The Software-Defined Data Center: A Foundation for Digital Transformation

tierpoint

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.
Watch Now

Spotlight

Several industries, such as financial services, telecom, retail and insurance, are among the leaders in collating, processing, and analyzing big data into reliable findings. Even more importantly, they have the ability to arrive at these insights in very quick, if not real-time. In telecom, big data analytics has helped providers mitigate the high rate of churn by predicting which customers are most likely to leave, enabling operators to target promotional offers more accurately, and even scouring social media conversations to spot telltale signs of defection. On the other hand, insurance companies have managed to speed up claims processing, improve risk management, price products based on predicted behavior (think auto insurance premium based on driving patterns), and accelerate report generation using analytics.

OTHER ON-DEMAND WEBINARS

How to Scale New Products with a Data Lake on AWS and Qubole

awscloud.com

Big data technologies can be both complex and involve time consuming manual processes. Organizations that intelligently automate big data operations lower their costs, make their teams more productive, scale more efficiently, and reduce the risk of failure.
Watch Now

Why Your Data Management Strategy Isn’t Working

IDERA

Despite good intentions, data management strategy and practices are coming up short in the majority of organizations. This is often due to many factors that are attributable to low data maturity and misalignment with business objectives. IDERA’s Ron Huizenga will discuss a number of misconceptions and behaviors that limit effectiveness, as well as how to overcome them. This will enable you to identify and implement the necessary changes to achieve business alignment and derive maximum value from your data management strategy and practices
Watch Now

Implementing Oracle Database-as-a-Service for Cloud-Like Agility

Robin Systems, Inc

A growing number of organizations are turning to Docker containers to help solve really big application requirements. Among the biggest out there are those imposed by Oracle and Oracle RAC. What if you could deploy Oracle or Oracle RAC as a stateless cloud-native workload in your environment? Transform a complex process into one with an App Store-like experience.
Watch Now

Improving Transactional Applications with Analytics

MariaDB

Today, most web and mobile applications are limited to “lightweight” analytics because general-purpose databases can be optimized for transactional or analytical workloads, but not both – and since transactional processing is critical, applications have to compromise on analytics. However, what if an e-commerce application could let customers know which products are soon to be sold out based on clickstream data, shopping carts, current inventory and recent purchases as well as historical buying patterns and emerging shopping trends? In this webinar, attendees will learn how to leverage MariaDB ColumnStore to provide transactional applications with real-time analytics on historical data.
Watch Now

Spotlight

Several industries, such as financial services, telecom, retail and insurance, are among the leaders in collating, processing, and analyzing big data into reliable findings. Even more importantly, they have the ability to arrive at these insights in very quick, if not real-time. In telecom, big data analytics has helped providers mitigate the high rate of churn by predicting which customers are most likely to leave, enabling operators to target promotional offers more accurately, and even scouring social media conversations to spot telltale signs of defection. On the other hand, insurance companies have managed to speed up claims processing, improve risk management, price products based on predicted behavior (think auto insurance premium based on driving patterns), and accelerate report generation using analytics.

resources