AWS Big Data Blog
Category: Analytics
Amazon Redshift at re:Invent 2019
The annual AWS re:Invent learning conference is an exciting time full of new product and program launches. At the first re:Invent conference in 2012, AWS announced Amazon Redshift. Since then, tens of thousands of customers have started using Amazon Redshift as their cloud data warehouse. In 2019, AWS shared several significant launches and dozens of […]
How Verizon Media Group migrated from on-premises Apache Hadoop and Spark to Amazon EMR
This is a guest post by Verizon Media Group. At Verizon Media Group (VMG), one of the major problems we faced was the inability to scale out computing capacity in a required amount of time—hardware acquisitions often took months to complete. Scaling and upgrading hardware to accommodate workload changes was not economically viable, and upgrading […]
Maximize data ingestion and reporting performance on Amazon Redshift
This is a guest post from ZS. In their own words, “ZS is a professional services firm that works closely with companies to help develop and deliver products and solutions that drive customer value and company results. ZS engagements involve a blend of technology, consulting, analytics, and operations, and are targeted toward improving the commercial […]
Amazon QuickSight: 2019 in review
2019 has been an exciting year for Amazon QuickSight. We onboarded thousands of customers, expanded our global presence to 10 AWS Regions, and launched over 60 features—more than a feature a week! We are inspired by you—our customers, and all that you do with Amazon QuickSight. We are thankful for the time you spend with […]
Working with nested data types using Amazon Redshift Spectrum
Redshift Spectrum is a feature of Amazon Redshift that allows you to query data stored on Amazon S3 directly and supports nested data types. This post discusses which use cases can benefit from nested data types, how to use Amazon Redshift Spectrum with nested data types to achieve excellent performance and storage efficiency, and some […]
Collect and distribute high-resolution crypto market data with ECS, S3, Athena, Lambda, and AWS Data Exchange
This is a guest post by Floating Point Group. In their own words, “Floating Point Group is on a mission to bring institutional-grade trading services to the world of cryptocurrency.” The need and demand for financial infrastructure designed specifically for trading digital assets may not be obvious. There’s a rather pervasive narrative that these coins […]
Under the hood: Scaling your Kinesis data streams
Real-time delivery of data and insights enables businesses to pivot quickly in response to changes in demand, user engagement, and infrastructure events, among many others. Amazon Kinesis offers a managed service that lets you focus on building your applications, rather than managing infrastructure. Scalability is provided out-of-the-box, allowing you to ingest and process gigabytes of […]
ETL and ELT design patterns for modern data architecture using Amazon Redshift: Part 2
New: Read Amazon Redshift continues its price-performance leadership to learn what analytic workload trends we’re seeing from Amazon Redshift customers, new capabilities we have launched to improve Redshift’s price-performance, and the results from the latest benchmarks. Part 1 of this multi-post series, ETL and ELT design patterns for modern data architecture using Amazon Redshift: Part 1, […]
ETL and ELT design patterns for lake house architecture using Amazon Redshift: Part 1
New: Read Amazon Redshift continues its price-performance leadership to learn what analytic workload trends we’re seeing from Amazon Redshift customers, new capabilities we have launched to improve Redshift’s price-performance, and the results from the latest benchmarks. Part 1 of this multi-post series discusses design best practices for building scalable ETL (extract, transform, load) and ELT (extract, […]
Matching patient records with the AWS Lake Formation FindMatches transform
Patient matching is a major obstacle in achieving healthcare interoperability. Mismatched patient records and inability to retrieve patient history can cause significant barriers to informed clinical decision-making and result in missed diagnoses or delayed treatments. Additionally, healthcare providers often invest in patient data deduplication, especially when the number of patient records is growing rapidly in […]