Operations | Monitoring | ITSM | DevOps | Cloud

Spark Performance Management Optimization Best Practices | Pepperdata

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.

How to Optimize Spark Enterprise Application Performance | Pepperdata

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.

How to Maximize the Value Of Your Big Data Analytics Stack Investment

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.

How DevOps Can Reduce the Runaway Waste and Cost of Autoscaling

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.

Learn How to Simplify Kubernetes Performance Management | Pepperdata

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.

Big Data Performance Management Solution Top Considerations

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.

What can you learn from IoT with i2M - Part 2

In the first part, I outlined some of the terms associated with the delivery of IoT. Next, let’s look at how this gets complex. You will need to read the state of each sensor (through their appropriate API and through their appropriate vendor-supplied hub), create logic to determine what actions must be taken when certain conditions are met, and then deliver these as a workflow to each responder, and confirm through data collected from sensors that the requested change was implemented.

Label standard and best practices for Kubernetes security

In this blog post, I will be talking about label standard and best practices for Kubernetes security. This is a common area where I see organizations struggle to define the set of labels required to meet their security requirements. My advice is to always start with a hierarchical security design that is capable of achieving your enterprise security and compliance requirements, then define your label standard in alignment with your design.

How Data Types and Query Tuning Can Improve Application Performance

One of the easier ways to improve the performance of your SQL Server and Azure SQL database queries is to ensure you choose the right data types for your data, and the data types in your application’s code match the ones in your stored procedures and queries. Choosing the right data type conserves space, because doing something like choosing a variable character type for data of fixed, regular length like a phone number or national ID number is wasteful.