data record to determine which shard a given data record belongs to. When an application injects data into a stream, it must specify a partition key. The total capacity of the stream is dependent on the number of shards and is equal to the sum of the capacities of its shards. The architecture of Amazon Kinesis is shown below.
2) Amazon Kinesis vs Kafka: SDK Support
Kafka Streams is a stream processing Java API provided by open-source Apache Kafka. Any Java or Scala application that uses the Kafka Streams library is considered a Kafka Streams application. If an application is written in Scala, developers can use the Kafka Streams DSL for Scala library, which removes much of the Java/Scala interoperability boilerplate as opposed to working directly with the Java DSL.
3) Amazon Kinesis vs Kafka: Retention
The retention period in the context of data stream platforms is the period of time certain data records are accessible after they are added to the stream. The default retention period for Apache Kafka is seven days, but users can change this using various configurations.
Stream retention period on Kinesis is usually set to a default of 24 hours after creation. Kinesis allows users to increase the retention period up to 365 days using the “IncreaseStreamRetentionPeriod” operation. And by using the “DecreaseStreamRetentionPeriod” operation, the retention period can be even cut down to a minimum of 24 hours. Streams with a retention period set to more than 24 hours will be charged more.
4) Amazon Kinesis vs Kafka: Monitoring
The Kafka Streams library offers a variety of metrics through Java Management Extensions (JMX). Here are a few built-in metrics to monitor Kafka stream applications:
- Client Metrics allows users to check: These include the version of the Kafka Streams client, the topology executed in the Kafka Streams client, and the state of the Kafka Streams client.
- Thread Metrics allows users to check: These include the execution time in milliseconds across all running tasks of the thread, the time spent by a thread on the respective operations for active tasks, and the average number of newly-created tasks per second.
- Task Metrics allows users to check: These include the average number of respective operations per second for a task, the lateness of a task with the timestamp, and the measure end-to-end latency of a task.
Developers can add additional metrics to their applications using the low-level Processor API.
Users can monitor their data streams in Amazon Kinesis Data Streams using the following features:
5) Amazon Kinesis vs Kafka: Pricing
Apache Kafka is open-source. As a result, there are no initial costs.
Amazon Kinesis has provision-based pricing. The above prices are with regards to the US East location and might change with location. The pricing is calculated in terms of shard hours, payload units, or data retention. A sample calculation on a monthly basis:
Shard Hour: One shard costs $0.015 per hour, or $0.36 per day ($0.015*24). If a stream has four shards, it will cost $1.44 per day ($0.36*4). For a month with 31 days, the monthly Shard Hour cost is $44.64 ($1.44*31). Try the Kinesis price calculator here.
For more information, check the Amazon Kinesis Data Streams Pricing page.
This article gave a comprehensive analysis of the 2 popular Data Streaming Platforms in the market today: Amazon Kinesis and Apache Kafka. It talks briefly about both tools and gave the parameters to judge each of them. Overall, the Amazon Kinesis vs Kafka choice solely depends on the goal of the company and the resources it has.
If the user wants flexibility with configurations, then Apache Kafka might be the right choice. But, if the user doesn’t want to take the burden of initial setup and integration that might take weeks with Kafka, it is better to leverage Amazon Kinesis to set up and start running with relative ease. For any information on Kafka Exactly Once, you can visit the following link.
In case you want to integrate data from data sources like Apache Kafka into your desired Database/destination and seamlessly visualize it in a BI tool of your choice, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and destinations.
Visit our Website to Explore Hevo
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand.
Share your experience of learning about Amazon Kinesis vs Kafka in the comments section below.