What is AWS Kinesis (Amazon Kinesis Data Streams)?

Introduction

Data analytics is imperative for business success. AI-driven data insights make it possible to improve decision-making. These analytic models can work on processed data sets. The accuracy of decisions improves dramatically once you can use live data in real-time. The AWS training will prepare you to become a master of the cloud, storing, processing, and developing applications for the cloud data. Amazon AWS Kinesis makes it possible to process and analyze data from multiple sources in real-time. This blog will explore the AWS Amazon Kinesis and how this managed platform can revamp data analytics.

Table of Content

• What is Amazon Kinesis?
• How Amazon Kinesis Works?
• What can I do with Kinesis Data Streams?
• Types of Services Offered by Amazon Kinesis
• Features of Amazon Kinesis
• Use Cases of Amazon Kinesis
• Conclusion
• FAQs

What is Amazon Kinesis?

Amazon Web Service (AWS) offers the Amazon Kinesis service to process a vast amount of data, including, but not limited to, audio, video, website clickstreams, application logs, and IoT telemetry, every second in real-time. Compared to Big Data tools, Amazon Kinesis is automated and fully managed. It enables users to quickly process large streams of real-time data without the need for a massive infrastructure.

As of 2024, about 73% of enterprises have deployed a hybrid cloud. With data from numerous sources, it makes sense to use it in real time instead of waiting for the entire data set. Businesses can leverage data from any data source and build analytics on it while using Amazon Kinesis to process it at scale. Applications can effortlessly ingest, process, and analyze data streams.

How Amazon Kinesis Works?

Amazon Kinesis enables the applications to use the data in real-time to respond better to time-sensitive events. Current and up-to-date data helps enhance the efficiency of services, improve customer experiences, and drive innovation. The Kinesis pipeline has four stages:

1.    Data Ingestion

Data from different streams, such as applications, sensors, etc., are sent to Amazon Kinesis. During the data ingestion process, data from other sources in various formats, including real-time data, will be accepted. Developers can use the data streams to capture and store multiple terabytes per hour from hundreds of thousands of sources.

2.    Sharding and Scaling

The platform converts collected data into shards for fault tolerance and redundancy. Kinesis excels because it can scale horizontally virtually limitlessly to process large volumes of data.

3.    Processing and Buffering

The shards of data are then processed through filters for aggregation and sorting of records. This is done using the Kinesis Client Library (KCL) to prepare the data for further analysis. After sorting, data can be stored in AWS data stores or other AWS services for deep analytics. For example, processed data can be stored in Amazon S3 for archival and batch processing, loaded into Amazon Redshift for data warehousing and complex queries, or indexed in Amazon Elasticsearch Service for full-text search and analytics.

4.    Enabling Data Access

Once the data processing is complete, the real-time data is available in the data stream. This supplies data to the applications waiting to use it. Using Kinesis Data Firehose, data is directly delivered to AWS services, and data analytics can be applied using SQL queries.

What can I do with Kinesis Data Streams?

Modern applications and devices create a constant influx of data, which is too much for traditional batch-processing methods. The suite of services available with Amazon Kinesis supports many real-time data processing applications. Some of the categories of applications that can benefit from Kinesis Data Streams are:

• Real-time Data Ingestion and Processing: Applications that need data in real-time, such as healthcare applications, can use the data directly from the sources. It is helpful in improving patient care as updated data is used for diagnosis and treatment. Using the latest data, it is also possible to predict if there is an emergency. When OTT devices such as heart rate and blood pressure monitors supply real-time data, healthcare applications can offer proactive treatments. Any type of user experience personalization is possible when data is available immediately.
• Streamlined Data Delivery and Storage: Real-time data is not available right now. However, it can be processed and stored as historical data for research applications. If necessary, the real-time data can be fed directly to other AWS services for further processing.
• Automation with Real-time Insights: Real-time insights are helpful for fraud detection and anomaly identification in financial applications. They can also help prevent unauthorized attempts in real-time. The latest data can also be used to assess customers for financial institutions. They enhance data-driven decision-making.

Types of Services Offered by Amazon Kinesis

Four major types of services are available with the Amazon Kinesis platform. They are explained below:

Kinesis Data Streams

AWS Kinesis Data Streams makes gathering and processing vast data streams possible. Developers can create data processing applications using the Kinesis Client Library. Multiple Amazon EC2 instances can be used to process the data. Once ready, the processed data can be sent to AWS dashboards. The applications can generate alerts and alter pricing and advertising strategies based on real-time user input.

Kinesis Video Streams

Amazon Kinesis Video Streams expands the power of user applications by securely streaming videos from different devices connected to AWS. These video streams can be used in ML applications, analytics, or other processing. The platform is inherently elastic with scalable infrastructure, enabling the simultaneous ingestion of video streams from multiple sources. Video data is durably encrypted, stored, indexed, and accessed through APIs.

Kinesis Data Analytics

The built-in machine learning feature, Kinesis Data Analytics, detects hotspots in streaming data. This real-time processing engine allows you to create SQL queries to generate meaningful information and feed that output to the Kinesis data streams. With the Hotspots function, building and training complicated ML models is unnecessary. The results can be achieved with simple SQL queries. The output from the Hotspots can also be used directly with the AWS Lambda function.

