Thousands of customers are building their data lakes on Amazon Simple Storage Service (Amazon S3). You can use AWS Lake Formation to build your data lakes easily—in a matter of days as opposed to months. However, there are still some difficult challenges to address with your data lakes: Supporting streaming updates and deletes in your data lakes
Amazon DynamoDB helps you capture high-velocity data such as clickstream data to form customized user profiles and online order transaction data to develop customer order fulfillment applications, improve customer satisfaction, and get insights into sales revenue to create a promotional offer for the customer.
The first article in this series provided an overview of a data lake solution architecture using Data Fusion for data integration and Cloud Composer for orchestration. In this article, I will provide an overview of the detailed solution design based on that architecture.
Business analysts working with larger and larger data sets are finding traditional BI methods can’t keep up with their need for speed. BigQuery BI Engine is designed to meet this need by accelerating the most popular dashboards and reports that connect to BigQuery.
Data driven enterprises are transforming their businesses by migrating their on-prem data lakes and data warehouses to the cloud so they can enable new analytics at scale. As every enterprise looks to migrate, it’s important that IT leaders don’t forget about a key data stakeholder, the data scientist.
Trace Analytics is now available for Amazon Elasticsearch Service (Amazon ES) domains running versions 7.9 or later. Developers and IT Ops teams can use this feature to troubleshoot performance and availability issues in their distributed applications.
Amazon QuickSight recently added support for Amazon Athena Federated Query , which allows you to query data in place from various data sources. With this capability, QuickSight can extend support to query additional data sources like Amazon CloudWatch Logs , Amazon DynamoDB , and Amazon DocumentDB (with Mongo DB compatibility) via their existing Amazon Athena data source.
The ability to query data and perform ad hoc analysis across multiple platforms and data stores with a single tool brings immense value to the big data analytical arena. As organizations build out data lakes with increasing volumes of data, there is a growing need to combine that data with large amounts of data in other data stores.
Software service providers offer subscription-based analytics capabilities in the cloud with Analytics as a Service (AaaS), and increasingly customers are turning to AaaS for business insights. A multi-tenant storage strategy allows the service providers to build a cost-effective architecture to meet increasing demand.
Amazon Athena is an interactive query service that makes it easy to analyze data in a data lake using standard SQL. One of the key
In this blog, I’ll talk about how we used Amazon QuickSight embedded business intelligence (BI) to quickly bring powerful dashboards to our on-premises and cloud-based customers.
In this report, we look at the data generated by the O’Reilly online learning platform to discern trends in the technology industry—trends technology leaders need to follow. But what are “trends”? All too often, trends degenerate into horse races over languages and platforms.
In a previous post, we introduced the Amazon EMR notebook APIs, which allow you to programmatically run a notebook on both Amazon EMR Notebooks and
In this post, I demonstrate how you can enable data analysts, scientists, and advanced business users to query data from Amazon Redshift or Amazon Simple Storage Service (Amazon S3) directly. I also demonstrate configuring a simple drag-and-drop interface for self-service analytics so you can prepare and publish insights based on enriched data stored in Amazon Redshift or Amazon S3 through Amazon QuickSight.
