OPTIMIZE PERFORMANCE FOR YOUR ENTIRE BIG DATA STACK

PLATFORM SPOTLIGHT

APPLICATION SPOTLIGHT

CAPACITY OPTIMIZER

The 451 Take on Cloud-Native: Truly Transformative for Enterprise IT

Helping to shape the modern software development and IT operations paradigms, cloud-native represents a significant shift in enterprise IT. In this report, we define cloud-native and offer some perspective on why it matters and what it means for the industry.

Elements of Big Data APM Success

Pepperdata delivers proven big data APM products, operational experience, and deep expertise.

PLATFORM SPOTLIGHT
PLACEHOLDER

Request a trial to see firsthand how Pepperdata big data solutions can help you achieve big data performance success. Pepperdata’s proven APM solutions provide a 360° degree view of both your platform and applications, with realtime tuning, recommendations, and alerting. See and understand how Pepperdata big data performance solutions helps you to quickly pinpoint and resolve big data performance bottlenecks. See for yourself why Pepperdata’s big data APM solutions are used to manage performance on over 30K Hadoop production nodes.

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Resources

Cloudwick Collaborates with Pepperdata to Ensure SLAs and Performance are Maintained for AWS Migration Service

Pepperdata Provides Pre- and Post-Migration Workload Analysis, Application Performance Assessment and SLA Validation for Cloudwick AWS Migration Customers

San Francisco — Strata Data Conference (Booth 926)  — March 27, 2019 — Pepperdata, the leader in big data Application Performance Management (APM), and Cloudwick, leading provider of digital business services and solutions to the Global 1000, today announced a collaborative offering for enterprises migrating their big data to Amazon Web Services (AWS). Pepperdata provides Cloudwick with a baseline of on-premises performance, maps workloads to optimal static and on-demand instances, diagnoses any issues that arise during migration and assesses performance after the move to ensure the same or better performance and SLAs.

“The biggest challenge for enterprises migrating big data to the cloud is ensuring SLAs are maintained without having to devote resources to entirely re-engineer applications,” said Ash Munshi, Pepperdata CEO. “Cloudwick and Pepperdata ensure workloads are migrated successfully by analyzing and establishing a metrics-based performance baseline.”

“Migrating to the cloud without looking at the performance data first is risky for organizations and if a migration is not done right, the complaints from lines of business are unavoidable,” said Mark Schreiber, General Manager for Cloudwick. “Without Pepperdata’s metrics and analysis before and after the migration, there is no way to prove performance levels are maintained in the cloud.”

For Cloudwick’s AWS Migration Services, Pepperdata is installed on customers’ existing, on-premises clusters — it takes under 30 minutes — and automatically collects over 350 real-time operational metrics from applications and infrastructure resources, including CPU, RAM, disk I/O, and network usage metrics on every job, task, user, host, workflow, and queue. These metrics are used to analyze performance and SLAs, accurately map workloads to appropriate AWS instances, and provide cost projections. Once the AWS migration is complete, the same operational metrics from the cloud are collected and analyzed to assess performance results and validate migration success.

To learn more, stop by the Pepperdata booth (926) at Strata Data Conference March 25-28 at Moscone West in San Francisco.

More Info

About Pepperdata
Pepperdata (https://pepperdata.com) is the leader in big data Application Performance Management (APM) solutions and services, solving application and infrastructure issues throughout the stack for developers and operations managers. The company partners with its customers to provide proven products, operational experience, and deep expertise to deliver predictable performance, empowered users, managed costs and managed growth for their big data investments, both on-premise and in the cloud. Leading companies like Comcast, Philips Wellcentive and NBC Universal depend on Pepperdata to deliver big data success.

 Founded in 2012 and headquartered in Cupertino, California, Pepperdata has attracted executive and engineering talent from Yahoo, Google, Microsoft and Netflix. Pepperdata investors include Citi Ventures, Costanoa Ventures, Signia Venture Partners, Silicon Valley Data Capital and Wing Venture Capital, along with leading high-profile individual investors. For more information, visit www.pepperdata.com.

About Cloudwick

Cloudwick is the leading provider of digital business services and solutions to the Global 1000. Its solutions include data migration, business intelligence modernization, data science, cybersecurity, IoT and mobile application development and more, enabling data-driven enterprises to gain competitive advantage from big data, cloud computing and advanced analytics. Learn more at www.cloudwick.com.

