AWS Big Data Blog

Build a Visualization and Monitoring Dashboard for IoT Data with Amazon Kinesis Analytics and Amazon QuickSight

Customers across the world are increasingly building innovative Internet of Things (IoT) workloads on AWS. With AWS, they can handle the constant stream of data coming from millions of new, internet-connected devices. This data can be a valuable source of information if it can be processed, analyzed, and visualized quickly in a scalable, cost-efficient manner. […]

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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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Test Your Streaming Data Solution with the New Amazon Kinesis Data Generator

When building a streaming data solution, most customers want to test it with data that is similar to their production data. Creating this data and streaming it to your solution can often be the most tedious task in testing the solution. Amazon Kinesis Streams and Amazon Kinesis Firehose enable you to continuously capture and store […]

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AWS Big Data Blog Month in Review: April 2017

by Derek Young | on | Permalink | Comments |  Share

Another month of big data solutions on the Big Data Blog. Please take a look at our summaries below and learn, comment, and share. Thank you for reading! NEW POSTS Amazon QuickSight Spring Announcement: KPI Charts, Export to CSV, AD Connector, and More!  In this blog post, we share a number of new features and […]

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Tips for Migrating to Apache HBase on Amazon S3 from HDFS

Starting with Amazon EMR 5.2.0, you have the option to run Apache HBase on Amazon S3. Running HBase on S3 gives you several added benefits, including lower costs, data durability, and easier scalability. HBase provides several options that you can use to migrate and back up HBase tables. The steps to migrate to HBase on […]

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Visualize Big Data with Amazon QuickSight, Presto, and Apache Spark on Amazon EMR

Last December, we introduced the Amazon Athena connector in Amazon QuickSight, in the Derive Insights from IoT in Minutes using AWS IoT, Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight post. The connector allows you to visualize your big data easily in Amazon S3 using Athena’s interactive query engine in a serverless fashion. This turned […]

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Near Zero Downtime Migration from MySQL to DynamoDB

Many companies consider migrating from relational databases like MySQL to Amazon DynamoDB, a fully managed, fast, highly scalable, and flexible NoSQL database service. For example, DynamoDB can increase or decrease capacity based on traffic, in accordance with business needs. The total cost of servicing can be optimized more easily than for the typical media-based RDBMS. […]

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Amazon QuickSight Now Supports Audit Logging with AWS CloudTrail

We launched Amazon QuickSight to democratize BI. Our goal is to make it easier and cheaper to roll out advanced business analytics capabilities to everyone in an organization. Overall, this enables better understanding of business, and allows faster data-driven decisions in an organization. In the past, the ability to share data presented an administrative challenge […]

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Manage Query Workloads with Query Monitoring Rules in Amazon Redshift

This blog post has been translated into Japanese and Chinese. Data warehousing workloads are known for high variability due to seasonality, potentially expensive exploratory queries, and the varying skill levels of SQL developers. To obtain high performance in the face of highly variable workloads, Amazon Redshift workload management (WLM) enables you to flexibly manage priorities and resource […]

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Build a Real-time Stream Processing Pipeline with Apache Flink on AWS

This post has been translated into Japanese. In today’s business environments, data is generated in a continuous fashion by a steadily increasing number of diverse data sources. Therefore, the ability to continuously capture, store, and process this data to quickly turn high-volume streams of raw data into actionable insights has become a substantial competitive advantage […]

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