Category: Analytics

ustomers running analytics, stream processing, machine learning, and ETL workloads on personally identifiable information, health information, and financial data have strict requirements for encryption of data at-rest and in-transit. The Apache Spark and Hadoop ecosystems lend themselves to these big data use cases, and customers have asked us to provide a quick and easy way to encrypt data at-rest and data in-transit between nodes in each execution framework.

In this post, I show an analytics pipeline which detects anomalies in real time for a web traffic stream, using the RANDOM_CUT_FOREST function available in Amazon Kinesis Analytics.

This post introduces you to the different types of windows supported by Amazon Kinesis Analytics, the importance of time as it relates to stream data processing, and best practices for sending your SQL results to a configured destination.

AWS IoT makes it easy to integrate and control your devices from other AWS services for even more powerful IoT applications. In particular, IoT provides tight integration with AWS Lambda, Amazon Kinesis, Amazon S3, Amazon Machine Learning, Amazon DynamoDB, Amazon CloudWatch, and Amazon OpenSearch Service.

In this post, I show you how to gain valuable insight into your network by using Amazon EMR and Amazon VPC Flow Logs. The walkthrough implements a pattern often found in network equipment called ‘Top Talkers’, an ordered list of the heaviest network users, but the model can also be used for many other types of network analysis.

In this post, I suggest ways you can monitor the Amazon Kinesis Client Library (KCL) application you use to process DynamoDB Streams to quickly track and resolve issues or failures so you can avoid losing data. Dashboards, metrics, and application logs all play a part. This post may be most relevant to Java applications running on Amazon EC2 instances.

This post introduces you to Amazon Kinesis Analytics, the fundamentals of writing ANSI-Standard SQL over streaming data, and works through a simple example application that continuously generates metrics over time windows.

This post shows you how to build a custom application for EMR for Apache Bigtop-based releases 4.x and greater. EMR nodes are based on the Amazon Linux AMI, so I will deploy on RPM packages and use Elasticsearch as the example application.

Jonathan Fritz is a Senior Product Manager for Amazon EMR We are excited to launch Amazon EMR release 5.0 today, giving customers the latest versions of 16 supported open-source applications in the big data ecosystem, including new major versions of Spark and Hive. Almost exactly a year ago, we shipped release 4.0, which brought significant […]

In this blog post, you will learn how to install JobServer on EMR using a bootstrap action (BA) derived from the JobServer GitHub repository. Then we’ll run JobServer using a sample dataset.