Category: Analytics

As the industry grows with more data volume, big data analytics is becoming a common requirement in data analytics and machine learning (ML) use cases. Also, as we start building complex data engineering or data analytics pipelines, we look for a simpler orchestration mechanism with graphical user interface-based ETL (extract, transform, load) tools. Recently, AWS […]

This is a guest post by Helen Anderson, data analyst and AWS Data Hero Every year, the Amazon Redshift team launches new and exciting features, and 2020 was no exception. New features to improve the data warehouse service and add interoperability with other AWS services were rolling out all year. I am part of a […]

Amazon DynamoDB helps you capture high-velocity data such as clickstream data to form customized user profiles and Internet of Things (IoT) data so that you can develop insights on sensor activity across various industries, including smart spaces, connected factories, smart packing, fitness monitoring, and more. It’s important to store these data points in a centralized […]

Amazon Athena now supports federated query, a feature that allows you to query data in sources other than Amazon Simple Storage Service (Amazon S3). You can use federated queries in Athena to query the data in place or build pipelines that extract data from multiple data sources and store them in Amazon S3. With Athena […]

September 2022: This post was reviewed for accuracy. 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 […]

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 […]