Operations | Monitoring | ITSM | DevOps | Cloud

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?

Whether you’re a product manager, a marketing organization, or in finance, you’re probably using the cloud in some way. But many businesses are still immature when it comes to building systems in the cloud, especially when it comes to how they use their financial resources. Here, CloudZero founder and CTO Erik Peterson breaks down the responsibility of using the cloud, how to break down the costs of cloud computing, and what you need to do to change the conversation from cost to profit.

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.

One of the best ways to achieve efficient product development is to make sure everyone involved with a project is on the same page, working toward the same ultimate goals. However, this is harder to do than it sounds. Engineering and product teams bring very different priorities to the table, and establishing which priorities to focus on first often becomes a matter of debate.

Companies of all sizes can take advantage of AWS Reserved Instances (RIs) to save money on the price of standard Amazon EC2 On-Demand instances. To receive the discounted rate associated with AWS Reserved Instances, you must commit to a year or three years of usage. In exchange, you can save up to 75% off the On-Demand price.

Optimizing Kubernetes costs isn’t an easy task. Kubernetes is as deep a topic as cloud (and even more complex), containing subtopics like: That’s a lot for a busy DevOps team to understand and manage, and doesn’t even consider that line-of-business stakeholders and finance team members should have some understanding of each cost driver’s function and importance to contribute to a successful FinOps Strategy.

As the complexity of Kubernetes environments grow, costs can quickly spiral out of control if an effective strategy for optimization is not in place. We’ve compiled expert recommendations and best practices for running cost-optimized Kubernetes workloads on AWS, Microsoft Azure, and Google Cloud (GCP).

When each quarter’s financial report lands on the desks of a company’s C-Suite executives, those execs are essentially looking at a black box of spending and revenue. They can see the total numbers and the growth or shrinkage of certain categories, but they ultimately have very little context into why each number is as high or low as it is. Without this context, it’s all too easy to feel sticker shock over a seemingly outrageous cloud bill and demand that costs be cut at once.

AWS Cost Explorer is one of the core pillars of AWS cloud financial management. It helps collect resource consumption data and associated cost information across multiple AWS services. Cost Explorer then helps visualize all of this data in one dashboard to ease cost management. You can then analyze, understand, and share cloud cost data with various parties. Cost Explorer is also ideal for companies that have simple cloud billing — and it's free with an AWS subscription.