AWS Big Data Blog

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 […]

AWS offers the broadest set of analytic tools and engines that analyzes data using open formats and open standards. To validate expertise with AWS data analytics solutions, builders can now take the beta for the AWS Certified Data Analytics — Specialty certification. The AWS Certified Data Analytics — Specialty certification validates technical expertise with designing, […]

Amazon Athena is an interactive query service that makes it easy to analyze the data stored in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. You can reduce your per-query costs and get better performance by compressing, partitioning, […]

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. This post details the two new preview features that you can start using today: connecting […]

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Amazon Athena has announced a public preview of a new feature that provides an easy […]

Organizations today use data stores that are the best fit for the applications they build. For example, for an organization building a social network, a graph database such as Amazon Neptune is likely the best fit when compared to a relational database. Similarly, for workloads that require flexible schema for fast iterations, Amazon DocumentDB (with […]

Amazon Athena recently added support for federated queries and user-defined functions (UDFs), both in Preview. See Query any data source with Amazon Athena’s new federated query for more details. Jornaya helps marketers intelligently connect consumers who are in the market for major life purchases such as homes, mortgages, cars, insurance, and education. Jornaya collects data […]

Amazon QuickSight now makes it easier for you to spot the highlights or low-lights in data through conditional formatting. With conditional formatting, you can specify customized text or background colors based on field values in the dataset, using solid or gradient colors. You can also display data values with the supported icons. Using color coding and […]

The Amazon QuickSight team is excited to announce the availability of Themes and more APIs! Themes for dashboards let you align the look and feel of Amazon QuickSight dashboards with your application’s branding or corporate themes. The new APIs added allow you to manage your Amazon QuickSight deployments programmatically, with support for dashboards, datasets, data sources, SPICE ingestion, and fine-grained access control over AWS resources. Together, they allow you to creatively tailor Amazon QuickSight to your audiences, whether you are using Amazon QuickSight to provide your users with an embedded analytics experience or for your corporate Business Intelligence (BI) needs. This post provides an overview of these new capabilities and details on getting started.

This post outlines the approach taken by Intuit, though it is important to remember that there are many ways to build a data lake (for example, AWS Lake Formation). We’ll cover the technologies and processes involved in creating the Intuit Data Lake at a high level, including the overall structure and the automation used in provisioning accounts and resources. Watch this space in the future for more detailed blog posts on specific aspects of the system, from the other teams and engineers who worked together to build the Intuit Data Lake.