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

• For more information on the offering, email sales@cloudwick.com or sales@pepperdata.com.
• Learn about Pepperdata’s free Big Data Cloud Migration Cost Assessment.
• Start a free Big Data Cloud Migration Cost Assessment.
• Pepperdata website: https://pepperdata.com

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.

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 Big Data Cloud Migration Assessment: https://www.pepperdata.com/big-data-cloud-migration
• Download a sample: https://www.pepperdata.com/big-data-cloud-migration-cost-assessment-sample
• Start an Assessment: https://www.pepperdata.com/free-big-data-cloud-migration-cost-assessment

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.

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

How Pepperdata Optimized Hadoop and Helped a Leading Online Travel Agency Save Millions

One of the world’s largest Online Travel Agencies (OTA) uses Pepperdata application and infrastructure performance management solutions to continuously optimize their Hadoop-based distributed computing environment. With more than 650 nodes, the customer’s installation ranks as one of the larger Hadoop clusters in existence.  Our customer relies on this big data platform to deliver reliable and timely predictive marketing analytics. Thousands of airline, hotel, and other travel service partners leverage the massive amount of real-time data that is captured and analyzed by our customer, enabling them to offer their users the best possible recommendations on accommodation, airfares, car rentals and more.

OTAs are third-party booking websites that offer travelers an easy-to-search database of travel services and providers. Most travel and hospitality providers offer their inventory via OTAs, giving the consumer a one-stop-shop for comparative shopping. Within the travel industry segment, online travel bookings are growing twice as fast as the overall market.

Market Growth Rewards the Business But Challenges IT Ops

While the steadily increasing OTA market share is a boon to our customer’s business, it also has the effect of increasing workloads and generating infrastructure capacity challenges for their IT Operations team. Our customer’s technology director is responsible for the performance and reliability of the Hadoop platform and ensuring that data renders on time and delivers accurate marketing predictions that can directly impact daily revenues.

Our customer was faced with distributed computing infrastructure slowdowns because of limited compute (CPU and memory) resources. Jobs were running long, because while storage on the cluster wasn’t an issue, computing was.  After researching solutions to address the compute resource issues, our customer realized that the only products that could help them optimize their under-utilized Hadoop cluster resources were Platform Spotlight and Capacity Optimizer from Pepperdata.  By deploying these solutions, our customer was able to reclaim wasted CPU and memory and immediately gain a compute resource lift of 30%.

To provide resource optimization, performance recommendations and alerts, as well as complete visibility into the customer’s Hadoop cluster, Pepperdata collects more than 350 real-time operational metrics from applications and infrastructure resources every five seconds. These include CPU, RAM, disk I/O, and network usage metrics on every job, task, user, host, workflow, and queue. That’s hundreds of millions of data points collected every hour. 

Big Win – Continuous Resource Optimization to Meet SLAs

“We immediately had a major win with Pepperdata. They saved us millions of dollars on hardware,” said the customer’s technology director. “And by continuously optimizing our existing Hadoop cluster resources we mitigate the risk of SLAs not being met, which has a huge impact on our business. If SLAs are not met, business is lost. With Pepperdata, we were also able to decommission an entire cluster and bring those users onto our primary cluster, reducing our footprint and simplifying overall infrastructure management.”

All telemetry data collected by Pepperdata is correlated and powers the Platform Spotlight performance dashboard.  This data enables the OTA’s IT Ops team to quickly diagnose cluster performance issues, make resource decisions based on user priorities and needs, and identify applications that are at risk of failing.

“We have thousands of jobs running and every single one of those jobs affects the queue, impacting SLAs and the bottom line,” said the technology director. “Pepperdata helps us prioritize job type or specific users to ensure that high-SLA apps have the resources they need and we immediately receive alerts from the dashboard when a potential problem arises.”

Faster Time to Root Cause

The customer highly values the ability to quickly determine the root cause. “Pepperdata makes it easier and faster to troubleshoot issues. That’s important. When a job is running, it hits hundreds of nodes. Pinpointing where the problem is would be impossible without Pepperdata monitoring every single data point. We’d have to download and analyze logs, which would take hours and wouldn’t even help us find the issue. Pepperdata helps us quickly find and immediately resolve issues.”

Pepperdata Capacity Optimizer saved the Online Travel Agency millions upon deployment by eliminating unnecessary expenditures on hardware and continues to provide value by continuously optimizing their application infrastructure.

Capacity Optimizer – Doing More with Less

Pepperdata Capacity Optimizer, a key component of Platform Spotlight, dynamically tunes hardware resource usage in Hadoop clusters to eliminate inefficiencies and bottlenecks. It monitors Hadoop and Spark applications and infrastructure in real-time and, leveraging AI with active resource management, identifies where more work can be done… enabling tasks to be added to servers with available resources. By recapturing under-utilized compute resources in a cluster, Capacity Optimizer enables organizations to run more workloads on existing hardware.

Tangible Benefits for DevOps

• Millions of dollars in savings with an immediate 30% resource lift, by eliminating unnecessary hardware expenditures
• More efficient use of existing hardware resources while maintaining business-critical SLAs
• Simplified infrastructure management for IT Ops as a result of reducing the overall Hadoop cluster footprint
• Empowerment of development teams with self-service access to Hadoop application performance data that provides insight into the individual job and workload performance issues

Learn More

• Read the OTA Case Study: Leading Global Travel Services Company Optimizes Capacity, Saves Millions with Pepperdata
• Use Hadoop Application Performance Management for FREE on up to twenty nodes.
• Request a Trial

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.

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

• View Pepperdata Big Data APM Solutions
  • Platform Spotlight
  • Application Spotlight
  • Capacity Optimizer
  • Free Big Data APM on up to Twenty Nodes
  • Free Cloud Migration Cost Assessment