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. Keep reading to learn more about why big data performance management is so important, how to do it right, and how one tool can make it all a lot easier.
What is Big Data in the Cloud?
To break this down: Big data refers to a large amount of data that comes in quickly and becomes difficult to manage with traditional methods. Big data in the cloud is essentially running big data on any cloud—not just in the data center. Instead of using your own hardware, you have space “in the cloud” where your big data runs instead.
Why is the Cloud Used for Big Data?
On-Demand Service
One of the most highlighted benefits of running big data processes in the cloud is on-demand services. An organization’s compute demand can differ drastically during peak hours, lull moments, and random spikes in services. While maintaining an adequate amount of compute resources to meet requirements has been the norm, such practice has proven to be very costly. Conversely, if an organization attempts to decrease costs by only maintaining minimal resources, it is very probable that there will not be enough resources available to meet peak requirements.
With a big data cloud model, enterprises enjoy this on-demand functionality where they can scale compute resources up or down, depending on the prevailing requirements through a click of a button, via API, or automatically through rule-based framework configuration. Users can provision or de-provision compute resources as demanded without having to interact with the cloud provider.
No Infrastructure Maintenance
Among the chief advantages of moving applications and processes to a cloud environment is the dramatically reduced infrastructure costs. Operating a large on-premises data center can set back an organization between $10 million to $25 million a year. A substantial portion is spent on infrastructure maintenance, which includes upkeep, monitoring, security, and auditing.
Leveraging a cloud approach, when done right, can result in massive savings. That’s because cloud-based processes and apps are adaptable, scalable, and cost effective. With big data apps and tasks being moved to the cloud, enterprises can create, test, and deploy products and services at a very rapid pace. In a few cases, enterprises that moved to the cloud managed to increase their savings tenfold.
Shift to Opex from CapEx
Cloud computing effectively facilitates the shift from a CapEx (capital expenditures) spending model to Opex (operational expenditures) model for today’s enterprises. Traditionally, IT infrastructure is CapEx-heavy, incurred mainly from acquiring and maintaining capital assets like buildings, hardware, and software licenses to mention a few. On the other hand, Opex are recurring expenses that help a company produce and deliver products and services to their customers. These include utilities, wages, and compute resources.
Moving to a cloud infrastructure helps enterprises eliminate many of their Capex costs. By switching to an Opex model, an organization can create lots of internal opportunities because it frees up valuable money and resources that are otherwise spent on acquiring, managing, and maintaining capital assets.
Big Data Cloud Services by Top Providers
The global cloud computing market is poised to grow from $445.5 billion in 2021 to an impressive $947.3 billion by 2026. This growth is highlighted by intense competition among and between cloud services vendors. Household names like Amazon, Microsoft, and Google dominate this space.
Amazon Web Services (AWS) offers a plethora of big data cloud services, including Amazon Elastic MapReduce (processing large volumes of big data), Amazon DynamoDB (NoSQL data storage service), and Amazon S3 (web-scale data storage service).
Microsoft Azure provides a suite of big data services solutions and tools. Among their popular offerings are Azure Purview (unified data governance) and Azure Data Lake Storage (big, scalable, and secure data lakes).
Google has a slew of big data cloud services to offer their clients. Among their top solutions are Google Big Query (fast SQL Query engine for large data sets) and Google Prediction API (ML-powered for big data analysis and learning).
Why Big Data Cloud Performance Management Matters
While we understand the draw of moving big data to the cloud, where does managing its performance come in? Well, there are a lot of moving parts when operating in the cloud, and there are many stakeholders involved in the process:
- The business unit is concerned with resource efficiency, future growth trends, cost projections, and meeting SLA requirements.
- Data engineering needs to ensure applications are running smoothly and delivering the most valuable insights to the business.
- IT operations deal with a learning curve now that they’re in the cloud. They’re also tasked with pinpointing who expensive users are, troubleshooting runaway jobs, and ensuring they don’t miss SLAs while understanding whether an application issue is the job or the platform.
