Tag: AWS Partner References

Today’s businesses deal with many different varieties of data, including structured datasets stored in various repositories like a relational databse management system (RDBMS) or enterprise resource planning (ERP); semi-structured datasets like web logs and click-stream datasets; and unstructured datasets like images and videos. AWS provides a secure, scalable, comprehensive, and cost-effective portfolio of services that enable customers to build their data lake in the cloud.

With the increased demand from various applications and massive growth of unstructured data from workloads such as high performance computing (HPC), video rendering, or editorial workflows, traditional Network Attached Storage (NAS) solutions are a poor fit for these workloads due to their limited scalability and performance constraints. Qumulo’s continuous replication feature and ability to easily share data enables some particular use cases, such as bursting workloads across locations.

SoftNAS combines and enhances native Amazon EBS and Amazon S3 to create a full-featured cloud NAS filer. Users can move data across networks, on-premises storage architectures, and AWS. It also allows you to migrate workloads and live business applications without performance or outage worries, and cloud-enable applications without custom coding or re-engineering. Customers can run SoftNAS on multiple AZs for high availability, with automatic failover in accordance with AWS best practices.

File sync and share platforms allow companies to share files within their organization, beyond the boundaries of their own datacenter. AWS is an ideal location for these types of workloads. File sync and share solutions from APN Partners use AWS services as building blocks to provide unique offerings to customers. With a few easy steps, you can deploy CTERA and start sharing files between many different clients and platforms.

Do you want to become more familiar with how your company can use artificial intelligence (AI) and machine learning (ML) but feel a bit lost amongst the buzzwords and hype? Driving business outcomes with AI doesn’t need to be overwhelming. It’s all about exploring which business problems you want to solve, how good predictions can help you achieve those outcomes, and then taking practical steps to get there while implementing an organization-wide AI strategy.

Companies struggling with data science don’t understand the data science life cycle. As a result, they fall into the trap of the model myth. This is the mistake of thinking that because data scientists work in code, the same processes that works for building software will work for building models. Models are different, and the wrong approach leads to trouble. Domino Data Lab shares that organizations excelling at data science are those that understand it as a unique endeavor, requiring a new approach.

Fostering a data-driven culture within your organization isn’t only about technology. It’s also about enabling stakeholders to make better decisions and realizing new opportunities by embracing an AI-driven mentality for solving business problems. In this post, AWS Machine Learning Competency Partner Crayon discusses some of the first steps you should take and the essential questions to ask yourself as you thoughtfully develop your company’s relationship with data.

The nature of data has changed dramatically. Just a decade back, the majority of our data was structured (residing in relational databases) or textual. Now, with the advent of self-driving vehicles, drones, and the Internet of Things (IoT), images and video data are taking the lion’s share of the data storage zoo. As we create more and more data on more and more devices, however, this problem is not going away. In fact, we have reached a point where there aren’t enough people on the planet to label all the data we’re creating.

A multi-company team led by ARRAY delivered for the U.S. Air Force a successful modernization of a COBOL-based system running on aged mainframes to a Java-based system running on x86 Red Hat Enterprise Linux (RHEL). AWS provides the system’s capabilities for reliability, scalability, and this post describes the objectives, approach, solution, lessons, and customer benefits realized from this experience.

Spring Venture Group reached out to APN Premier Partner Logicworks to architect and manage their AWS deployment. Their developers were comfortable building and deploying containers in their on-premises environment, but were eager to get greater agility and flexibility on AWS. Running AWS Service Catalog and Amazon ECS has created a reliable, stable deployment pipeline, resulting in hundreds of hours saved for their engineering team each month.