If you have to catch up with this expeditiously growing race of technology and have to work faster, will you choose to work after collecting all the supporting data, or will work with real-time data?
Obviously, the one where we can be rapid, and that is working with real-time data.
If you match my above assumption then let’s deep dive into the process of how we can achieve that:-
- Amazon Kinesis helps us to analyze and process data at the same time it arrives and instantly responds over it which makes it rapid over waiting for complete data to arrive and then traversing it.
- Using Amazon Kinesis, you can ingest real-time data such as:
- application logs,
- website clickstreams,
- and IoT telemetry data for machine learning, analytics, and other applications.
- Amazon Kinesis makes it easy to collect, process, and analyze real-time streaming data so that one can timely insights and react quickly to new information.
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So, I called streaming. Now, what’s that? Let’s dig it out…
A profusion of continuous flowing data which are generated by a data source is termed “Streaming Data”. The continuous generation of data causes its smaller size i.e, in order of kilobytes.
These streaming data are chronically analyzed with time and used for a wide scope of analytics and their filtering, aggregations, and utilization.
Let me make that more ease with examples:
1. Finance: Analysts use real-time fluctuations of candlesticks to read and analyze ongoing market demand and supply and help traders sharpen their trades. There are many analysis tools for chart analysis that uses the real-time flow of the market to draw charts like moving averages, relative strength index, and many more.
2. Website media publisher: A website media publisher uses streaming data for analyzing the interest and indulgence of their users with their content and uses it for further enhancement.
3. Online Gaming: Gamer has nothing more to do except improve their gaming strategies and hence they use streaming data of their game to read their lackings and light up their strengths.
Since we have an idea about Amazon Kinesis and streaming data now we can strike with the benefits we have in the bag.
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Benefits of Amazon Kinesis:
- Realtime analyzer: Amazon Kinesis let us derive insights in seconds and minutes rather than juggling for hours by ingesting, buffering, and processing data in real time.
- Scalable: Amazon Kinesis doesn’t limit the amount of streaming data and process data to be tackled. It can handle thousands of data sources and hence increases the scalability factor.
- Serverless: Amazon Kinesis is serverless, fully managed, and runs streaming applications without any requirement for infrastructure management.
- Reliable: Amazon Kinesis is based on Apache Kafka and hence it is expeditious, reliable, and easily operable.
With such benefits, Amazon Kinesis would be holding strength of capabilities. Isn’t it?
Let’s have a glance over there too…
Capabilities of Amazon Kinesis:
i) Kinesis Video Streams:
- Amazon Kinesis Video Streams make it easy to stream secured video. Videos can be streamed from devices connected to AWS and can be used for analytics, machine learning, and other processes.
- Amazon Kinesis Video Streams helps to play live videos and playback on-demand videos.
- It also helps to generate applications that take help from streaming analytics using Amazon Rekognition Video, and libraries for ML frameworks such as Apache MxNet, TensorFlow, and OpenCV.
- Amazon Kinesis Video Streams supports open-source projects that enable streaming and association of web pages, mobile applications, and joined devices via supportive APIs.
- Uses of Amazon Kinesis Video Streams can be peer-to-peer broadcasting and streaming, video chats, content analytics, and many more.
ii) Kinesis Data Streams:
- Kinesis Data Streams helps out in logging and analyzing the biggest data stored in gigabytes on regular basis filtered from applications, clickstreams, events of app users, and many more.
- It helps in building applications based on data streams and analytics gained in seconds using AWS Lambda or Amazon Kinesis Data Analytics.
- Amazon Kinesis Data streams are used in power event-driven applications.
- Amazon Kinesis Data Streams owes big customers like Comcast, Thomson Reuters, and Hearst Corporation.
iii) Kinesis Data Firehose:
- Kinesis Data Firehose delivers streaming data directly to Amazon S3( Simple Storage Service ) and converts data into the utilizable format of analysis without creating processing pipelines.
- Provided network security creates alerts whenever threats arise using SIEM( Security Information and Event Management ) tools.
- Enhances the achieved data streams using concepts of ML models to move streams to a valid destination. It also performs transformations which include compression, aggregation, encryption, data batching, and Lambda functions.
- 3Victors, Redfin, and Repp Health are some of the customers who fall in the category who uses Amazon Kinesis Data Firehose.
iv) Kinesis Data Analytics:
- Kinesis Data Analytics drivers stream data within seconds to open search services and more.
- One can run Apache Flank applications and maintain scalability using it and the upper hand is that it takes no setup cost and managing servers.
- Kinesis Data Analytics can be used to build applications in SQL, Java, Python, or Scala.
- BT groups, Nextdoor, and Lighttricks are some of the users that fall into these categories.
Key points to take home:
- Amazon Kinesis makes an easy to process, analyze, and aggregate streaming data in real-time and further reuse it.
- Analytics of streaming data has various fields of demand like finance, web applications, online gaming, travel companies, and many more.
- Amazon Kinesis holds various capabilities: Kinesis Video Streams, Kinesis Data Streams, Kinesis Data Firehose, and Kinetics Data Analytics.
- Various big users are relying on Amazon Kinesis and hence we can blindly put it in a reliable and secured category.