Data streaming is the process of continuous data transfer from various sources. You can use these streams of data for further processing and real-time analysis. They help you gain insights into customer activity, preferences, and purchase behavior, which you can utilize to improve your business performance. Data streaming also has other critical applications, such as fraud detection, location tracking, and media streaming.
There are many software tools that you can use to stream data. A good knowledge of the different features offered by any data streaming service helps you choose a suitable one according to your requirements. This article compares Kinesis vs. Kafka, two popular data streaming platforms, and highlights their strengths to help you decide which is ideal for your usage.
Kinesis Overview
Kinesis is a real-time data streaming service provided by Amazon Web Services. It allows you to stream and analyze massive amounts of data. This data can be in any form, such as audio, video, application logs, website clickstreams, and IoT telemetry.
Kinesis provides four specialized services, which are classified according to the stages of processing streaming data. These are Kinesis Data Streams, Kinesis Data Firehose, Kinesis Data Analytics, and Kinesis Video Streams.
Unique Features of Amazon Kinesis
- Serverless: You do not have to manage servers while using Amazon Kinesis Data Streams. It provides an on-demand scaling mode, which automatically scales capacity when workload traffic increases, removing the need to manage throughput.
- High Availability: Kinesis distributes your data streaming workloads across various AWS availability zones for redundancy and fault tolerance. It also allows you to retain data for up to 24 hours by default, which you can extend up to 365 days. Kinesis also enables automatic recovery from failures.
- Monitoring: Kinesis provides monitoring services for your data stream through features like CloudWatch metrics, Kinesis Agent, API logging, and Kinesis Client Library, providing insights into your data streams.
- AWS Integration: With Kinesis, you can gather data from AWS services, such as AWS EventBridge and AWS Simple Queue Service. You can then process the collected data using tools such as AWS Lambda and AMS for Apache Flink. It also facilitates integration with other tools like Apache Spark, Apache Flink, and Quix.
Kafka Overview
Apache Kafka is an open-source distributed event store and stream-processing platform. It allows you to publish (write) and subscribe (read) streams of events and continuously import or export data from other systems.
Kafka also enables you to store data streams for 7 days by default, and you can retain these streams for as long as you want by configuring Kafka based on the time period or size of the data stream. Using this platform, you can stream data in a distributed, highly scalable, elastic, fault-tolerant, and secure way.
Unique Features of Apache Kafka
- Scalability: Kafka offers horizontal scaling by adding new servers to handle increasing data volumes. It also facilitates partitioning, replication, and rebalancing to provide better scalability.
- Durable Performance: The messages in Kafka are small pieces of data and are delivered to you at least once while streaming. Using replication, Kafka assures that each message is written into multiple brokers, creating many copies of the data. This prevents it from losing your data in case of broker failure.
- Ease of Use: It offers a simple interface and allows you to easily integrate your data with platforms like Postgres, Elasticsearch, AWS S3, etc.
- Low Latency: You can achieve low latency data streaming in Kafka through its distributed architecture, persistent storage, compression techniques, and partitioning capabilities.
AWS Kinesis vs. Kafka: Quick Comparison
Quick comparisons of AWS Kinesis vs Kafka are represented in the table below:
AWS Kinesis vs. Kafka
Here’s a detailed comparison of architecture, configuration, performance tuning, costs, and more between Kinesis and Kafka:
Architecture
Architecture is the most important point in the Kinesis vs. Kafka comparison.
Kinesis: A key element of Kinesis architecture is a shard. Each shard consists of data records. These data records contain a sequence number, partition key, and data blob. The sequence number acts as a unique identifier assigned to data records, while the partition key separates records into different shards of a stream. A data blob consists of non-changeable bytes of data.
Applications that write data to Kinesis streams are called producers. AWS provides a Kinesis Producer Library (KPL) that facilitates producer development. Data is then received or read by applications called consumers. You can also develop consumer applications easily using the Kinesis Consumers Library (KCL).
Kafka: Brokers, producers, and consumers are the most important components of Kafka architecture. Brokers store and manage the data records in topics, which are categories used to organize data records in Kafka.
Producers send data to topics, and consumers subscribe to topics to receive data streams. Both producers and consumers are client applications that interact with Kafka through APIs to publish or subscribe to topics.
