Managing multi-tenant big data clusters is complex. Pepperdata partners with you to deliver predictable performance, empowered users, managed cost, and managed growth with proven big data APM.
Pepperdata delivers proven big data APM products, operational experience, and deep expertise.
PHILIPS WELLCENTIVE
“Before Pepperdata, we experimented with various approaches to solve our big data performance issues, but we couldn’t see deep enough into our Hadoop cluster. Platform Spotlight shined a bright light into our Hadoop cluster and provided detailed data that helped us isolate and resolve the problem.”
RUBICON PROJECT
“At Rubicon Project, having the appropriate visibility and insight into our big data applications is extremely important when delivering detailed reports to our clients and meeting our SLA. We challenged Pepperdata to find a solution to profile our applications before going to production that would help us maintain our SLA to our customers as we introduce new applications. Pepperdata listened to us and quickly understood the problem we were trying to address.”
CHARTBOOST
“Chartboost is the world’s largest mobile games-only advertising platform, reaching one billion active players around the world every month. Chartboost utilizes Apache Spark on large Amazon EC2 Hadoop clusters for machine learning and ETL workflows. Understanding Spark application performance in these complex environments is always a challenge. As a current user of Pepperdata Platform Spotlight, it has been great to work with Pepperdata on the development of the Application Spotlight self-service portal software. It will give us a comprehensive insight into Spark jobs.”
EXPEDIA
“The level of support and expertise that we receive from the Pepperdata team made a big difference to us. Pepperdata worked closely with us on our Platform Spotlight implementation to ensure success on our big data cluster. With Pepperdata capacity optimization, the DevOps team runs more jobs, faster. We’ve seen a big performance boost across the cluster and have a much more efficient data footprint. Using the Pepperdata dashboard to see application-level metrics, unique custom views, resource utilization per workload drill-downs, and hardware utilization by various workgroups has significantly improved the way that we manage and troubleshoot big data cluster performance issues.”
See who’s using Pepperdata big data APM solutions to achieve predictable performance, empower users, and to manage cost and growth for their big data investment.
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.
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 clusters.
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:
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.
###
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.
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:
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
Pepperdata Application Spotlight Free for Enterprises with a Single Cluster with Up to 20 Nodes
NEW YORK — January 8, 2019 — Pepperdata, the leader in big data Application Performance Management (APM), today announced a free version of Application Spotlight™ for enterprises for use in a single cluster with up to 20 nodes.
Pepperdata Application Spotlight is a self-service APM solution that provides developers with a holistic and real-time view of their applications in the context of the entire big data cluster, allowing them to quickly identify and fix problems (failed Spark applications, for instance) to improve application runtime, predictability, performance and efficiency. Pepperdata Application Spotlight also provides automatic tuning for applications, delivers job-specific recommendations, and enables users to set up alerts on specific behaviors and outcomes to avoid the risk of failure.
“This free offering gives developers a powerful introduction to the robust capabilities of Pepperdata,” said Ash Munshi, Pepperdata CEO. “Application Spotlight gives them self-service access to all the data on their applications in one place so they can determine whether performance issues were caused by their application or other applications running on the cluster.”
With Pepperdata Application Spotlight, developers can:
Availability
Application Spotlight is available immediately for free for use in a single cluster with up to 20 nodes by visiting www.pepperdata.com/free-enterprise-apm.
About Pepperdata
Pepperdata (https://pepperdata.com) is the leader in big data Application Performance Management 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. Other names may be trademarks of their respective owners.
In February, we wrote about retail big data use cases, and in January, we looked at the digital transformation in healthcare. While retail and healthcare are two industries that leverage big data in big ways, no industry compares to banking for the amount of data collected.
IDC forecasts worldwide revenues for big data and business analytics (BDA) solutions will reach $260 billion in 2022. According to the Worldwide Semiannual Big Data and Analytics Spending Guide, banking spends the most in big data and business analytics solutions, not surprisingly, along with discrete manufacturing, process manufacturing, professional services, and government. Additionally, financial services is one of the industries delivering the fastest BDA revenue growth, along with retail and professional services.
Let’s face it. Big data provides consumers and businesses with insights that improve outcomes. Customers can make faster and more informed purchase decisions, right from their mobile devices. Because customers get instant access to new features and services almost daily, financial organizations must create and market products that hit the mark faster. Insights derived from big data increase the success rate of these exercises by showing the most useful services, the most engaging products, and what the purchase trends are by demographics. Banks can maximize performance and improve customer satisfaction and retention by delivering better researched and more personalized services.
Here are three important ways that leading banks leverage big data analytics and improve the bottom line.
