Data has been the central hotspot for all organizations. Merely collecting and analyzing data is not sufficient now. Data must be streamed and processed in real-time to get the best output. The need for data processing and streaming solutions is increasing and these two names are reigning high in this arena – Kinesis vs Kafka.
Apache Kafka and Amazon Kinesis are both popular streaming analytics software that offers attractive visualization and real-time reports based on the data streaming from disparate sources. These two messaging queue systems are competitive and well known for their salient features.
It is difficult for organizations to decide which one to choose. Before we compare them, based on different parameters, let us individually understand what they are.
What Is AWS Kinesis?
Amazon Kinesis makes it easy to collect, process, and analyse real-time, streaming data so you can get timely insights and react quickly to new information
AWS Kinesis or Amazon Kinesis provides competencies that can effectively stream data flexibly and in a scalable manner. It also lets users choose the best tools that fit the requirements perfectly. It helps in swiftly collecting, processing, and analyzing data streams and video in real-time.
It is a managed, cloud-based service that facilitates real-time streaming of data per second. It takes data from huge, distributed streams, social media feeds, and event logs. Once the data is processed, it is distributed to many users at the same time. The major components of Kinesis consist of Kinesis Video Streams, Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics.
- Ingestion, buffering, and processing of real-time data streaming
- Faster insight into information
- Fully managed with no dependency
- Capability to manage any amount of data streaming from multiple sources
- Graphs and metrics to create different reports
- Serverless and no need to provision infrastructure
What Is Apache Kafka?
Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Powered by Apache, Kafka is one of the most active products of the Apache Software Foundation. It offers enriched documentation – online training, tutorials, sample projects, and wide community support. It is used to create real-time streaming data pipelines. It is a perfect combination to message, store, and stream data, permitting storage and analysis.
It has excellent performance, is fast, effective, and easy to set up. It has fault-tolerant storage because of which it is trustworthy and secure. It is a distributed messaging system that can get data from different source systems. It is written in Scala and Java. It has emerged from a messaging queue to a comprehensive event streaming platform. It possesses the capacity of managing trillions of events per day.
- High-end throughput with latency as low as 2 ms
- Easy expansion and contraction of storage and processing
- Safe, durable, and distributed storage of data
- Effective connectivity of clusters
- Event streaming with filters, aggregations, joins, etc.
- Integration with multiple event sources
- Reads, writes, and processes events in many programming languages
Evaluation Of AWS Kinesis vs Kafka
As we start comparing Amazon Kinesis vs Kafka, there are certain similarities that can be observed in both, here are they:
- Distributed platforms for messaging
- Streaming analytics software solutions
- Reporting and visualization
- Works with streaming data like stock prices, geospatial data, IoT sensors, etc.
- Designed for low latency, high-performance applications
- Message processing via a centralized processor
- Management of large data streams effectively
- Tracking of log events and processing complicated, real-time data streams
- Customizable, flexible, robust, and secure
Kinesis vs Kafka – Comparison Between Two Streaming Services
|Parameters||Amazon Kinesis||Apache Kafka|
|Storage of Data||Data is stored in shards. Stores data for 24 hours to 7 days.||Data is stored in partitions. Stores data for as much time as needed.|
|Architecture||The basic architecture consists of Producers, Consumers, and Kinesis Data Streams. Messages are pushed into KDS for conversion to shards.||The basic architecture consists of Producers, Consumers, and Topics. Messages are pushed into topics for conversion to partitions.|
|Nature of Service||AWS service with a managed platform. Needs no team for implementation or management.||Open-source, distributed messaging solution. Needs a team for the management and installation of clusters.|
|Costing||Inhouse costs are lower since there is less management and hence easy to handle. Payment is for shard hours, payload units, and data retention.||Has more of inhouse costs because it needs a team to be handled. Payment is for per hour bill irrespective of the number of messages.|
|Retention Period||Data is accessible from 24 hours to about 365 days||Data is accessible for 7 days and can be changed later|
|Metrics Monitoring||Data monitoring metrics like CloudWatch metrics, Kinesis Agent, Kinesis Client Library, etc.||Inbuilt metrics like client metrics, thread metrics, task metrics, etc.|
|SDK Support||Amazon SDK supports Java, Android, Go, .NET||Apache Kafka supports Java|
|Event Processing||Around thousands of events per second||More than thousands of events per second|
|Configuration Store||Amazon DynamoDB||Apache Zookeeper|
|Skilled Expertise Needed||Basic knowledge needed||Advanced knowledge needed|
|Local Execution||Cloud-based service and hence cannot operate locally||Can execute on local machines since it is free and open-source|
|Security||Provides server-side security with encryption and AWS KMS master keys through Amazon Virtual Private Cloud||Provides client-side security features through SSL or SASL with data encryption with TLS or SSL|
|Log Compaction||Not supported||Supported|
|Replication Capabilities||Messages replicate to three availability zones||Makes use of MirrorMaker for topic replication|
|Scalability||Users can make use of an API call for increasing number of shards||Users can add on increasing number of partitions to a topic|
|Setting Up||Takes a couple of hours||Takes a few weeks|
|Components||Video Streams, Data Streams, Data Analytics, Data Firehose||Kafka Streams, Kafka Connect|
|Operational Management||Efforts to manage operations are lesser since it is managed. No need to worry about replication and scaling.||Efforts to manage and maintain the Kafka cluster are more since it needs more resources. Replication and scaling are needed.|
Kinesis vs Kafka – Wrapping Up
Based on the above comparison and assessment, if there is a totally new project, Kinesis would be a better choice. If there are existing clusters, Kafka would be better. If there is more flexibility and scalability available, Kafka is a better choice. If you need a managed solution and there is no time to spend on setting up the infrastructure, Kinesis could prove better.
The comparison between AWS Kinesis Vs Kafka has been interesting. There is something in common and yet they both hold their own significance. Selecting an apt data streaming solution relies on different factors such as budget, administrative resources, work culture, the volume of work, and organizational needs.
Overall, be it Kafka or Kinesis, the choice lies in the hands of the organization after taking into consideration different factors. Both are good, both are popular, and both are reliable. Both have their own individualities which must be appreciated and recognized. There lies the main crux!