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Contact:
Samantha Leggat
samantha@pepperdata.com

Pepperdata and the Pepperdata logo are registered trademarks of Pepperdata, Inc. Other names may be trademarks of their respective owners.

March 27, 2019

Pepperdata Announces Free Big Data Cloud Migration Cost Assessment to Automatically Select Optimal Instance Types and Provide Accurate Cost Projections

Pepperdata Eliminates Guesswork and Complexity Associated with Identifying Best Candidate Workloads Down to Queue, Job and User Level, for Moving to AWS, Azure, Google Cloud or IBM Cloud

CUPERTINO, Calif. — March 6, 2019 — Pepperdata, the leader in big data Application Performance Management (APM), today announced its new Big Data Cloud Migration Cost Assessment for enterprises looking to migrate their big data workloads to AWS, Azure, Google Cloud or IBM Cloud. By analyzing current workloads and service level agreements, the detailed, metrics-based Assessment enables enterprises to make informed decisions, helping minimize risk while ensuring SLAs are maintained after cloud migration.

The Pepperdata Big Data Cloud Migration Cost Assessment provides organizations with an accurate understanding of their network, compute and storage needs to run their big data applications in the hybrid cloud. Analyzing memory, CPU and IO every five seconds for every task, Pepperdata maps the on-premises workloads to optimal static and on-demand instances on AWS, Azure, Google Cloud, and IBM Cloud. Pepperdata also identifies how many of each instance type will be needed and calculates cloud CPU and memory costs to achieve the same performance and SLAs of the existing on-prem infrastructure.

“When enterprises consider a hybrid cloud strategy, they estimate the cost of moving entire clusters, but that’s not the best approach,” said Ash Munshi, Pepperdata CEO. “It’s far better to identify specific workloads that can be moved to take full advantage of the pricing and elasticity of the cloud. Pepperdata collects and analyzes detailed, granular resource metrics to accurately identify optimal workloads for cloud migration while maintaining SLAs.”

The Big Data Cloud Migration Cost Assessment enables enterprises to:

  • Automatically analyze every workload in your cluster to accurately determine their projected cloud costs
  • Get cost projections and instance recommendations for workloads, queues, jobs, and users
  • Map big data workloads to various instance types including static and on-demand
  • Compare AWS, Azure, Google Cloud, and IBM Cloud

Availability

Pepperdata Big Data Cloud Migration Cost Assessment is available free at pepperdata.com/free-big-data-cloud-migration-cost-assessment. Pepperdata customers should email support@pepperdata.com for their free assessment.

Learn more:

About Pepperdata
Pepperdata (https://www.pepperdata.com) is the leader in big data Application Performance Management (APM) solutions and services, solving application and infrastructure issues throughout the stack for developers and operations managers. The company partners with its customers to provide proven products, operational experience, and deep expertise to deliver predictable performance, empowered users, managed costs and managed growth for their big data investments, both on-premise and in the cloud. Leading companies like Comcast, Philips Wellcentive and NBC Universal depend on Pepperdata to deliver big data success.

 Founded in 2012 and headquartered in Cupertino, California, Pepperdata has attracted executive and engineering talent from Yahoo, Google, Microsoft and Netflix. Pepperdata investors include Citi Ventures, Costanoa Ventures, Signia Venture Partners, Silicon Valley Data Capital and Wing Venture Capital, along with leading high-profile individual investors. For more information, visit www.pepperdata.com.

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Contact:
Samantha Leggat

925-447-5300
samantha@pepperdata.com

Pepperdata and the Pepperdata logo are registered trademarks of Pepperdata, Inc. Other names may be trademarks of their respective owners.

March 5, 2019

Pepperdata Unveils 360° Reports, Enabling Enterprises to Make More Informed Operational Decisions to Maximize Capacity and Improve Application Performance

360° Reports Empower Executives to Better Understand Financial Impacts of Operational Decisions

CUPERTINO, Calif. — February 19, 2019 — Pepperdata, the leader in big data Application Performance Management (APM), today announced the availability of 360° Reports for Platform Spotlight. Pepperdata 360° Reports leverage the vast amount of proprietary data collected and correlated by Pepperdata to give executives capacity utilization insights so they better understand the financial impacts of operational decisions.

