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Cost Management

The latest News and Information on Cost Management and related technologies.

Lower Your Google Cloud Costs with These 5 Google Dataproc Best Practices

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.

Understanding AWS EC2 Billing: Ways To Optimize Your Costs

EC2 instances come with a spider web of charges — when you take a look at your bill, you’ll see there are hundreds of unique line items connected to your usage. In addition to the instance, you’re also billed for storage, data transfer, and networking associated with that instance. It’s important to fully understand those charges so you can find ways to optimize your costs.

Cloud Economics - And The Cloud Cost Metrics You Should Be Tracking

Over the past decade, businesses have flocked to the public cloud, with the promise of faster innovation, improved scalability — and of course, cost savings. Yet for many companies, the reality of cloud migration has been a different story. CFOs are blindsided by high costs and uncontrolled spending. Meanwhile, engineering teams struggle to justify and explain their spend — often frustrated that finance doesn’t understand, even when spend is growing healthily along with the business.

3 Improvements Finance Teams Can Make To Their FP&A Process

FP&A is a strategic part of the finance organization and has the potential to drive important business outcomes. When done right, it can have a major positive impact on the future of the business. When done poorly, it can slow a company down. The role of FP&A has evolved. Today it isn’t just about taking inputs and crunching numbers — it’s about being a strategic advisor to the organization.

Pepperdata Lets AWS Auto Scaling Execute More Big Data Workloads

Here at Pepperdata, we continuously work to improve our products and better serve our customers. Whether it’s executing more big data workloads or ensuring their resource consumption remains optimal, we want our customers to get the best value and tangible benefits from our products while not overshooting their big data cloud budgets. Today, we’re bringing you the data to back up our claims that all of this is possible.

Anodot Acquires Pileus to Transform the Cloud Cost Optimization Space

We couldn’t be more excited here at Anodot at the announcement of the acquisition of Pileus. Acquiring a company is a very special event, a moment that is the culmination of months of thought and deliberation. Is there a strong synergy between the two entities? Do we share the same DNA and culture? Is the additional product aligned with our long-term vision?

Serverless Cost Optimization: 4 Ways To Lower Your Costs

Serverless services save time. When you switch to serverless architecture, you offload redundant cloud management activities to your cloud provider and gain more time to focus on the most important parts of your business — development and innovation. Even better, you only pay for what you use, so you don’t have to worry about committing to reserved instances or committing to a savings plan. But there’s a catch.

How DevOps Can Reduce the Runaway Waste and Cost of Autoscaling

Autoscaling is the process of automatically increasing or decreasing the computational resources delivered to a cloud workload based on need. This typically means adding or reducing active servers (instances) that are leveraged against your workload within an infrastructure.

Learn How to Simplify Kubernetes Performance Management | Pepperdata

Complex applications running on Kubernetes scale super fast, but this can create visibility gaps that can make detecting and troubleshooting Kubernetes issues as difficult as finding a needle in a haystack. Although Docker and Kubernetes are now becoming standard components when building and orchestrating applications, you’re still responsible for managing the performance of applications built atop this new stack.

Big Data Performance Management Solution Top Considerations

The growing adoption of Hadoop and Spark has increased demand for Big Data and Performance Management solutions that operate at scale. However, enterprise organizations quickly realize that scaling from pilot projects to large-scale production clusters involves a steep learning curve. Despite progress, DevOps teams still struggle with multi-tenancy, cluster performance, and workflow monitoring. This webinar discusses the top considerations when choosing a big data performance management solution.