Fraud Detection
The financial industry is constantly targeted by cyber criminals. it’s crucial that banks adapt with increasingly effective security. According to Innovation Enterprise, ransomware attacks reached a new high in the last two years and crypto attacks rose 44% in 2018. Banks are obligated to guarantee customers the highest level of security and machine learning applied to big data has proven to be an effective way to catch fraudsters. Examples include flagging unusual purchases or cash withdrawals from a location outside of a customer’s normal range, triggering a hold on the account or a call from the bank to verify the behavior is legitimate.
Data breaches are not going to end anytime soon and according to an OpenAccessGovernment.org article, financial organizations must leverage AI and machine learning to analyze data across devices, applications, and transactions. As the article argues, “taking a risk-based analytics approach, organizations can detect complex fraud patterns that are difficult for analysts to manually identify.”
Improving Customer Satisfaction
Real-time analytics on customer big data enables banks to deliver highly personalized services that leverage insights derived from behavior, financial history, social media and feedback data. This information can also be used to generate valuable reports that banks can leverage when planning new products and services. Data science teams can also study behavior to discover exactly when and where customers need the most advice or help, and provide them with better services based on spending habits, social-demographic trends, location, and other preferences.
Sentiment Analysis
Consumers have an increasing number of avenues to log satisfaction and complaints and provide feedback. Social media and review sites provide valuable data to analyze customer sentiment and quickly and effectively respond to problems. By tracking and analyzing this data, financial organizations will better understand their customers as well as the banking products they need and want.
Fortify Big Data for Financial Use Cases
To ensure infrastructure availability for big data analytics, financial organizations must ensure their infrastructures are performing reliably. These organizations require a performance management solution that monitors the entire environment, from the applications to all of the hardware resources.
Pepperdata provides Fortune 100 banks with its combined application performance management (APM) and infrastructure performance management (IPM) to help these customers save millions by optimizing resource capacity, ensure peak efficiency for analytics applications, and reduce MTTR by up to 90%. With our proven performance management solutions, operational experience on multi-tenancy clusters, and deep expertise, find out how Pepperdata can help you achieve big data success. Contact sales@pepperdata.com.
More info:
Cloud migration has passed the tipping point, with nearly 60% of North American enterprises now relying on public cloud platforms, five times the percentage that did just five years ago. According to research by IBM’s Institute for Business Value, 98 percent of surveyed organizations plan to use multiple hybrid clouds by 2021, another sign of tremendous cloud momentum.
Despite this trend, migration to the cloud and the transformation to a hybrid multi-cloud IT operating environment is often viewed with anticipation and anxiety, and perceived as complicated. Most companies require an approach that ensures that their data migration is smooth and fast, putting pressure on the IT team managing the process. No matter how much data you want to move, there are many ways (some of them quite surprising) to actually transfer it to your preferred cloud service provide (CSP). As AWS notes, one should never underestimate the bandwidth of a semi truck filled with disks hurtling down the highway.
Leading CSPs AWS, Azure, Google and IBM each provide data migration services, and they assume that you have already decided to move to the cloud, you’re ready to transfer your data, and you have chosen your CSP. Each of them has carefully refined the process of getting your data from your on-prem servers to their cloud servers. After all, successful migration is a critical component of their customer acquisition strategies.
However, before you sign up with any service provider to migrate to the cloud and all the features that come with it — like a multi-tier read-write cache, deduplication, pre-fetching, asynchronous write-back, and network optimizations — you must first conduct a thorough independent assessment of your existing on-prem workloads. This will save the IT operations team significant time and provide accurate cost projections.
To begin, each of your workloads must be carefully matched to an appropriate cloud instance. A pre-migration workload assessment should automatically analyze a CSP’s general purpose, memory-optimized, and CPU-optimized instance types and identify which is the most cost-effective for each of your workloads. Identifying the right instances for a single workload from a single CSP can be like navigating a maze. Check out this comparison of Amazon EC2 instances and you’ll see what I mean.
In addition, your actual CPU and memory requirements must be analyzed by workload to determine baseline and burst characteristics for mapping to the appropriate static or on-demand instances. This is where a cloud migration cost assessment that analyzes down to the workload really shines because the cost differential between static and on-demand instances can be quite significant. Static or “reserved” instances are more expensive because the associated memory and CPU resources in the cloud are dedicated. These are ideal for applications that have steady state or predictable usage.
On-demand instances make a best effort to provide resources when needed. Applications with short-term, spiky, or unpredictable workloads that cannot be interrupted are a good match for on-demand instances which increase or decrease your compute capacity depending on the demands of the application. The cost benefit is that you only pay for what you use. So you can see now that understanding your individual workload profiles and mapping those to the optimal cloud instances can result in measurable economic benefits.
It would be remiss of me to not mention the application performance benefits that detailed workload mapping provides. When conducting a cloud migration cost assessment, it’s essential to calculate the cloud CPU and memory needed to achieve the same performance and SLAs that are delivered by your existing on-prem infrastructure. Missing this critical step can result in application performance failure and loss of revenue, depending on the service the application supports.
