Amazon Kinesis is an AWS feature which enables gathering, processing and analyzing real-time data and video streams. This helps us in getting timely insights and making decisions effectively. It possesses the key capabilities to process the streaming data very flexibly by making use of the tools that well fulfill the requirements of your application. Amazon Kinesis helps in consuming vast varieties of real-time data such as audio, video, website clickstreams, application logs, etc. It can also process IoT data as well, for analytics, machine learning and other applications, etc. It is very instant when it comes to responding without making you wait until the entire data collection and processing happens.
AWS Kinesis Data Streams
Amazon Kinesis Data Streams (KDS) is a real-time data streaming service. It is quite durable and scalable. It can continuously abduct huge quantity of data, like in some gigabytes or terabytes. It can acquire so from numerous sources, probably hundreds or thousands of sources like financial transactions, IT logs, social media feeds, database event streams, website clickstreams, etc. There are various benefits offered by AWS Kinesis Data Streams which are as follows:
Durability: It ensures minimal data loss along with synchronous duplication of streaming data across all the Availability Zones in the AWS Region.
Security: Sensitive data can be encrypted within KDS so that you can access your data privately through Amazon Virtual Private Cloud (VPC).
Easy to use and low cost: The components like connectors, agents, Kinesis Client Library (KLC) etc can help you build streaming applications quickly and effectively. There is no upfront cost for Kinesis Data Streams. You only have to pay for the resources you use.
Elasticity and Real-Time Performance: According to SNDK Corp, you can easily dynamically scale your applications from gigabytes to terabytes of data per hour adjusting the throughput. Real-Time Analytics Applications can be supplied with real-time streaming data within a very short time of the data being collected.
Real-Time Hotspot Detection in Amazon Kinesis Analytics
Amazon Kinesis Data Analytics is a brand-new machine learning feature recently introduced in order to detect “hotspots” in the streaming data. It is basically a real-time processing engine which lets you write and execute SQL queries in order to extract meaningful information from the data. It supplies the output or results to Kinesis Data Streams. There is this function called “HOTSPOTS” which enhances the existing machine learning capabilities. It further allows clients to drag unsupervised streaming based machine learning algorithms.
You don’t need to explicitly build and train complicated machine learning models. The HOTSPOTS function has some simple syntax and accepts data types such as DOUBLE, FLOAT, TINYINT, REAL, INTEGER, etc. The input accepted by this function is in the form of cursor and the output returned is in the form of JSON string. You can stream the hotspots programmatically out to either a Kinesis Data Stream or an AWS Lambda function.
Preprocessing Data in Amazon Kinesis Analytics using AWS Lambda
Kinesis Analytics is the best and easiest way to obtain real-time insights regarding your streaming data. You can build entire streaming applications using SQL queries. Few other applications of Kinesis Analytics are aggregation, filtering and anomaly detection, as mentioned by SNDK Corp. AWS Lambda is a flexible tool which defines the type of data being analyzed by your Kinesis Analytics Application.
Some of the common use cases of Kinesis Analytics are as follows:
Transformation: Kinesis Analytics creates a schema automatically in case of JSON or CSV records. A single column is meant to represent the entire record in case of unstructured text input records. Lambda transform can be of great use here which can transform streaming data simply in order to map to a schema.
Enrichment: Your streaming data can be enhanced further with additional information. You can make use of the data from Amazon S3 by utilizing the Reference Data Feature. Lambda function can be used for preprocessing your streaming data in order to add dynamic data.
Architecture: Kinesis Analytics reads data records in the form of batches from your Kinesis Stream. Lambda processor is responsible for the passing of batches to the Lambda function. You can achieve your preprocessing requirements by applying your business logic while iterating through the list using the function.
Real-Time Clickstream Anomaly Detection with Amazon Kinesis Analytics
There is an analytics pipeline which detects anomalies for a web traffic stream in real time, known as RANDOM_CUT_FOREST function. The function scores the data which flows through a dynamic data stream. It recognizes a normal pattern in streaming data, then compares it with new data points as a reference. This is how RANDOM_CUT_FOREST function is useful for detecting anomalies.
The programming required for anomaly detection is quite simplified by the RANDOM_CUT_FOREST function. You should know the type of data which you are using, such as linear, logarithmic, etc. This will assist you with selecting the right parameters required for this function.
AWS Kinesis embeds components such as Data Streams, Firehose, etc which perform the task of collecting streaming data and loading it into analytics tools or data stores.