As we have seen, the world can change quickly. What you were working on yesterday may not be what you work on today. How do you adapt and be able to existentially flex, stay ahead, and deliver quickly. This session will look at how to do this by building composable solutions with APIs to integrate, connect, and automate various infrastructure operations. Leverage various cloud APIs to build high quality solutions through reusability and sharing for quicker time to delivery.

During this live session, I am super excited to share how we built Qovery - a cloud deployment platform for growing startups. In a few numbers:

Did you know that your service desk plays a key role in co-creating your organisation's value and is therefore an important consideration in plans for continual improvement? Find out why and learn more insights into the topic from IT expert Peter Brooks. Here are the key takeaways: Watch more ITIL/ITSM related videos from industry experts: Follow us on social.

Join our Customer Solutions Engineer for a hands-on introduction to SquaredUp.

Gain the knowledge of Spark veteran Alex Pierce on how to manage the challenges of maintaining the performance and usability of your Spark jobs. Apache Spark provides sophisticated ways for enterprises to leverage big data compared to Hadoop. However, the increasing amount of data being analyzed and processed through the framework is massive and continues to push the boundaries of the engine.

Does your big data analytics platform provide you with the Spark recommendations you need to optimize your application performance and improve your own skillset? Explore how you can use Spark recommendations to untangle the complexity of your Spark applications, reduce waste and cost, and enhance your own knowledge of Spark best practices. Topics include: Join Product Manager Heidi Carson and Field Engineer Alex Pierce from Pepperdata to gain real-world experience with a variety of Spark recommendations, and participate in the Q and A that follows.

Big data analytics performance management is a competitive differentiator and a priority for data-driven companies. However, optimizing IT costs while guaranteeing performance and reliability in distributed systems is difficult. The complexity of distributed systems makes it critically important to have unified visibility into the entire stack. This webinar discusses how to maximize the business value of your big data analytics stack investment and achieve ROI while reducing expenses. Learn how to.

Autoscaling is the process of automatically increasing or decreasing the computational resources delivered to a cloud workload based on need. This typically means adding or reducing active servers (instances) that are leveraged against your workload within an infrastructure.

Complex applications running on Kubernetes scale super fast, but this can create visibility gaps that can make detecting and troubleshooting Kubernetes issues as difficult as finding a needle in a haystack. Although Docker and Kubernetes are now becoming standard components when building and orchestrating applications, you’re still responsible for managing the performance of applications built atop this new stack.

The growing adoption of Hadoop and Spark has increased demand for Big Data and Performance Management solutions that operate at scale. However, enterprise organizations quickly realize that scaling from pilot projects to large-scale production clusters involves a steep learning curve. Despite progress, DevOps teams still struggle with multi-tenancy, cluster performance, and workflow monitoring. This webinar discusses the top considerations when choosing a big data performance management solution.