AWS Big Data Blog

Most organizations generate data in real time and ever-increasing volumes. Data is captured from a variety of sources, such as transactional and reporting databases, application logs, customer-facing websites, and external feeds. Companies want to capture, transform, and analyze this time-sensitive data to improve customer experiences, increase efficiency, and drive innovations. With increased data volume and […]

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. In the fourth post of the series, we discussed optimizing memory management. In this post, we focus on writing ETL scripts for AWS Glue jobs locally. AWS Glue is built on […]

Apache Hive is an open-source data warehouse package that runs on top of an Apache Hadoop cluster. You can use Hive for batch processing and large-scale data analysis. Hive uses Hive Query Language (HiveQL), which is similar to SQL. ACID (atomicity, consistency, isolation, and durability) properties make sure that the transactions in a database are […]

This is a guest post by Steven Collings, Senior Data Consultant at Zoopla Zoopla is a property website that enables users to find residential or commercial property to buy or rent in the UK and overseas. Since acquiring Property Software Group and Expert Agent, we also offer a backend software that agents can use to […]

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Customers tell us that they want extremely fast query response times so they can make equally fast decisions. This post presents the recently launched, […]

Amazon Elasticsearch Service (Amazon ES) is a popular choice for log analytics, search, real-time application monitoring, clickstream analysis, and more. One commonality among these use cases is the need to write and run queries to obtain search results at lightning speed. However, doing so requires expertise in the JSON-based Elasticsearch query domain-specific language (Query DSL). […]

This is a guest post co-written by Siddharth Thacker and Swatishree Sahu from Aruba Networks. Aruba Networks is a Silicon Valley company based in Santa Clara that was founded in 2002 by Keerti Melkote and Pankaj Manglik. Aruba is the industry leader in wired, wireless, and network security solutions. Hewlett-Packard acquired Aruba in 2015, making […]

Enterprises often want to centralize governance and compliance requirements, and provide a common set of policies on how Amazon EMR instances should be set up. You can use AWS Service Catalog to centrally manage commonly deployed Amazon EMR cluster configurations, and this helps you achieve consistent governance and meet your compliance requirements, while at the […]

Customers use Amazon Redshift for everything from accelerating existing database environments, to ingesting weblogs for big data analytics. Amazon Redshift is a fully managed, petabyte-scale, massively parallel data warehouse that offers simple operations and high performance. Amazon Redshift provides an open standard JDBC/ODBC driver interface, which allows you to connect your existing business intelligence (BI) tools and reuse existing analytics queries. Amazon Redshift can run any type of data model, from a production transaction system third-normal-form model to star and snowflake schemas, data vault, or simple flat tables. This post takes you through the most common performance-related opportunities when adopting Amazon Redshift and gives you concrete guidance on how to optimize each one. 

This post provides guidance on how to configure Amazon Athena federation with AWS Lambda and Amazon Redshift, while addressing performance considerations to ensure proper use.