“Pepperdata 360° Reports demonstrate the power of data and the valuable insights Pepperdata provides, enabling enterprises to make more informed and effective operational decisions,” said Ash Munshi, Pepperdata CEO. “Operators get a better understanding of what and where they’re spending, where waste can be reclaimed, and where policy and resource adjustments can be made to save money, maximize capacity and improve application performance.”

360° Reports for Pepperdata Platform Spotlight include:

  • Capacity Optimizer Report: This gives operators insight into memory and money saved by leveraging Pepperdata Capacity Optimizer to dynamically recapture wasted capacity.
  • Application Waste Report: This report compares memory requested with actual memory utilization so operators can optimize resources by changing resource reservation parameters.
  • Application Type Report: This gives operators insight on the technologies used across the cluster and the percentage of each (percentage of Spark jobs, etc.). This provides executives with insights into technology trends to make more data-driven investment decisions.
  • Default Container Size Report: This report identifies jobs using default container size and where any waste occurred so operators can make default container size adjustments to save money.
  • Pepperdata Usage Report: This presents Pepperdata dashboard usage data, highlighting top users, days used, and more to give operators insights to maximize their investment. With this data, operators can identify activities to grow the user base, such as promoting features, scheduling onboarding sessions, and training on custom alarms.

Availability

Pepperdata 360° Reports are available immediately for Pepperdata Platform Spotlight customers. For a free trial of Pepperdata, visit https://www.pepperdata.com/trial.

About Pepperdata
Pepperdata (https://pepperdata.com) is the leader in big data Application Performance Management (APM) solutions and services, solving application and infrastructure issues throughout the stack for developers and operations managers. The company partners with its customers to provide proven products, operational experience, and deep expertise to deliver predictable performance, empowered users, managed costs and managed growth for their big data investments, both on-premise and in the cloud. Leading companies like Comcast, Philips Wellcentive and NBC Universal depend on Pepperdata to deliver big data success.

 Founded in 2012 and headquartered in Cupertino, California, Pepperdata has attracted executive and engineering talent from Yahoo, Google, Microsoft and Netflix. Pepperdata investors include Citi Ventures, Costanoa Ventures, Signia Venture Partners, Silicon Valley Data Capital and Wing Venture Capital, along with leading high-profile individual investors. For more information, visit www.pepperdata.com.

###

Contact:
Samantha Leggat
samantha@pepperdata.com

Pepperdata and the Pepperdata logo are registered trademarks of Pepperdata, Inc. Other names may be trademarks of their respective owners.

Sample report attached.

Sample Capacity Optimizer Report – memory and money saved with Capacity Optimizer

February 19, 2019

Why MTTR Matters and How Big Data APM Can Help

In the world of big data IT, performance is everything. User satisfaction with IT infrastructure is determined by application availability and response times. But in that same world, failure is inevitable, even within the most robust IT infrastructure. And each instance of downtime or failure to meet availability and/or performance objectives can have a significant effect on customer satisfaction. So when technology fails, your first thought is how to utilize incident management knowledge to resolve the situation and minimize downtime.  

MTTR is an acronym that has been typically associated with Mean Time to Repair, a measure of how long it takes to get a product or subsystem up and running after a failure. It’s used in the context of a traditional data center and relates to the physical infrastructure of an organization like servers and the network. Mean Time to Repair is calculated by taking total maintenance time over a given period and dividing it by the number of incidents that occurred.

However, In a digitized world that revolves around big data applications and distributed computing architectures, it’s more accurate to think in terms of another MTTR definition, Mean Time to Recovery.  When IT support speed is of the essence, that definition of MTTR becomes a key focus.  Mean Time to Recovery is a service-level metric that measures the average elapsed time from when an incident is reported until the incident is resolved and the affected system or service has recovered from a failure.  It includes the time it takes to identify the failure, diagnose the problem and repair it, and is measured in business hours, not clock hours. 

A ticket that is opened at 4:00 pm on a Friday and closed out at 4:00 pm the following Monday, for example, will have a resolution time of eight business hours, not 72 clock hours. MTTR comes into play when entering into contracts that include Service Level Agreement (SLA) targets or maintenance agreements. In SLA targets and maintenance contracts, you would generally agree to some Mean Time to Recovery metric to provide a minimum service level that you can hold the vendor accountable for. In a digitized environment where infrastructure and hardware repair has become more automated, Mean Time to Recovery can refer to application as well as infrastructure issues.

