Welcome to Codingcompiler. Amazon Kinesis is an Amazon Web Services service that lets you capture, process, and analyze streaming data in real time. Recordable data includes video and audio data, IoT device telemetry data, or application data.
What Is Amazon Kinesis Data Streams?
Amazon Kinesis is an Amazon Web Services (AWS) service. Kinesis enables you to capture, edit and analyze large volumes of streaming data in real time and at high speed. The data can be different in nature and come from different sources.
For example, Amazon Kinesis collects video and audio data, telemetry data from Internet of Things ( IoT) devices, or data from applications and Web pages. On the basis of the processed and analyzed data, applications for machine learning or big data processes can be realized.
Unlike data batch processing, Amazon Kinesis responds in real-time and does not have to wait for the data to fully process itself. Data volumes of several hundred terabytes per hour can be easily processed with Amazon Kinesis.
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Differences between batch processing and stream processing
Amazon Kinesis controls the stream processing of data. This type of data processing is clearly different from the usual batch processing. Conventional batch processing works in single steps. First, data is collected.
These are then loaded into a database at regular intervals. The analysis and processing of the data takes place at a further time interval by accessing this database. Until current data can be edited and analyzed, a certain amount of time passes.
Stream processing takes a different approach. Instead of applying database queries to stored data, processes continuously process and analyze the data as it arrives. A previous saving in a database is not necessary. The stream processing platform must be able to process and analyze the data at the speed of its arrival, and requires appropriate performance.
Based on stream processing, faster results can be achieved than with conventional batch processing. A challenge for the stream processing represents the required performance of the systems.
The real-time processing is resource-consuming and requires high throughput rates as well as minimal latency times. It requires a high-performance, high performance server network that meets the processing speed requirements. Amazon Kinesis provides these systems for real-time stream processing.
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The benefits of Amazon Kinesis
Important advantages of Amazon Kinesis are:
- Capture, buffer, process and analyze streaming data in real time
- quick insights into large amounts of data
- Provide a fully managed and powerful infrastructure for streaming large volumes of data
- good scalability of the system for any amount of streaming data from a variety of sources
The different functional blocks of Amazon Kinesis
Amazon Kinesis is divided into four functional blocks. These function blocks are:
- Kinesis Video Streams
- Kinesis Data Streams
- Kinesis Data Firehose
- Kinesis Data Analytics
Kinesis Video Streams is designed to capture, process and store video streams. Videos can be streamed to Amazon Web Services from various devices for video analytics, machine learning, or other purposes.
Kinesis Data Streams captures, processes and stores data from real-time applications. The processing is supported by various frameworks.
Kinesis Data Firehose allows you to load data streams into AWS data stores. The streams can be captured and transformed with Kinesis Data Firehose. BI tools (Business Intelligence Tools) perform the real-time analysis of the data in the data stores.
Using Kinesis Data Analytics , data streams can be parsed using the standard SQL database language. This provides a very simple way to process and analyze the streaming data without a framework or special programming language.
Application examples for Amazon Kinesis
On the basis of Amazon Kinesis and the various functional blocks numerous applications are feasible. For example, cameras can stream videos to AWS that are analyzed in real time for face detection or security monitoring.
Streaming data from IoT devices can be evaluated and actions taken. Thus, a particular process can be started when a threshold value of a sensor is exceeded. Imminent disturbances can be detected early on during operation. Processes trigger appropriate measures, such as commissioning a service or ordering a replacement part.
For example, Netflix uses Amazon Kinesis to monitor communications between different applications and fix problems quickly. This makes it possible to achieve high availability of the system. Sonos uses Kinesis to monitor events on wireless hi-fi devices.
On the basis of Amazon Kinesis applications are programmable, which analyze the customer and order behavior in an
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