Category: Amazon Kinesis

This post presents a reference architecture and optimization strategies for building serverless data analytics solutions on AWS using Amazon Kinesis Data Analytics. In addition, this post shows the design approach that the engineering team at NortonLifeLock took to build out an operational analytics platform that processes usage data for their VPN services, consuming petabytes of […]

Flink Forward Global 2021 is a 2-day virtual conference for the Apache Flink and stream processing communities. Apache Flink is an open-source distributed engine for processing data streams that can support both streaming and batch workloads. Amazon Kinesis Data Analytics is a fully managed service for Apache Flink on AWS that reduces the complexity of […]

Amazon Kinesis Data Firehose provides a convenient way to reliably load streaming data into data lakes, data stores, and analytics services. It can capture, transform, and deliver streaming data to Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon OpenSearch Service (successor to Amazon Elasticsearch Service), generic HTTP endpoints, and service providers like Datadog, New […]

MEDHOST has been providing products and services to healthcare facilities of all types and sizes for over 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their patient care and operational excellence with its integrated clinical and financial EHR solutions. MEDHOST also offers a comprehensive Emergency Department Information System with […]

When designing multi-tenant streaming ingestion pipelines, there are myriad ways to design and build your streaming solution, each with its own set of trade-offs. The first decision you have to make is the strategy that determines how you choose to physically or logically separate one tenant’s data from another. Sharing compute and storage resources helps […]

This post is co-written with Noah Mundahl, Director of Public Cloud Engineering at United Health Group. Update (12/1/2021): Amazon Kinesis Data Streams On-Demand mode is now the recommended way to natively auto scale your Amazon Kinesis Data Streams. In this post, we cover a solution to add auto scaling to Amazon Kinesis Data Streams. Whether […]

Before the release of Amazon Kinesis Data Analytics Studio, customers relied on Amazon Kinesis Data Analytics for SQL on Amazon Kinesis Data Streams. With the release of Kinesis Data Analytics Studio, data engineers and analysts can use an Apache Zeppelin notebook within Studio to query streaming data interactively from a variety of sources, like Kinesis […]

In Part 1 of this series, you learned how to calibrate Amazon Kinesis Data Streams stream and Apache Flink application deployed in Amazon Kinesis Data Analytics for tuning Kinesis Processing Units (KPUs) to achieve higher performance. Although the collection, processing, and analysis of spiky data stream in real time is crucial, reacting to the spiky […]

In real-time stream processing, it becomes critical to collect, process, and analyze high-velocity real-time data to provide timely insights and react quickly to new information. Streaming data velocity could be unpredictable, and volume could spike based on user demand at a given time of day. Real-time analysis needs to handle the data spike, because any […]

For organizations moving towards a serverless microservice approach, Amazon DynamoDB has become a preferred backend database due to its fully managed, multi-Region, multi-active durability with built-in security controls, backup and restore, and in-memory caching for internet-scale application. , which you can then use to derive near-real-time business insights. The data lake provides capabilities to business teams to plug in […]