Configuration
Kinesis: You can easily set up Kinesis within hours to start the data streaming process. AWS manages Kinesis's infrastructure, which simplifies the deployment and maintenance of hardware and software. Kinesis also allows you to interact with its brokers from outside AWS with the help of Kinesis APIs and Amazon Web Service (AWS) SDKs.
Kafka: Compared to Kinesis, setting up Apache Kafka can be more complex and time-consuming. This is because it involves a complex deployment process that requires a cluster, a high number of nodes (brokers), configuring replication, and managing software. Thus, in comparison to Kinesis, Kafka demands technical expertise for configuration setup.
Performance Tuning
Kinesis: Kinesis provides high availability through data replication across multiple availability zones. You can enhance the throughput by increasing the number of shards within a stream. However, the latency issues are handled by AWS. Thus, Kinesis provides only limited flexibility for manual performance tuning.
Kafka: To ensure optimal throughput and low latency, you have to configure producers and consumers in Kafka appropriately. Producers are tuned based on the number of bytes of data to be collected. Consumers are tuned based on the replication factor and a ratio of the number of consumers for a topic to the number of partitions. Thus, unlike Kinesis, you can manually calibrate Kafka for high availability according to your requirements.
Use Cases
Kinesis: Amazon Kinesis can be used to design applications that monitor your geographic locations for real-time data analytics. It is also used in fraud detection and streaming data from IoT (Internet of Things) devices.
Kafka: It is used to track website activities, such as user registration, page clicks, and purchases. You can also use Kafka for real-time data processing, messaging, collecting operational metrics for application monitoring, and log file aggregation.
Security
Kinesis: AWS Kinesis offers security features such as server-side encryption using AWS master keys or user-provided encryption libraries. It also provides access control mechanisms and compliance resources to prevent data security breaches.
Kafka: It provides you with data security features like authentication and authorization to meet necessary compliance regulations. You can also add your own layer of security at your expense to manage and monitor data streams in Kafka.
Costs
Kinesis: It has a pay-as-you-go model, where you pay according to your service usage. The pricing of Kinesis is based on the number of shards used and the Payload Unit, which is the size of data transmitted by producers to the data streams. This type of price structure saves you from investing money and resources in infrastructure management.
Kafka: As an open-source product, you do not need to pay for Kafka’s licensing cost. However, you must invest additional technical resources to set up and maintain it.
A Step Further - Streamline Data Movements with Airbyte
While Kinesis and Kafka are efficient solutions for real-time data movement, getting that data to its final destination, like a data warehouse or a data lake, often requires an additional step. This is where data integration and replication platforms like Airbyte can help you streamline the entire data movement from source to destination with simple steps.
With Airbyte’s 350+ in-built connectors, you can seamlessly integrate your data between various sources and destinations. The large library of connectors enables you to consolidate your extracted data at a unified destination of your choice.
Some key features of Airbyte that make it a favorable choice are:
- PyAirbyte is an open-source Python library that enables you to extract data from connectors supported by Airbyte within your Python environment.
- Airbyte supports CDC and facilitates capturing only incremental changes in data since the last synchronization. This eliminates unnecessary transfer of data and reduces processing time.
- Airbyte provides a Connector Development Kit (CDK), which allows you to build your own connector using minimal code.
Conclusion
Data streaming is essential for gaining real-time insights. This blog compares Kinesis vs. Kafka data streaming platforms to help you make an informed decision while choosing between these platforms. It comparatively analyzes the configuration, performance, costs, and security features of these two prominent streaming tools.
Both Kafka and Kinesis are robust real-time streaming platforms. However, you can choose any of these platforms for data streaming depending on your resources, preferences, and time. Kinesis, as a managed service requires less configuration and technical effort compared to Kafka’s self-managed approach. This can be a significant advantage if you prefer a quick setup time.
FAQs
Does Kinesis use S3?
Kinesis offers a Firehose service to stream data from S3 in real-time.
Which platforms can be used as data streaming tools?
There are many platforms that you can use for data streaming services, like Google Cloud DataFlow, Amazon Kinesis, Apache Kafka, Apache Storm, Azure Stream Analytics, etc.