AWS Big Data Blog

Category: Amazon Kinesis

Integrate Etleap with Amazon Redshift Streaming Ingestion (preview) to make data available in seconds

Amazon Redshift is a fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using SQL and your extract, transform, and load (ETL), business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per day and power analytics workloads. Etleap […]

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Now Available: Updated guidance on the Data Analytics Lens for AWS Well-Architected Framework

Nearly all businesses today require some form of data analytics processing, from auditing user access to generating sales reports. For all your analytics needs, the Data Analytics Lens for AWS Well-Architected Framework provides prescriptive guidance to help you assess your workloads and identify best practices aligned to the AWS Well-Architected Pillars: Operational Excellence, Security, Reliability, […]

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Continuous monitoring with Sumo Logic using Amazon Kinesis Data Firehose HTTP endpoints

Amazon Kinesis Data Firehose streams data to AWS destinations such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, and Amazon OpenSearch Service (successor to Amazon Elasticsearch Service). Additionally, Kinesis Data Firehose supports destinations to third-party partners. This ability to send data to third-party partners is a vital feature for customers who already use these […]

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Query your Amazon MSK topics interactively using Amazon Kinesis Data Analytics Studio

Amazon Kinesis Data Analytics Studio makes it easy to analyze streaming data in real time and build stream processing applications powered by Apache Flink using standard SQL, Python, and Scala. With a few clicks on the AWS Management Console, you can launch a serverless notebook to query data streams and get results in seconds. Kinesis […]

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How NortonLifelock built a serverless architecture for real-time analysis of their VPN usage metrics

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 […]

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Register now for Flink Forward Global, October 26-27, 2021

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 […]

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Kinesis Data Firehose now supports dynamic partitioning to Amazon S3

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 […]

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How MEDHOST’s cardiac risk prediction successfully leveraged AWS analytic services

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 […]

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Secure multi-tenant data ingestion pipelines with Amazon Kinesis Data Streams and Kinesis Data Analytics for Apache Flink

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 […]

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Auto scaling Amazon Kinesis Data Streams using Amazon CloudWatch and AWS Lambda

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 […]

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