AWS Big Data Blog

Category: Analytics

Running queries securely from the same VPC where an Amazon Redshift cluster is running

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 […]

As illustrated in the following architecture diagram, the DQAF exclusively uses serverless AWS technology.

Building a serverless data quality and analysis framework with Deequ and AWS Glue

March 2023: You can now use AWS Glue Data Quality to measure and manage the quality of your data. AWS Glue Data Quality is built on DeeQu and it offers a simplified user experience for customers who want to this open-source package. Refer to the blog and documentation for additional details. With ever-increasing amounts of data […]

This blog covers use case based walkthroughs of how we can achieve the top 7 among those transformations in AWS Glue DataBrew.

7 most common data preparation transformations in AWS Glue DataBrew

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 […]

Scheduling SQL queries on your Amazon Redshift data warehouse

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 […]

The following diagram shows the workflow to connect Apache Airflow to Amazon EMR.

Dream11’s journey to building their Data Highway on AWS

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 […]

The following diagram provides a basic illustration of the various Apache JMeter building blocks to be leveraged in this load test.

Building high-quality benchmark tests for Amazon Redshift using Apache JMeter

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 […]

How FanDuel Group secures personally identifiable information in a data lake using AWS Lake Formation

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 […]

We’ll walk through a solution that takes sets up a recurring Profile job to determine data quality metrics, and using your defined business rules.

Setting up automated data quality workflows and alerts using AWS Glue DataBrew and AWS Lambda

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 […]

The following diagram depicts the cloud DW benchmark data model used.

Sharing Amazon Redshift data securely across Amazon Redshift clusters for workload isolation

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 […]

New charts, formatting, and layout options in Amazon QuickSight

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 […]