Tag: Amazon Athena

Many enterprises have .NET Framework legacy applications they need modernize and convert to cloud-native. One of the largest product feed automation and optimization platforms, GoDataFeed has a back-end platform that ingests millions of items of catalog data from multiple sources. Learn how Kloia addressed GoDataFeed’s challenges by transforming their legacy .NET Framework monolithic application into a .NET Core-based decoupled architecture.

Fragmented supply-chain management systems can impair an enterprise’s ability to make informed, timely decisions. Accenture’s Intelligent Revenue and Supply Chain (IRAS) platform integrates insights generated by machine learning models into an enterprise’s technical and business ecosystems. This post explains how Accenture’s IRAS solution is architected, how it can coexist with other ML forecasting models or statistical packages, and how you can consume its insights in an integrated way.

To help everyone visualize COVID-19 data confidently and responsibly, we brought together APN Partners Salesforce, Tableau, and MuleSoft to create a centralized repository of trusted data from open source COVID-19 data providers. Anyone can work with the public data, blend it with their own data, or subscribe to the source datasets directly through AWS Data Exchange, and then use Amazon Redshift together with Tableau to better understand the impact on their organization.

Explore several common AWS services and architectural patterns used by SaaS vendors to interact with their customers’ cloud accounts. Examples of SaaS products requiring some level of account interaction often fall into the categories of logging and monitoring, security, compliance, data analytics, DevOps, workflow management, and resource optimization. SaaS products, such as the ones in these categories, regularly interact with resources in the subscribing customer’s AWS account.

As larger and more complex workloads are deployed on AWS, multi-account solutions are an increasingly common architectural blueprint. Often referred to as cloud “landing zones,” these blueprints enable simple administrative boundaries. However, using multiple accounts increases the complexity of security tooling, access control and authorization, and cross-account networking. AWS Control Tower simplifies the process of setting up multi-account environments with predefined security baseline templates.

Next Caller uses machine learning on AWS to drive data analysis and the processing pipeline. Amazon SageMaker helps Next Caller understand call pathways through the telephone network, rendering analysis in approximately 125 milliseconds with the VeriCall analysis engine. VeriCall verifies that a phone call is coming from the physical device that owns the phone number, and flags spoofed calls and other suspicious interactions in real-time.

Data is the lifeblood of a digital business and a key competitive advantage for many companies holding large amounts of data in multiple cloud regions. Imperva protects web applications and data assets, and in this post we examine how you can use SQL to analyze big data directly, or to pre-process the data for further analysis by machine learning. You’ll also learn about the benefits and limitations of using SQL, and see examples of clustering and data extraction.

Over two days, Steamhaus conducted an AWS Well-Architected Review on-site with the team who designed, built, and currently manage Elmer at Sperry Rail. Elmer uses machine intelligence to inspect thousands of miles of ultrasound scans collected by Sperry’s inspection vehicles, searching for evidence of cracks in the rail. This partnership allowed quick improvements in efficiency, while ensuring the requirements of running the business day-to-day did not get in the way of improving Elmer.

Many organizations face the challenge of gathering market intelligence on new product and platform announcements made by their partners and competitors—and doing so in a timely fashion. Harnessing these insights quickly can help businesses react to specific industry trends and fuel innovative products and offerings inside their own company.Learn how Accenture helped a customer use AWS to gather critical insights along with key signals and trends from the web using AI and ML techniques.

Analyzing large datasets can be challenging, especially if you aren’t thinking about certain characteristics of the data and what you’re ultimately looking to achieve. There are a number of factors organizations need to consider in order to build systems that are flexible, affordable, and fast. Here, experts from CloudZero walk through how to use AWS services to analyze customer billing data and provide value to end users.