Digital transformation encompasses cloud adoption, rapid change, and the implementation of new technologies. It also requires a shift in focus to applications and developers, an increased pace of innovation and deployment, and the involvement of new digital components like machine agents, Internet of Things (IOT) devices, and Application Program Interfaces (APIs). 

When your network or applications unexpectedly fail or crash, IT downtime can have a direct impact on your bottom line and ongoing business operations. According to Gartner, the average cost of IT downtime is $5,600 per minute, which extrapolates to well over $300K per hour.  However, this is just an average and there is a large degree of variance based on the characteristics of your business and IT environment. The cost to online businesses can soar into the millions of dollars per hour.  Amazon’s one hour of downtime on Prime Day in 2018 may have cost it up to $100 million in lost sales.

Reducing and accelerating MTTR enables you to save time and IT resources, as well as mitigate incident severity, frequency, and the likelihood of application or service downtime. To resolve issues there are usually three basic steps involved:

  • Detecting the problem, ideally before it impacts users or when its significance is low
  • Diagnosing the problem rapidly using detailed information to consistently narrow the search
  • Resolving and testing to confirm that the problem has been fixed

Reducing MTTR is a key objective of IT Operations groups with the desired outcome of improved stakeholder satisfaction. The majority of total problem resolution time is taken with identifying the root cause of a problem, and the minority in actually fixing it. Problems that are left to escalate will have a much higher cost to the organization. So, being able to quickly identify the root cause of a problem can drastically reduce the MTTR for enterprise applications and analytics workloads.

However, application environments vary in scale and complexity and there is no “one size fits all” solution. Big data environments, for example, are exceptional and require a specialized approach to resolving application and service MTTR issues. Data is constantly generated anytime we open an app, search Google or simply travel from place to place with our mobile devices. The result is big data: massive, complex structured and unstructured data sets that are generated and transmitted from a wide variety of sources, stored on Hadoop and Spark platforms, and ultimately visualized and analyzed.

There is no official definition of big data, but a common one is “data sets that are too large for traditional tools to store, process, or analyze”. Traditional application performance management (APM) solutions simply aren’t equipped to handle this kind of complexity and volume. Resolving big data performance issues requires an APM solution specifically designed for big data environments.

Big data workloads and applications are often plagued by multiple performance problems that result in system failures, which are only magnified in a distributed computing architecture like Hadoop and Spark.  Intermittent performance problems, in particular, tend to be the most challenging to diagnose for several reasons:

  • The conditions of the failure are often elusive
  • Re-occurrence is unpredictable
  • There are few opportunities to observe the problem
  • The environment itself is changing through the course of these long-running problems

A big data APM approach addresses all of these challenges and enables ITOps and Developers to quickly diagnose performance problems. That’s because a big data APM approach, using Pepperdata Application Spotlight and Platform Spotlight, continuously collects application and infrastructure performance metrics from more than 300 data points, from each node in a big data cluster, every five seconds. This rich set of metrics enables Pepperdata customers to rapidly detect the root cause of problems. Over the past year, Pepperdata has captured more than 900 Trillion data points from more than 275 big data production clusters, a figure which continues to grow.  

Proactive big data application performance management with Pepperdata Application Spotlight and Platform Spotlight can reduce MTTR by up to 95 percent, and in many cases, pre-empt service downtime in large-scale, multi-tenant Hadoop and Spark environments. With Pepperdata big data APM solutions, determining the root cause of bottlenecks and other performance-related problems takes minutes instead of hours or days. Pepperdata big data APM solutions also help raise the flag on symptoms before they become problems, from finding sluggish queries to identifying high volume requests that should be optimized.

August 20, 2019

Why Financial Services Needs Big Data APM

Why Financial Services Needs Big Data APM

Financial Services organizations operate in a challenging environment. As one of the most heavily regulated industries in the world, they are a constant target of hackers and fraudsters. At the same time, their applications and services are essential components of the global economy. These systems must be highly available and performance-optimized while generating investor and shareholder returns.

The primary big data use case for financial services is business analytics that run on Hadoop.  Data-driven analytics are key to the current and future competitiveness of financial services companies.  By capturing and leveraging massive volumes of data, financial services companies are capitalizing on new data-driven business opportunities. But the highly regulated nature of the financial services sector and concerns around uptime and data security make managing these applications difficult.

