Amazon Kinesis and Elasticsearch Service

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Amazon Kinesis

Amazon Kinesis makes it easy to collect, process, and analyze real-time streaming data. So the user can get timely insights and react quickly to new information. It is also known as a streaming pipeline that allows its users to get data into the Elasticsearch service.

Three sub-services come with different capabilities under the Amazon Kinesis service group.

Kinesis Streams: Amazon Kinesis Streams stores data as a continuous replayable stream for custom applications. The user can use different frameworks or technologies according to their choice to process the data in real-time, panicking the stream. A KCL at Kinesis’s Client Library application can use spark streaming. The user can use the Lambda function or Kinesis analytics.

Kinesis Firehose: It’s an abstraction layer on top of the Kinesis stream. It automatically loads streaming data in real-time into different analytical and data storage destinations, including the S3 Redshift and Amazon Elasticsearch service.

Kinesis Analytics: Kinesis Analytics allows its users to use standard language to run queries and analyses against the data stream directory. So, the user can use the SQL scale, which most of customers already have today, to run a real-time analysis against the real-time data stream to get analysis results.

What is Kinesis Firehose?

It allows its users to deliver streaming (event) data into destinations such as BI databases, data exploration tools, dashboards, etc. It’s fully managed with elastic scaling that responds to increased throughput and allows users to batch many events into a single output file.

Key Concepts of Amazon Kinesis Firehose

Delivery Stream: The underlying entity of Firehose. The user can use Firehose by creating a delivery stream to a specified destination and sending data to it.

Record: The data of interest that an organization’s data producers send to a delivery stream. A record can be as significant as 1000 KB.

Data Producers: Producers send records to a delivery stream. For example, a web server that sends log data to a delivery stream is a data producer.

Data Flow Overview of Amazon Kinesis

  1. Capture data and submit streaming data to Firehose.
  2. Firehose loads streaming data continuously into Amazon S3, Redshift, or Elasticsearch Service.
  3. Analyze streaming data using any analytical tool.

Zero Administration: Capture and stream data into Amazon S3, Redshift, and Elasticsearch Service without writing an application or managing infrastructure.

Direct-to-Store Integration: Batch, compress, and encrypt streaming data for delivery into data destinations in as little as 60 seconds using simple configurations.

Seamless Elasticity: Seamlessly scale to match data throughput without intervention.


Amazon Kinesis – Firehose Vs. Stream

Amazon Kinesis Stream: It’s for the use case that requires custom processing, per incoming record, with sub-second processing latency and choice of stream processing frameworks.

Amazon Kinesis Firehose: Kinesis Firehose is for use cases that require zero administration, the ability to use existing analytics tools based on Amazon S3, Amazon Redshift, Amazon Elasticsearch, and a data latency of 60 seconds or higher.

Why Kinesis Firehose for Elasticsearch

  • Easy to Use
  • Integrated with AWS Data Store
  • Serverless Data Transformation
  • Near Real-Time
  • No Ongoing Administration
  • Pay Onley for What You Use

Amazon Elasticsearch service is a cost-effective managed service that makes it easy to deploy, manage, and scale open-source Elasticsearch for log analytics, full-text search, and more. An enterprise can run all its streaming applications without having to deploy and maintain costly infrastructures. Amazon Kinesis can handle any amount of streaming data and process it from hundreds of sources with low latency.

Amazon Elasticsearch Service Benefits

  • Easy to use
  • Supports open-source APIs and Tools
  • Secure
  • Highly Available
  • Tightly integrated with other AWS Service
  • Easily Scalable


Author: SVCIT Editorial Copyright

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