In this article you can easily understand what is aws kinesis and difference between aws kinesis vs kafka:
Amazon Kinesis is a fully managed, cloud-based service for real-time processing of streaming data at scale. It enables you to build custom applications that process and analyze data as it arrives, and respond in real-time to changing business and market conditions.
With Amazon Kinesis, you can ingest, process, and analyze real-time streaming data using your own custom applications. You can also use it to build real-time dashboards, perform anomaly detection, and create alerts.
Some common use cases for Amazon Kinesis include:
- Real-time processing of log and event data
- Real-time analytics and visualization of streaming data
- Real-time data ingestion and integration with other systems and data stores
- Stream processing of social media data, IoT data, and machine learning model training data
Amazon Kinesis consists of the following components:
- Streams: A stream is a real-time data feed that can handle terabytes of data per hour from hundreds of thousands of sources. Streams can be used to continuously collect and process data from sources such as social media feeds, website clickstreams, IoT devices, and financial transactions.
- Producers: Producers are applications or devices that generate data and send it to a stream.
- Consumers: Consumers are applications or processes that read and process data from a stream.
- Shards: A shard is a unit of capacity in a stream, and it serves as the foundation for building custom stream-processing applications. Each shard can support up to 1,000 records per second for reads, up to 2 MB per second for writes, and up to 1 MB per second for data retrieval.
You can use Amazon Kinesis with a variety of programming languages and tools, including Java, Python, C++, and the AWS SDKs. You can also use Kinesis with managed services such as Amazon EMR, Amazon Redshift, and Amazon Elasticsearch Service to perform advanced analytics on your data.
aws kinesis vs kafka:
Amazon Kinesis and Apache Kafka are both real-time data streaming platforms that can process and analyze large volumes of data in near real-time. However, there are some key differences between the two:
- Architecture: Amazon Kinesis is a fully managed service offered by Amazon Web Services (AWS), while Apache Kafka is an open-source solution that can be installed and run on-premises or in the cloud.
- Scalability: Both Amazon Kinesis and Apache Kafka are highly scalable, but Kafka is generally considered to be more scalable than Kinesis. Kafka is designed to handle millions of messages per second, and it can handle high levels of throughput without the need for additional hardware or infrastructure. Kinesis, on the other hand, is limited to a maximum of 10,000 records per second.
- Ease of use: Amazon Kinesis is generally easier to set up and use than Apache Kafka, as it is a fully managed service and does not require any infrastructure or server setup. Kafka, on the other hand, requires more setup and maintenance, as it is an open-source solution.
- Cost: Amazon Kinesis is generally more expensive than Kafka, as it is a fully managed service with a pay-as-you-go pricing model. Kafka is an open-source solution that can be run on your own infrastructure, so it can potentially be less expensive than Kinesis depending on your setup.
Overall, the choice between Amazon Kinesis and Apache Kafka will depend on your specific use case and requirements. If you need a fully managed, easy-to-use solution with good scalability and are willing to pay for it, Amazon Kinesis might be a good option. If you prefer a more flexible, open-source solution that requires more setup and maintenance, Kafka might be a better choice.