Proactively monitoring the performance of your critical applications and services with a big data Application Performance Management (APM) solution can help you avoid operational nightmares and enable you to find and fix application and infrastructure issues before they impact your organization. Pepperdata Big Data APM products like Application Spotlight and Platform Spotlight monitor and optimize business intelligence applications that analyze customer data, manage thousands of concurrent queries, automate business processes, optimize risk controls and business outcomes, and ultimately improve customer experience and drive growth.

Optimizing Performance of BI Applications and Workloads – Seven Use Cases

Here are seven examples of financial services BI applications and workloads that Pepperdata big data APM solutions monitor and optimize for performance. Each of these delivers tangible business benefits to the organization.

  1. Predicting the risk of churn for individual customers and recommending proactive retention strategies to improve customer loyalty. Banks and card issuers can identify at-risk customers and respond quickly to retain them.
  2. Providing early warning predictions using liability analysis to identify potential exposures prior to default. This enables proactive engagement with customers to manage their liabilities and limit exposure.
  3. Predicting risk of loan delinquency and recommending proactive maintenance strategies by segmenting delinquent borrowers and identifying “self-cure” customers. With this insight, banks can better tailor collection strategies and improve on-time payment rates.
  4. Detecting financial crime such as fraud, money laundering, or counter-terrorism financing activities by identifying transaction anomalies or suspicious activities using transactional, customer, black-list, and geospatial data.
  5. Predicting operational demand based on historical data and future events. With this insight, banks can anticipate call center traffic volumes or predict demand for cash at ATMs.
  6. Evaluating customer credit risk by analyzing application and customer data for automated real-time credit decisions based on information such as age, income, address, guarantor, loan size, job experience, rating, and transaction history.
  7. Managing customer complaints using data from various interaction channels to understand why customers complain, identify dissatisfied customers, find the root causes of problems, and rapidly respond to affected customers.

The applications and workloads that the Pepperdata big data APM solutions optimize in these analytics and BI use cases provide the “source of truth” that ultimately underlies customer-facing, transactional use cases. For example, banks and card issuers now deploy chatbots that address customer needs and inquiries, walk customers through process steps, provide predictive messages and behavior insights, and automate tasks such as money transfers or balance inquiries. Over time, the behavioral data that chatbots collect is analyzed in the Hadoop cluster to further develop and refine appropriate replies to user requests.

Big Data APM Scalability for Massive Deployments

Pepperdata big data APM solutions provide the scalability that makes them the choice of the world’s largest financial services organizations, with some customers running in excess of 1,000 nodes in their distributed computing environment. Customers with high node counts face unique operational challenges, including extremely high numbers of concurrent queries. They cannot afford any service or data loss. To reduce risks associated with potential downtime and data loss, some organizations have established data centers with triple-redundancy cluster architectures.

Financial services organizations with such huge physical infrastructure investments naturally want to maximize their workloads and utilize their infrastructure as efficiently as possible. For these customers, Pepperdata big data APM solutions automatically optimize infrastructure capacity and application performance to provide:

  • 90% capacity utilization without manual application tuning
  • Up to 50% improvement in throughput that results in significant savings in infrastructure spend
  • 95% reduction in MTTR, with an average 5,200 hours per year saved on triage and troubleshooting time

Bridging the DevOps Communication Gap

Our financial services customers appreciate the ability of Pepperdata big data APM solutions to help bridge the communication gap that can exist between developers and IT operations, a situation that can negatively impact application development and the production workloads.  Using Pepperdata Application Spotlight, customers can readily monitor an app as it transitions through the development cycle from pre-product to production.  As the application evolves, issues like bottlenecks, CPU, and memory mismatches can be quickly detected and resolved using Pepperdata Platform Spotlight and Capacity Optimizer to ensure optimal performance in the production environment. Better communications enable ITOps to help the application team efficiently work through the development transitions. These benefits optimize application performance and uptime and help ensure that SLAs are met.  

We don’t need to explain the significance of ROI to IT Operations leaders in the financial services industry.  At a macro level, profitability is the function of stable and high performing analytics, applications and services that result in customer loyalty and retention.  With an investment in big data APM solutions from Pepperdata, you can bulletproof your foundational analytics applications and workloads and not only avoid application performance issues but also increase revenue and customer satisfaction.

Other Things You Can Do

July 16, 2019

How Pepperdata Big Data APM Delivers ROI by Controlling Cloud Costs

For most big data enterprises, application performance management (APM) is considered an essential element of application-centric IT operations and a DevOps-enabling bridge between production and development on one side, and IT and digital business on the other.  APM strives to detect and diagnose complex application performance problems to maintain an expected level of service, and in doing so, APM can reduce mean time to repair (MTTR), reduce IT maintenance and infrastructure costs, and improve business outcomes.

