A change in leadership is an exciting phase for any organization. Here at Pepperdata, we are excited to announce that we have a new CEO at the helm: Maneesh Dhir. Our former CEO, Ash Munshi, will assume the role of Executive Chairman of the company’s board of directors, following five years of continued growth. We are thrilled to have Maneesh as our new chief executive and look forward to his extensive experience and expertise in the tech space helping us thrive in the coming months and years.

Today, we have some exciting news to share: Pepperdata Capacity Optimizer now supports Azure Kubernetes Service (AKS). This means users of AKS are able to automatically optimize their workloads running on the Azure Kubernetes Service. AKS users can now rely on Pepperdata to help them monitor and optimize Spark applications on Kubernetes AKS. This evolution of our platform has been in the works for some time, and we know our users will be delighted by this development.

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?