Tag: Data Lake

When we first looked into the possibility of building a cloud-based data warehouse many years ago, we were struck by the fact that our customers were storing ever-increasing amounts of data, and yet only a small fraction of that data ever made it into a data warehouse or Hadoop system for analysis. We saw that […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. 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 […]

February 2024: This post was reviewed and updated to reflect changes in Amazon Athena engine version 3, including cost-based optimization and query result reuse. Amazon Athena is an interactive analytics service built on open source frameworks that make it straightforward to analyze data stored using open table and file formats in Amazon Simple Storage Service […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Ben Snively is a Solutions Architect with AWS Speed and agility are essential with today’s analytics tools. The quicker you can get from idea to first results, the more you can experiment […]

NOTE: The solution in this post is in the process of being updated. For the most current information, please visit the What is a data lake? page. This blog post has been translated into Japanese. Many of our customers choose to build their data lake on AWS. They find the flexible, pay-as-you-go, cloud model is […]

In today’s blog post, I will discuss how to optimize Amazon S3 for an architecture commonly used to enable genomic data analyses. This optimization is important to my work in genomics because, as genome sequencing continues to drop in price, the rate at which data becomes available is accelerating.

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.

Ben Snively is a Solutions Architect with AWS With big data, you deal with many different formats and large volumes of data. SQL-style queries have been around for nearly four decades. Many systems support SQL-style syntax on top of the data layers, and the Hadoop/Spark ecosystem is no exception. This allows companies to try new […]