OPTIMIZE PERFORMANCE FOR YOUR ENTIRE BIG DATA STACK

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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
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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

The Top Five Trends We Observed at Strata Data Conference 2019

The Pepperdata team had a fantastic time at this year’s Strata Data Conference in New York City. All of us are still buzzing from all the discussion of cutting-edge data strategies, techniques, and technologies. 

Here are the top five trends that kept coming up:

1. Big Data Applications are the Future

Everyone at Strata agreed: Big data applications are growing faster and have more impact on the business than ever before. For example, for financial services, the primary big data use case 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.

2. The Cloud Continues to Rise

According to Gartner, this year, the worldwide public cloud service market will reach nearly $222 billion, growing 16% over 2018. Our conversations at Strata confirmed this ongoing rise. Almost everyone we spoke to was either in the cloud, or were actively evaluating cloud options. Most customers said they were intending to maintain their on-premise clusters, but were looking to move many new workloads to the cloud. 

3. Hadoop Is Alive And Well

At Strata, we saw that companies are continuing to grow and expand their Hadoop implementations. There continues to be a strong push towards leveraging open-source standards and technologies, and moving away from proprietary, vendor-controlled technologies. Hadoop is much more than just Hadoop, with YARN, Spark, Kubernetes, and many more open-source technologies comprising its ecosystem. Far from being dead, Hadoop provides a modern data architecture for managing and analyzing data that customers continue to embrace as a foundational technology.  

4. Enterprises Are Struggling With Visibility 

Enterprise companies are struggling to gain visibility into their big data clusters, and struggling to know how they can better utilize their resources. Often, this can be a risk to a company’s overall data strategy. To optimize data operations across the full stack, you must be able to simultaneously manage everything: Hadoop, EMR, HD Insights, Spark, HDFS, S3, and more. On-prem, in the cloud or in hybrid environments. That’s a tall order but very possible today.

5. IT Operations is Seeking Next-Generation Capabilities to Predict and Manage Costs as it Matures Across the Enterprise

IT operations isn’t just tasked with understanding, explaining, and resolving problems in enterprise applications. The next wave of issues is already here: producing financially modeled cost projections and implementing chargeback methodologies which are needed to enhance savings and provide predictable spending across the IT landscape. IT complexity includes financial risk, which requires a more sophisticated approach to supporting multi-tenancy while centralizing IT, and maintaining accountability to the end users of IT services. IT operations is seeking solutions to these new challenges and the race is on to address this area of concern.

Attending the Strata Data Conference was a great experience. There was a variety of people representing many different companies and industries, all of them discussing the future of data in interesting ways. 

We’d love to hear your thoughts on the trends we’ve mentioned here. Did you notice any trends that we may have left out? Want to discuss more about one of the ones mentioned above? Use the hashtag #StrataTrends and tweet us @pepperdata to share your thoughts.

 

October 9, 2019

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

 

September 3, 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