Data Visualization to Educate and Informs + Audi Case Study

As organizations seek to deliver new applications and services, they often also need highly scalable and lightning-fast data management platforms to support these innovations. For instance, some new applications work against millions or billions of rows of data and require a response time in milliseconds. These might include financial services applications that need to process transactions with sub-millisecond response rates and be globally available.

As reported in Forbes last year, 73% of companies are planning to move to a fully software-defined data center within two years. A software-defined data center is based on a virtualized environment of compute, storage, networking and security in conjunction with policy-based management and automation. The evolution from a traditional data center architecture to one that is software-defined can take months if not years but can yield immense benefits for the business. Join us for a discussion on the progress toward the fully software-defined data center, including benefits of infrastructure as code and overcoming challenges associated with traditional workflows.

Fresh data beats stale data for machine learning applications. This on demand webinar discusses the value of fresh data as well as different types of architecture and challenges of online prediction, it will also cover the tradeoffs between latency, staleness, and cost.

What began as a trickle is now the mainstream: Organizations are moving to the cloud for data management. No longer is the cloud just a cheaper place to park data; it is key to supporting business-critical innovation in advanced analytics, data science, and AI as well as for end-user business intelligence, data exploration, and data visualization. Data lakes and data warehouses running on market-leading platforms such as AWS are growing fast, just as they are on the platforms of competing providers. However, as cloud-based workloads grow in number and size, organizations are facing difficult data preparation challenges. The flow of data raw, diverse, and frequently unstructured can quickly turn cloud data lakes into impenetrable swamps. Without good data preparation technologies and practices, users of all types are frustrated; their productivity and satisfaction suffer because it’s too hard to get accurate data that’s appropriately transformed and structured for their purposes.