Kinesis Data Firehose

If your applications need real-time data, the Kinesis Data Firehouse service delivers streaming data directly to Amazon S3, Amazon ES, and Amazon Redshift. It also supports third-party services like MongoDB, Datadob, and New Relic. There is no need to create applications for resource management. Developers can configure data generators to stream data directly to Kinesis Data Firehose, and you only need to specify the destination where the data will be delivered. Customising Data Firehose to rework the data before delivery is also possible.

Features of Amazon Kinesis

Using the Kinesis platform will be much easier if you have used the AWS tutorial to learn about various AWS services. The automated and managed Amazon Kinesis platform has the following features:

• Scalability: Kinesis seamlessly handles large volumes of streaming real-time data. More shards to the stream can be added horizontally, expanding the platform to process terabytes of data per hour. The number of sources from which data is ingested is not limited, making it suitable for applications with high data throughput requirements.
• Real-time Processing: Kinesis allows businesses to gain immediate insights and respond to events as and when they occur. Timely decision-making for fraud detection and real-time analytics for customer personalization is hassle-free with Kinesis.
• Integration: Kinesis offers seamless integration with other AWS services, enabling developers to create end-to-end data processing pipelines. For example, Kinesis Data Streams can ingest data and pass it along to AWS Lambda, which can be analyzed using Amazon Redshift. Amazon Quicksight can be used to generate visualizations for real-time data. This reduces ETL delivery and nullifies the need for complex data processing workflows.
• Durability: Real-time data is not only streamed and processed but also stored for further processing. Using Kinesis Data Streams, data can be stored across multiple availability zones. This feature prevents data loss for mission-critical applications.
• Security: Built-in robust security features protect the data throughout the process. AWS Key Management Service (KMS) encrypts the data, and AWS Identity and Access Management (IAM) policies can be applied to restrict and control user access. This ensures data availability only to authorized users.
• Cost–Effective: Amazon Kinesis is a completely managed platform with scalable infrastructure that is offered as a pay-as-you-go model. You can use the services you need and pay only for them based on your current needs. If your business must be scaled up, it can be done readily. This is especially useful for applications with varying data throughput requirements. There is no operational overhead associated with infrastructure management.

Use Cases of Amazon Kinesis

There are several use cases for Amazon Kinesis across industries. Some of them are:

• Real-time Analytics: Amazon Kinesis’s real-time analytics can drastically improve enterprise decision-making. Data can be analyzed as it is generated, reducing the time lag. So, timely data-driven decisions can be made quickly. For example, a marketing manager can create viral strategies based on real-time user behavior and engagement. Stock brokers can make profitable trade decisions using real-time market data.
• Machine Learning Applications: ML models can be trained on real-time data to improve their output accuracy and performance, allowing for real-time predictors. For example, feeding real-time streaming data to a healthcare ML model can predict current health risks, allowing medical professionals to make timely interventions before the occurrence of the medical issue.
• Streamline ETL Pipeline: Kinesis can dramatically transform and automate the ETL pipeline, extracting data from various sources, transforming data according to business needs, and loading data into necessary data stores for subsequent analysis. For example, financial institutions can use Kinesis Data Streams to aggregate transaction data from multiple branches and load it using Kinesis Firehose to a data warehouse for compliance reporting.
• IoT Applications: IoT devices send data in real time, and using Kinesis, IoT applications can quickly process large volumes of real-time data. Manufacturing companies can collect sensor data from factory equipment for real-time monitoring and preventive maintenance, avoiding costly repairs later.

Conclusion

Amazon AWS Kinesis is a powerful platform for real-time data streaming and processing. If you are interested in becoming an AWS developer, prepare with AWS interview questions to impress the recruiter. Learning and upgrading your skills to use the latest Kinesis suite will help you build real-time analytics applications that are useful across various industries. Organizations can immediately improve data-driven decisions when real-time data is readily available.

FAQs

1.    What is AWS kinesis used for?

AWS Kinesis is used for real-time data streaming, processing, and analysis. It enables businesses to collect, process, and analyze data as it is generated, providing immediate insights and enabling timely decision-making.

2.    What is Amazon Kinesis Data Firehose?

Amazon Kinesis Data Firehose is a fully managed service that delivers real-time streaming data to AWS data stores. It simplifies the process of loading data into destinations like Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk and can transform data using AWS Lambda functions before delivery.

3.    What is Amazon Kinesis Analytics?

Amazon Kinesis Analytics is a service that allows users to process and analyze streaming data using standard SQL. It enables users to create SQL queries to perform operations like filtering, aggregating, and joining data streams in real time. The service can send the results to other AWS services for further analysis or storage.

4.    What is the difference between Amazon Kinesis and Kafka?

Amazon Kinesis and Apache Kafka are both platforms for real-time data streaming, but they have some key differences. Unlike Kinesis, which is a fully managed AWS service, Kafka is an open-source platform. Kafka requires manual integration with AWS services, and Kinesis integrates seamlessly. Both Kinesis and Kafka are scalable. Due to its open-source nature, Kafka is highly customizable but requires investment in infrastructure. The Kinesis pay-as-you-go model is more cost-effective.