You’d probably struggle to find a big data practitioner who’s never heard of Apache Spark or used big data with Spark. We’d even go so far as to say it’s near impossible—and that’s for good reason. Spark is well known because it’s fast, reliable, and capable. Let’s dive into why that is, answer some common questions surrounding Spark computing, how to easily use it to achieve success, and more.

Are you sure your Hive queries are performing at their best? You might be surprised. Apache Hive is the most prevalent query engine used in many of the largest enterprise environments today, but that doesn’t mean it works optimally automatically. To get the most out of the engine and achieve Hive query optimization, it’s important to tune its performance. But before we dive into that, let’s cover the basics of Hive performance tuning. What is Hive performance tuning?

Container monitoring strategies and purpose-built container monitoring tools just may be the next hot topics swirling around the Kubernetes discussion forums this year. Over 77% of IT professionals expected to migrate 50% or more of their workloads to containers with Kubernetes by the end of last year. With the rise of container usage growing, having the ability to monitor the performance of your containerized workloads is critical.

Big data cloud performance management is key to success in the cloud. Paired with cloud computing, big data can transform an enterprise—especially when managed correctly. It requires no CapEx, enables quicker data processing and analysis, and allows for rapid scalability. But not having a plan to properly manage your big data performance in the cloud can be the difference between realizing the ROI the cloud promises and having to move back to the data center in defeat.

Recent data shows that the global APM (application performance management) market is booming. Currently valued at $6.3 billion, the global APM industry is expected to reach $12 billion by 2026. This growth indicates the increasing importance of monitoring, diagnosing, and improving application performance. Visibility and automation is key to sustaining the growth and evolution of APM.

Peppedata ends 2021 on a very strong note as a premiere solution for providing visibility and automation for your big data stack. Among the highlights of this year’s run are a few recognitions from our friends at G2. The site gets 5.5 million visits every month from people and enterprises that are looking for reliable solutions that meet both their needs and budget. As the biggest and most trusted marketplace for software vendors and consumers, any award from G2 is always a cause for celebration.

More companies than ever before are migrating to Kubernetes and seeing the results of Kubernetes in action. Kubernetes (K8s) is a key platform for big data users, and as such, we wanted to dive deeper and discover some new truths about current Kubernetes challenges and what the solutions might be. We surveyed 600 IT and big data professionals from various industries to determine which big data applications enterprises are moving or intending to move to Kubernetes.

Cloud computing is one of the great technologies of our era. As such, enterprises everywhere are in a hurry to migrate to the cloud. However, one of the less-talked-about trends of our time is cloud repatriation: the process of enterprises reversing their decision, leaving the cloud, and returning to an on-prem setting. According to TechTarget, 85% of enterprises reported plans of repatriating their workloads from the public cloud in 2019.

Thinking about using Google Dataproc as your cloud vendor? We can see why. Google Dataproc is a powerful tool for analytics and data processing, but to get the most out of it you have to ensure you use it properly. We’re going to explore five best practices you can use to lower your Google cloud costs while maximizing efficiency: Following these tips will ensure the best performance and help keep your cloud costs in line.