CSPs generally make it easy for you to map your on-prem VM to a cloud VM, but fail to adequately profile and analyze each of your workloads before you commit to their services. Without that detailed analysis, it’s impossible to select the best instance options for each workload and accurately calculate cloud costs. That’s where the free Pepperdata Cloud Migration Cost Assessment comes into play.
FREE ASSESSMENT
Pepperdata can automatically analyze and profile every workload in your cluster to accurately determine your projected cloud costs, and provide the most appropriate instance recommendations for workloads, queues, jobs, and users…before you commit to a cloud service provider. We also map your big data workloads to various instance types to meet SLA requirements and enable you to compare services and costs across AWS, Azure, Google Cloud, and IBM Cloud.
Use our Big Data Cloud Migration Assessment to get a detailed understanding of your on-prem workloads, with recommended instance options and cloud cost projections before you commit. You’ll be equipped to make better-informed, fact-based cloud migration decisions based on the most comprehensive workload performance profiles available – saving you significant time and expense.
Nearly five years ago, in June 2014, Google open-sourced Kubernetes (k8s), the container orchestration platform based on software that manages the hundreds of thousands of servers that run Google. Kubernetes is based on a project called Borg that was originally developed internally at Google. Kubernetes not only beat Apache Mesos and Docker in the container orchestration race, but it has also become the hottest open source technology to emerge since the Linux operating system that commoditized enterprise UNIX operating systems and became a ubiquitous technology platform. The question now is how rapidly will it become the dominant way for enterprises to develop and deploy applications?
The four pillars of big data – volume, variety, velocity, and veracity – are not showing any signs of breaking down — on the contrary, they are stronger than ever. However, the realities of the underlying technology have changed, and with them, the architectures and the economics. Hadoop was built in a compute era with different fundamental assumptions than those that exist today. Network latency was a major bottleneck, and cloud storage was not a competitive option because of memory cost. Most data was located on-premises and co-located with the computing function. Today, network latency is not a critical issue for cloud providers, the cost of memory has plummeted, there are many cloud service providers to choose from, and hybrid cloud architectures are becoming the norm for many large enterprises.
Applications that generate and use data need to be deployed in multi-cloud and hybrid cloud environments seamlessly. This is where containers — and Kubernetes – enter the picture. Application delivery on Kubernetes starts by building applications as a set of microservices in a container-based, cloud-native architecture. Kubernetes is the product of an ongoing realignment of the software resources that comprise a network application. That alignment is centered around a concept called a workload – a job performed by one or more applications, or one or more services, across a multitude of processors.
There is nothing structurally unique that distinguishes Kubernetes from any other type of application. Its orchestrator runs on an operating system. When running, it maintains a cluster of nodes, which are servers that may be physical or virtual. On each of these nodes are pods of containers. And within each container is a client-side agent called the kubelet, which manages functions independently on behalf of the orchestrator, for the node to which it’s assigned.
Here are three reasons why Kubernetes and container orchestration are achieving an increasingly wider appeal to enterprises.
When an application is comprised of granular components, it becomes much easier to evolve that application granularly by updating and improving those components individually. The orchestrator can make appropriate adjustments in response to how those individual changes impact the workload as a whole. Feature improvements to applications don’t have to be implemented in massive overhauls, which can sometimes negatively impact their usability. The concept of continuous integration and continuous delivery (CI/CD) can be much more easily automated by a platform that’s designed from the outset to support deployment in smaller, more manageable steps.
Kubernetes maintains active replicas of container groups, called replica sets, for the purpose of maintaining uptime and responsiveness in the event that any container or container grouping (or pod) fails. For example, a data center does not have to replicate the entire application and trigger a load balancer to switch over to the secondary application if the primary one fails. Multiple pods in a replica set are typically running at any one time, and the orchestrator’s job is to maintain that redundancy throughout the lifespan of the application.
The key value for organizations that orchestrate distributed workloads using Kubernetes is the built-in ability for workloads to multiply through the system as necessary, and to scale up and back down again according to established policies. To minimize the potential for problems, Kubernetes groups related containers together as pods. A service called the autoscaler can be set to automatically replicate pods to different nodes when it determines that resources allocated to those pods are not being utilized as much as they could be.
By winning the container battle, Kubernetes has made life easier for everyone. Now the industry can innovate on Kubernetes as the de facto standard for container orchestration. Kubernetes is now the foundation for the new generation of artificial intelligence, machine learning, data management, and distributed storage in cloud-native environments. But K8s still has a way to go to support stateful big data applications that require persistent volume support, automated multi-tenancy support, and enterprise-grade security. Stay tuned!
