AWS Big Data Blog

Tag: AWS Lambda

Preprocessing Data in Amazon Kinesis Analytics with AWS Lambda

Kinesis Analytics now gives you the option to preprocess your data with AWS Lambda. This gives you a great deal of flexibility in defining what data gets analyzed by your Kinesis Analytics application. In this post, I discuss some common use cases for preprocessing, and walk you through an example to help highlight its applicability.

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Build a Serverless Architecture to Analyze Amazon CloudFront Access Logs Using AWS Lambda, Amazon Athena, and Amazon Kinesis Analytics

Nowadays, it’s common for a web server to be fronted by a global content delivery service, like Amazon CloudFront. This type of front end accelerates delivery of websites, APIs, media content, and other web assets to provide a better experience to users across the globe. The insights gained by analysis of Amazon CloudFront access logs […]

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Build a Healthcare Data Warehouse Using Amazon EMR, Amazon Redshift, AWS Lambda, and OMOP

In the healthcare field, data comes in all shapes and sizes. Despite efforts to standardize terminology, some concepts (e.g., blood glucose) are still often depicted in different ways. This post demonstrates how to convert an openly available dataset called MIMIC-III, which consists of de-identified medical data for about 40,000 patients, into an open source data […]

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How Eliza Corporation Moved Healthcare Data to the Cloud

This is a guest post by Laxmikanth Malladi, Chief Architect at NorthBay. NorthBay is an AWS Advanced Consulting Partner and an AWS Big Data Competency Partner “Pay-for-performance” in healthcare pays providers more to keep the people under their care healthier. This is a departure from fee-for-service where payments are for each service used. Pay-for-performance arrangements provide […]

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Building Event-Driven Batch Analytics on AWS

Karthik Sonti is a Senior Big Data Architect with AWS Professional Services Modern businesses typically collect data from internal and external sources at various frequencies throughout the day. These data sources could be franchise stores, subsidiaries, or new systems integrated as a result of merger and acquisitions. For example, a retail chain might collect point-of-sale […]

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Processing VPC Flow Logs with Amazon EMR

Michael Wallman is a senior consultant with AWS ProServ It’s easy to understand network patterns in small AWS deployments where software stacks are well defined and managed. But as teams and usage grow, its gets harder to understand which systems communicate with each other, and on what ports. This often results in overly permissive security […]

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Data Lake Ingestion: Automatically Partition Hive External Tables with AWS

Songzhi Liu is a Professional Services Consultant with AWS The data lake concept has become more and more popular among enterprise customers because it collects data from different sources and stores it where it can be easily combined, governed, and accessed. On the AWS cloud, Amazon S3 is a good candidate for a data lake […]

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Simplify Management of Amazon Redshift Snapshots using AWS Lambda

Ian Meyers is a Solutions Architecture Senior Manager with AWS Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all your data using your existing business intelligence tools. A cluster is automatically backed up to Amazon S3 by default, and three automatic snapshots of the cluster […]

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Real-time in-memory OLTP and Analytics with Apache Ignite on AWS

Babu Elumalai is a Solutions Architect with AWS Organizations are generating tremendous amounts of data, and they increasingly need tools and systems that help them use this data to make decisions. The data has both immediate value (for example, trying to understand how a new promotion is performing in real time) and historic value (trying […]

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From SQL to Microservices: Integrating AWS Lambda with Relational Databases

Bob Strahan is a Senior Consultant with AWS Professional Services AWS Lambda has emerged as excellent compute platform for modern microservices architecture, driving dramatic advancements in flexibility, resilience, scale and cost effectiveness. Many customers can take advantage of this transformational technology from within their existing relational database applications. In this post, we explore how to […]

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