Handling real-time data in AWS

By: | Ankur Jain

Publish Date: December 19, 2019

AWS provides infrastructure for delivering powerful insights on any streaming data with high efficiency in real-time. The real-time analytics is designed for ensuring efficient handing of business-critical processes or workloads.

Real-time data is usually generated continuously by scores of data sources. Typical examples of real-time data include examples of streaming activities like web apps, eCommerce transactions, in-game transactions, interactions in social networks, or say telemetry from IoT devices, etc. All this data needs to process in real-time on an incremental basis. These data points need to be analyzed over a sliding time window for a diverse set of descriptive analytics like correlations, filtering, etc.

These will help companies derive visibility into their business and customer-centric metrics like active usage, revenue earned, engagement through website clicks, which in turn will help them respond to critical situations.

For example: Based on the real-time activity during shopping sales, eCommerce companies can tweak their offers to respond to the changing shopping trends quickly.
Some examples of real-time data requirements include

  • IoT devices in industrial equipment, farm equipment, transportation systems, etc.
  • Financial institution handling billions of credit card transaction or millions of trades in a single day
  • Online companies processing millions of click stream telemetry for real-time content optimizations

Challenges of working with real-time data
Real-time streaming data handling requires two types of layers – a storage layer and a processing layer. The storage layer should bring fast and consistent read/writes of large data streams. DynamoDB Streams is a powerful service that can be combined with other AWS services to address these challenges. When enabled, DynamoDB Streams captures a time-ordered sequence of item-level modifications in a DynamoDB. Applications can access a series of stream records, which contain an item change; from a DynamoDB stream in near real time. Whereas the processing layer should be able to run computational models at scale. The key requirements for any system should

  • Hyper scalability
  • Handling distributed data architecture
  • Data precision and accuracy
  • Low Latency
  • Fault-tolerant computing

AWS provides all of these capabilities through its flagship Amazon Kinesis services.
Amazon Kinesis Services: Complete solution for real-time data analytics
Amazon Kinesis provides the complete e2e solution right from collection, to fast processing and real-time analysis of the streaming data for actionable business and product insights. Amazon Kinesis provides the following four set-ups

Amazon Kinesis Data Streams

Amazon Kinesis Data Streams (KDS) is a Hyper scalable real-time data streaming service. KDS can continuously capture GBs of data per second parallelly from several sources such as clickstreams, event streams, financial transactions, social media feeds, and geospatial events. This enables real-time dashboards, anomaly detection, dynamic pricing, etc. for some actionable insights in real-time.

Amazon Kinesis Data Firehose

Amazon Kinesis Data Firehose is a fully managed service that automatically scales to manage your data throughput velocity. It reliably loads streaming data into data lakes, data stores, and analytics tools. It can capture, transfigure, and load streaming data into Amazon Redshift, Amazon Elasticsearch Service, Amazon S3, and Splunk. This will enable incorporating real-time analytics with the existing BI tools and dashboards. It can also put the data in batches, drives efficient compression and transformation, and bring impermeable encryption on the data before loading it.

With Kinesis Data Firehose, you only pay for data you transmit through the service, and if applicable, for data format conversion.

Amazon Kinesis Data Analytics

Amazon Kinesis Data Analytics is probably the most convenient way to analyze streaming data, gain actionable insights. With very little complexity of building, managing, and integrating streaming applications with other AWS services, you can comfortably run your real-time applications continuously that scales automatically to your data throughput. SQL users can conveniently query streaming data or develop complete streaming applications by using templates & an interactive SQL editor. Java developers can rapidly create sophisticated streaming applications by using open source Java libraries & AWS integrations to transfigure & analyze data in real-time.

Amazon Kinesis Video Streams

Amazon Kinesis Video Streams makes it very convenient for usage and safely streams video from connected devices to Amazon Web Services. This can be further used for analytics, machine learning, and playback capabilities. Apart from hyper scalability, it provides durable storage, encryption, and indexing of the video data in your streams. The data is available through APIs. With playback video for live & on-demand viewing, one can rapidly develop applications that use computer vision with the integration of Amazon Recognition Video and libraries for ML frameworks.  It also supports WebRTC, an open-source project that enables real-time media streaming and interaction between web browsers, mobile applications, and connected devices via simple APIs.

Real-time data storage and analytics demand is set to grow multifold, and we can see growing traction from industry to move from batch data storage to real-time data storage analytics. It is important to engage expertise and oversight of capable partner to utilize these technologies and stay relevant in the rapidly evolving world.

AWS Cloud is well positioned to address the challenges of real-time streaming analytics with cost effective tooling as well as the ability to manage the volume, velocity, and variety of data. Serverless can be a key consideration while architecting solutions as data processing needs continues to scale up. Kinesis makes everything much more manageable and cost effective in comparison to handling compute instances.

Reference links

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