Tag: Amazon S3

Setting and monitoring clear, appropriate, and actionable reliability goals can be a challenging task, especially in large-scale businesses. Nobl9 helps organizations use service-level objectives (SLOs) to find a balance between operational efficiency, reliable delivery of services, cost control, and customer satisfaction. Explore how you can use Nobl9’s integration with Amazon Redshift to easily set up SLOs on top of your product data and derive actionable insights.

Both AWS and Deloitte understand that a customer’s cloud journey is different and have their own set of requirements. This is why Deloitte’s Cyber CMS solution provides a wide range of options for businesses to choose from to create the right package for them. Learn how Cyber CMS can help organizations become more trustworthy, resilient, and secure through proactive management of cyber risks.

AWS Transfer Family provides SFTP, FTPS, and/or FTP access to Amazon S3 or Amazon Amazon EFS. It does that by providing a secure, highly available, and scalable server endpoint. You pay for the time this endpoint is enabled, and for data transfer (upload and download). Learn how this makes a multi-tenant setup where an endpoint is shared between different users (or applications), a more cost-efficient solution than having dedicated endpoints.

Data quality is traditionally inconsistent and slow across teams and functions. Custom solutions are purpose-built, of varying quality, and rarely maintained. Elula developed a proprietary Data Quality Checker (DQC) powered by AWS Glue, and an Automated Data Assessment (ADA) user interface to accelerate the identification and prioritization of issues. Elula is an AWS Partner and leading provider of artificial intelligence (AI) software to the financial services sector.

Data is growing in all aspects of our world; every vertical and technical domain is being pushed to the limit by growing data—geospatial is no exception. Learn about Esri’s ArcGIS GeoAnalytics Engine on Amazon EMR and how its geospatial capabilities can complement your current analytics workflows. GeoAnalytics seamlessly integrates with Amazon EMR and can be deployed with an Esri-provided bootstrap script at EMR cluster creation.

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.

One of the biggest advantages of VMware Cloud on AWS is that it can readily integrate with other AWS services. That gives you countless ways to elevate your workloads. If you’re amassing data in your databases over time and are looking for novel ways to glean fresh insights out of it, using Amazon Redshift and Amazon QuickSight is an easy and accessible way to achieve it. This post describes how to get more out of existing data residing inside your databases running in VMware Cloud on AWS.

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.

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.