- System Architects are now tasked with optimizing a new set of systems to build an analytics stack that performs in not just one cloud but multiple.
And those are just the stakeholders and the complexity they bring to the equation. Once you add on tools, APM solutions, legacy data center tools, and homegrown solutions, it can get messy. In the cloud, there’s even more to watch out for.
So how do you make sense of everything and ultimately ensure your big data is performing optimally? Not with simply collecting more data. A tool that analyzes your performance data for you while providing observability and automation is the solution.
Observability and Automation
Data is useless without good analysis. Face it: You need answers and solutions, not just more data. That’s where a tool providing observability and automation comes in.
Observability
Observability allows you to understand why something happened—instead of just providing you with what happened. Without observability into your cluster, you’re flying blind. It takes longer to troubleshoot application problems, missed SLAs are more likely due to lack of insight, and right-sizing workloads is almost impossible. So when you’re looking for a cloud performance management solution, look for one that provides observability, not simply monitoring. Key questions to ask when looking for an observability solution are:
- Can it automatically attribute problems, performance issues, and cost to specific hosts, users, queues, and apps?
- Does it alert you to potential problems as performance changes?
- Can it automatically fix problems on its own or tell you what to do if not?
If the answer is no to any of the above questions, you’ll want to continue with your search.
Automation
From autoscaling to autonomous tuning, automation is the final piece of the puzzle when it comes to optimizing your big data performance. Manually tuning can’t cut it today. Even the most capable teams can’t manually tune every application and workflow with the required precision and speed to keep up with scale in the cloud. In fact, manually tuning is likely reducing your ROI due to wasted hardware resources, time, and effort.
Autoscaling
Another opportunity to benefit from automation in the cloud is autoscaling. While the cloud is great for its scalability and flexibility, this can be a double-edged sword that results in runaway costs for the unprepared. To combat this, many cloud providers provide autoscaling capabilities that cut waste. However, they don’t always catch it all.
Regular autoscaling typically happens in large increments, but workloads are dynamic. They may need much smaller increments of compute or need to scale up and back down quicker than the cloud provider’s autoscaling can achieve. This results in preventable waste and extra costs.
The chart below shows this clearly. The first chart displays how traditional autoscaling grew the cluster to 100 nodes for the entire runtime duration. However, the second chart shows what the cluster actually ran during the runtime duration. Sometimes it was just one task, other times no tasks were running. The 100 nodes autoscaling provided for the entire duration wasn’t necessary.
By combining observability with automation, you can better keep up with scale, shorten MTTR, and raise ROI. Automated alerts will let you know when a problem occurred, and they will provide actionable insights so you can quickly fix the issue. Proper autoscaling allows you to cut waste and runaway costs in the cloud. Automatic chargeback mechanisms empower you to better track costs, and automatic tuning ensures that deployed resources are used efficiently. These all enable you to keep up with the scale that’s expected in the cloud without blowing your cloud budget.
The Pepperdata Solution
When you’re running big data in the cloud and want to optimize its performance, observability and automation is key. Without visibility into your big data performance, you’re basically flying blind.
The Pepperdata solution offers full-stack observability and automation—in the form of autonomous tuning, managed autoscaling, and more—so you can be confident your big data stack is running at its best.
Pepperdata Capacity Optimizer with managed autoscaling provides the dynamic scaling big data workloads needed in the cloud. When paired with cloud service providers’ autoscaling features, you can further cut costs and achieve a high ROI. In fact, we recently benchmarked Capacity Optimizer against AWS Custom Auto Scaling to find out just how much of an improvement we provide. We found that when paired with the AWS Custom Auto Scaling Policy, Capacity Optimizer increased cloud CPU utilization by 157%.
Check out our white paper Observability and Continuous Tuning at Scale to learn more about how observability and automation impact big data cloud performance management.