Operations | Monitoring | ITSM | DevOps | Cloud

Here at Pepperdata, we put a premium on individuals whose skills and experience can help us reach our goals. We believe our team is composed of the best people in their respective fields. Our “Pepperdata Profiles” series gives a spotlight on our gifted individuals and allows us to highlight our employees’ experiences.

Site reliability engineering (SRE) is fast becoming an essential aspect of modern IT operations, particularly in highly scaled, big data environments. As businesses and industries shift to the digital and embrace new IT infrastructures and technologies to remain operational and competitive, the need for a new approach for IT teams to find and manage the balance between launching new systems and features and ensuring these are intuitive, reliable, and friendly for end users has intensified as well.

Self service analytics are becoming increasingly popular and essential in this data-driven world. For many businesses, there is a growing need for their internal departments to access their data and business intelligence and harness its power themselves. Traditionally, business intelligence processes are the purview of IT teams and data specialists.

One of our clients is a software developer that specializes in design and manufacturing software solutions. This software firm caters to some of the biggest organizations operating in primary sectors like engineering, architecture, manufacturing, entertainment, and more. Every new software and update our client rolls out must meet stringent SLA requirements.

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?

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.