AWS Big Data Blog

For all analytics and ML modeling use cases, data analysts and data scientists spend a bulk of their time running data preparation tasks manually to get a clean and formatted data to meet their needs. We ran a survey among data scientists and data analysts to understand the most frequently used transformations in their data […]

Amazon Redshift is the most popular cloud data warehouse today, with tens of thousands of customers collectively processing over 2 exabytes of data on Amazon Redshift daily. Amazon Redshift is fully managed, scalable, secure, and integrates seamlessly with your data lake. In this post, we discuss how to set up and use the new query […]

This is a guest post co-authored by Pradip Thoke of Dream11. In their own words, “Dream11, the flagship brand of Dream Sports, is India’s biggest fantasy sports platform, with more than 100 million users. We have infused the latest technologies of analytics, machine learning, social networks, and media technologies to enhance our users’ experience. Dream11 […]

Updated April 2021  to offer more Apache JMeter tips, and highlight some capabilities in the newer version of Apache JMeter. In the introductory post of this series, we discussed benchmarking benefits and best practices common across different open-source benchmarking tools. As a reminder of why benchmarking is important, Amazon Redshift allows you to scale storage […]

This post is co-written with Damian Grech from FanDuel FanDuel Group is an innovative sports-tech entertainment company that is changing the way consumers engage with their favorite sports, teams, and leagues. The premier gaming destination in the US, FanDuel Group consists of a portfolio of leading brands across gaming, sports betting, daily fantasy sports, advance-deposit […]

Proper data management is critical to successful, data-driven decision-making. An increasingly large number of customers are adopting data lakes to realize deeper insights from big data. As part of this, you need clean and trusted data in order to gain insights that lead to improvements in your business. As the saying goes, garbage in is […]

Amazon Redshift data sharing allows for a secure and easy way to share live data for read purposes across Amazon Redshift clusters. 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. It allows […]

Amazon Redshift federated query allows you to combine data from one or more Amazon Relational Database Service (Amazon RDS) for MySQL and Amazon Aurora MySQL databases with data already in Amazon Redshift. You can also combine such data with data in an Amazon Simple Storage Service (Amazon S3) data lake. This post shows you how […]

Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to create and deliver insights to everyone in your organization. In this post, we explore how authors of QuickSight dashboards can use some of the new chart types, layout options, and dashboard formatting controls to deliver dashboards that intuitively deliver insights […]

Today’s modern applications use multiple purpose-built database engines, including relational, key-value, document, and in-memory databases. This purpose-built approach improves the way applications use data by providing better performance and reducing cost. However, the approach raises some challenges for data teams that need to provide a holistic view on top of these database engines, and especially […]