It’s been said that almost every business now is a software business in some form or another. That means that the reliability and performance of your software applications are critical to your success. From this perspective, APM solutions can deliver a significant return on investment (ROI) if used to their full potential. Strictly speaking, the ROI is the ratio between the net profit of an investment and what it cost to implement it. It is often expressed as a percentage, to represent how much profit was made compared to the costs.

ROI for IT Investments is Different

Traditionally, when IT professionals and executive management discussed the ROI of an IT investment, they were dealing primarily with hardware/infrastructure and mostly thinking of “financial” benefits. Financial benefits include impacts on the organization’s budget and finances, e.g., cost reductions or revenue increases.

With the rise of software-defined everything and cloud-based service offerings, business leaders and technologists also consider the “non-financial” benefits of IT investments, including impacts on operations or mission performance and results, e.g., improved customer satisfaction, better information, shorter cycle-time. These are the so-called “intangibles”, “soft”, or “unquantifiable” benefits of information technology.  Unlike financial returns, there may be no widely-accepted metrics that can be applied. However, IT’s potential for producing positive impacts on business performance is undeniable. Both financial and non-financial benefits must be taken into account to fully assess the value of any technology solution, and APM is no different.

Enabling Big Data DevOps ROI with APM

Large enterprises typically run multi-tiered applications across a variety of systems and platforms. These can range from in-house systems to external clouds. With the accelerating use of cloud-based apps, the complexity of integrating these applications is a challenge for even the most sophisticated IT teams. Greater agility is the underlying business case for a DevOps approach. Leveraging increased automation, DevOps applies agile and lean practices throughout the software lifecycle. It allows IT to launch higher quality applications and deploy them faster than in the past. 

As more organizations discover the efficiencies of adopting DevOps best-practices for application lifecycle management, they quickly realize that APM enables DevOps ROI. Similarly, IT operations teams are recognizing the value of APM to manage expensive cluster resources more efficiently and to better inform DevOps teams who depend on reliable and consistent infrastructure availability and performance.

Align Your Compute Resources and Costs with Actual Service Demands

Pepperdata APM solutions are not only helpful for measuring the performance of your applications and helping to identify opportunities for improvement, they can also deliver more tangible “financial” ROI by reducing your infrastructure and hosting costs through analysis and optimizations.  Applications and IT infrastructure must work together. IT resources represent both capital and operational expenses, putting more pressure on IT organizations to optimize the use of existing resources and acquire new resources only when required. 

Pepperdata Capacity Optimizer is a capacity management solution that aligns IT resources with service demands, optimizing resource utilization, and reducing costs. Capacity Optimizer leverages active resource management features in Hadoop to dynamically tune cluster resources and eliminate inefficiencies and bottlenecks. Running continuously, it improves the capacity utilization of your existing production clusters without manual tuning or intervention. Enterprise deployments typically achieve a 30-50% increase in throughput performance on existing hardware with Capacity Optimizer, enabling them to save thousands of dollars in unnecessary infrastructure and services expenditures.

Only Pay for the Cloud Resources You Need

For organizations migrating to the cloud, Pepperdata Capacity Optimizer provides an even more compelling benefit. It’s easy to forgive IT Ops for over-provisioning on-premises compute resources to avoid an application bottleneck.  On-prem resources represent a sunk cost and are already paid for, so the worst that can happen is an up-tick in chargeback. But taking the same approach to cloud-based resources will yield a nasty surprise in the form of an unexpectedly high monthly bill from your cloud service provider who charges you for every memory and CPU instance that you’ve subscribed to, whether you need and have used those resources or not.

Capacity Optimizer ensures that you are only using the compute resources in the cloud (and on-prem) that you actually need to achieve optimal application performance.  When you assess your priorities for monitoring and managing your technology stack, remember that the only thing that your customers see, and thus the only thing that they care about is the performance of the application they’re using. Whatever may be happening in your big data stack, the application is where the rubber meets the road.  

Fine-tune your big data application environment and achieve tangible ROI with Pepperdata Capacity Optimizer by understanding exactly what CPU and memory resources each application requested, what it needs, what it used, and what it wasted, and identify the true impact on your big data application performance.

July 10, 2019