Tag: AWS Partner Success Stories

DataMasque is an AWS Partner that removes sensitive data from the databases and replaces it with realistic and functional masked values that enable effective development, testing, and analytics. This empowers AWS customers to do secure deployments in testing or non-production environments while mitigating the data breach security risks and allowing relevant teams to focus on development and testing of their applications without hinderance.

This year marks the 10-year anniversary of the AWS Partner Network (APN). To celebrate this important milestone, we’re launching a series of partner transformation stories—“10 Years of Success”—highlighting some of our AWS Partners who have been “all in” and grown with us over the past decade. Up first in our Q&A-style series is Cloudsoft, an AWS Advanced Tier Services Partner that helps businesses securely migrate, run, and evolve applications in the cloud.

As part of its SaaS platform, Druva runs containerized applications with microservices architecture using Auto Scaling groups and Amazon Amazon ECS. Explore the cost optimization journey of Druva using Spot instances with Auto Scaling groups, challenges faced by them during this journey, and how attribute-based instance type selection (ABS) came to the rescue in addressing those challenges in optimizing costs.

To ensure the safety of travelers and give them renewed confidence in flying, the air travel industry has introduced processes to obtain and validate COVID-19 vaccination information before journey commencement at the origin airport. In this post, we discuss how the WNS Document Verification Solution leverages Amazon Web Services (AWS) to capture auditor telemetry for enabling increased auditor productivity.

LoRaWAN is a leading protocol utilizing Semtech’s LoRa long-range communications technology for LPWAN in battery-operated, remote monitoring, and control applications. AWS IoT Core for LoRaWAN offers a fully-managed LoRaWAN network server and delivers highly scalable, reliable services for a wide variety of use cases. Learn the core elements in an asset tracking application and how AWS and Semtech help lower the barrier for developers looking to deploy solutions.

AWS Migration Hub Refactor Spaces is designed to help customers refactor existing applications incrementally, while increasing business agility and time to market and maximizing the business value of AWS. Follow a demonstration of an actual upgrade of a monolithic application with AWS Migration Hub Refactor Spaces, including setting up the Migration Spaces environment and refactoring two microservices of the monolithic application.

Firms spend substantial efforts to identify and collect quality data streams from different sources. However, identifying and interpreting energy, water, or gas usage patterns and consumption types is sometimes insufficient. In this post, you’ll learn how Storm Reply, combining industry knowledge with its expertise in the development of data analytics platforms in AWS, can help customers in the design, development, and maintenance of secure serverless IoT big data platforms with a focus on sustainability.

AWS artificial intelligence services can serve as a kind of ready-made building block that enable companies of all sizes and sectors to gain experience and create their own AI services, without having to build the fundamental functions from the bottom up. Via the cloud, companies can access AWS AI services and create their own chatbots, image analysis, or personalization tools, for example. To implement and customize these services, companies can draw on the expertise of Trivadis – Part of Accenture.

Data-driven decision making is accelerating and defining the way organizations work. With this transformation, there has been a rapid adoption of data lakes across the industry. Hear from Rackspace, an AWS Premier Tier Services Partner, about the drawbacks of existing data lake architecture, what Apache Iceberg is, and how it overcomes the shortcomings of the current state of data lakes. Then dive deep on the design differences between Apache Hive and Iceberg.

Data-centric AI (DCAI) has been described as the discipline of systematically engineering the data used to build an AI system. It prescribes prioritizing improving data quality over tweaking algorithms to improve machine learning models. In this post, explore a DCAI solution built on Snowflake and Amazon SageMaker to serve as a factory for predictive analytics solutions. Learn about Snowflake’s integrations with SageMaker and get hands-on resources to help you put these capabilities into practice.