Kubernetes is fast becoming the world’s most preferred open-source container orchestration tool. Used for building and managing cloud-native microservices-based apps as well as the containerization of existing apps, its adoption is continuously increasing. Already, 61% of enterprises have adopted Kubernetes, and 30% are planning to embrace it within the next 12 months.

Are your Apache Impala queries running slow and not achieving peak performance? Given Impala’s complexity, troubleshooting can be very difficult. Optimizing query performance is near impossible without the right tools. Good news: Pepperdata Query Spotlight now supports Apache Impala.

In the realm of big data, Hive is a big deal. Well-written and well-designed Hive queries accelerate data retrieval from datasets. Hive is much better than SQL as the former works with complicated data more effectively. In addition, Hive queries help bring down processing costs. This is why it’s critical to write and optimize Hive queries correctly for big data analytics users and developers.

It’s a wrap! From October 24–28, the Pepperdata team attended KubeCon + CloudNativeCon North America 2022, which brought together top engineers and developers from communities centered around Kubernetes and cloud native. With topics ranging from container observability practices to K8s innovations, Pepperdata unveiled the latest offering of its own: Autonomous FinOps for Kubernetes.

TechEx North America 2022’s AI & Big Data Expo, America’s leading enterprise technology exhibition, concluded with incredible insights across ML, cloud infrastructure, and big data analytics industries. Showcasing expert panel discussions, live demos and high-level presentations from over 250+ speakers, the convention highlighted five key takeaways — some innovations, yet some gaps in terms of big data progression.

Bill shock stemming from surprisingly high cloud bills remains a persistent concern for enterprises that have migrated to the cloud. Cloud service providers often market the cloud as a highly effective means to lower operational costs. By moving their applications and processes to cloud-hosted infrastructures, organizations are saving money that would have gone to datacenters, hardware, and personnel.

Picking the right AWS instance is challenging because you have to match the ever-changing resource usage patterns of your apps with the 500+ AWS instance types, and the result is over-provisioning and waste. Nearly one-third of businesses exceed their cloud budgets by as much as 40%, in part due to poorly sized instances.

As almost 90% of organizations are executing on a multi-cloud strategy for migrating their data and analytics workloads to the cloud, the term “modern data stack” continues to gain more traction. A modern data stack is a suite of technologies and apps built specifically to funnel data into an organization, transform it into actionable data, build a plan for acting on that data, and then implement that plan.

In part one of this two-part blog post, we began our deep dive into Apache Spark tuning to optimize resources. We looked at what is involved in executor and partition sizing, particularly choosing the number of partitions and choosing an executor size. After establishing some principles of optimization here, we ended by asking an important question: Is it really practical for all applications to be optimized? As our recent State of the Market report helped reveal, the answer is two-sided. The good news?

As our recent survey showed, Apache Spark is poised to continue as big data’s most dominant large-scale big data processing platform. Thus it is imperative that Spark users learn and master Spark tuning if they want to get the most out of their Spark environments. But what is tuning in Spark? How is it done? Read on to know more about Spark tuning.