AWS Big Data Blog

Category: 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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Implement Continuous Integration and Delivery of Apache Spark Applications using AWS

In this post, we walk you through a solution that implements a continuous integration and deployment pipeline supported by AWS services. You can use the sample template and Spark application shared in this post and adapt them for the specific needs of your own application.

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Building a Real World Evidence Platform on AWS

Deriving insights from large datasets is central to nearly every industry, and life sciences is no exception. To combat the rising cost of bringing drugs to market, pharmaceutical companies are looking for ways to optimize their drug development processes. They are turning to big data analytics to better quantify the effect that their drug compounds […]

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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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Data Lake Ingestion: Automatically Partition Hive External Tables with AWS

In this post, I introduce a simple data ingestion and preparation framework based on AWS Lambda, Amazon DynamoDB, and Apache Hive on EMR for data from different sources landing in S3. This solution lets Hive pick up new partitions as data is loaded into S3 because Hive by itself cannot detect new partitions as data lands.

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