AWS Big Data Blog

Large enterprises often provide software offerings to multiple customers by providing each customer a dedicated and isolated environment (a software offering composed of multiple single-tenant environments). Because the data is in various independent systems, large enterprises are looking for ways to simplify data processing pipelines. To address this, you can create data lakes to bring […]

You generally write unit tests for your code, but do you also test your data? Incoming data quality can make or break your application. Incorrect, missing, or malformed data can have a large impact on production systems. Examples of data quality issues include the following: Missing values can lead to failures in production system that […]

Customers who don’t need to set up a VPN or a private connection to AWS often use public endpoints to access AWS. Although this is acceptable for testing out the services, most production workloads need a secure connection to their VPC on AWS. If you’re running your production data warehouse on Amazon Redshift, you can […]

With ever-increasing amounts of data at their disposal, large organizations struggle to cope with not only the volume but also the quality of the data they manage. Indeed, alongside volume and velocity, veracity is an equally critical issue in data analysis, often seen as a precondition to analyzing data and guaranteeing its value. High-quality data […]

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 […]