Tag: Amazon Simple Storage Service (Amazon S3)

Users have successfully leveraged Amazon Web Services (AWS) Snow Family to transfer petabytes of data between on premises and AWS Regions since its launch in 2015 with the AWS Snowball device. Increasingly, users are not just migrating data with the AWS Snow Family but now are using AWS Snowball Edge Compute Optimized devices to host […]

Organizations continue to grow their data lakes in the cloud as they build out new and innovative analytics, machine-learning, and generative AI workloads. At the same time, these workloads often access data that requires compliance with stringent data security and privacy standards. These compliance frameworks typically specify additional requirements for encryption at-rest, which leads customers […]

Users face challenges in their digital transformation journey involving the migration of data across various platforms, on-premises file systems, and other cloud services. When using other cloud providers, scenarios arise where the seamless transfer of data becomes essential. Whether executing a one-time data transfer or integrating it into a scheduled workflow, minimizing business downtime is […]

Cost is top of mind for many enterprises, and building awareness of different cost contributors is the first step toward managing costs and improving efficiency. Costs for transferring data may segregate into common but low cost and less frequent but higher cost groups. Data about these two groups is mixed together, and separating them enables […]

Over time, Enterprises may need to undertake operations or make modifications to their data as part of general data management, to address changing business needs, or to comply with evolving data-management regulations and best practices. As datasets being generated, stored, and analyzed continue to grow exponentially, the need for simplified, scalable, and reproduceable data management […]

Moving large volumes of data across your hybrid cloud environments can seem like a daunting task, especially when dealing with a litany of requirements that arise when working within the technical limits of network, storage, compute, and operating system layers, both on-premises and in the cloud. Users face additional challenges when balancing their Recovery Time […]

The telecommunications industry has undergone major technological changes in recent years, and the extensive data generated by telecom operations demands strategic handling. According to a McKinsey survey, over 50% of telecom companies said they were moving more assets to the cloud, including many business-critical applications such as business support systems (BSS). Telecom companies must prioritize […]

Companies from different industries use data to help their Artificial Intelligence (AI) and Machine Learning (ML) systems make intelligent decisions. For ML systems to work well, it is crucial to make sure that the massive datasets used for training ML models are of the highest quality, minimizing noise that can contribute to less-than-optimal performance. Processing […]

Many organizations generate large quantities of data locally, including digital imagery, sensor data, and more. Customers require local compute and storage to ingest and enable real-time predications based on their data, and often preprocess this data locally before transferring to the cloud to unlock additional business value such analysis, reporting, and archiving. Automating transfers to […]

Data, the lifeblood of any modern organization, is rarely static. For high-performance applications and workloads, enterprises need the ability to run operations on massive amounts of data, including modifying the data as is necessary for each use case, to further accelerate processing. This could include modifying uploaded images with a watermark, changing the bitrate of […]