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

Serialization and Its Role in Spark Performance

Apache Spark™ is a unified analytics engine for large-scale data processing. It is known for running workloads 100x faster than other methods, due to the improved implementation of MapReduce, that focuses on keeping data in memory instead of persisting data on disk. 

However, despite its many great benefits, Spark also comes with unique issues, one of these being serialization. What is the best way to deal with this? In our webinar, Pepperdata Field Engineer Alexander Pierce took on this question.

The Problem with Serialization

This plays an important role in the performance of any distributed application. Formats that are slow to serialize objects into, or those that consume a large number of bytes, will greatly slow down the computation. 

“Serialization is fairly important when you’re dealing with distributed applications,“ Alex explains. “Because you’ll have to distribute your code for running and your data for execution, you need to make sure that your programs can both serialize, deserialize, and send objects across the wire quickly.” Often, this will be the first thing you should tune to optimize a Spark application. The Java default serializer has very mediocre performance with respect to runtime, as well as the size of its results. Alex recommends the use of the Kryo serializer.

Watch our webinar to learn more about tackling the many challenges with Spark. Understand how to improve the usability and supportability of Spark in your projects and successfully overcome common challenges.

Or… if you want to skip ahead to the ‘good stuff’ and see how Pepperdata takes care of these challenges for you, start your free trial now!

What to Expect in Big Data for 2020

Gartner recently performed a deep dive into the top trends that will define the world of data through 2020 and beyond. Anyone working in big data – and all Pepperdata users – should be very interested in what they have to say. What does this coming year hold for the world of big data analytics? Here’s what Gartner had to say. 

Augmented Analytics

By 2020, augmented analytics will be a dominant driver in the big data industry.  Augmented analytics automates the discovery of insights, and makes it available to businesses for optimal decision making. While augmented analytics is fast and powerful, it requires increased data literacy across the organization to be successful. 

Augmented Data Management

This trend impacts all of enterprise data management: databases, data quality, metadata management, data integration, and metadata management. With the exponential growth of data today, vendors will need to leverage the capabilities of AI and machine learning to automate data management processes.

Blockchain in Data and Analytics

Blockchain technologies are still only used in cryptocurrency systems, and they have yet to mature to production level scalability. But this technology could potentially address two data and analytics challenges: providing lineage of assets and transactions, and providing transparency for complex networks of participants.

Commercial AI and Machine Learning

The increased use of commercial artificial intelligence (AI) and machine learning (ML) will accelerate production deployments and drive more business value. Currently, open-source platforms have become the primary source of innovation in algorithms and dev environments for AI and ML. But commercial vendors, though slow to respond to the trend at the start, are now offering enterprise features necessary to scale both. In 2020, automation frameworks will allow data scientists to create their own data pipelines that are close to production-ready.

Continuous Intelligence

Gartner predicts that more than half of major business systems will incorporate continuous intelligence by 2022. This has long been sought by organizations, and now it is finally practical to implement such systems because of advancements in the cloud, streaming software, and the Internet of Things (IoT).

Data Fabric

A data fabric is a custom-made design orchestrating a combination of data integration approaches to provide reusable data services, pipelines, semantic tiers, or APIs. Deriving value from analytics investments is fairly easy when you have such frictionless data sharing and access.

Graph Analytics

Gartner predicts that the application of graph processing and databases will double annually over the next few years, accelerating data preparation and enabling more complex and adaptive data science. 

Natural language processing (NLP) and Conversational Analytics

By 2021, this trend will boost analytics and business intelligence adoption from 35% to 50% for employees. Business people now have an easier way to ask questions and get data and insights with NLP, while conversational analytics enables the verbal communication of such questions.

Persistent Memory Servers

This trend willhelp businesses extract more practical and actionable insights from their data. Currently, most database management systems (DBMS) use in-memory database structures. However, new server workloads demand faster processing, faster storage, and massive memory. Although it may take years to modify current software to take advantage of this trend, DBMS vendors are now experimenting with persistent memory. 

To find out more about these upcoming data trends, check out Gartner’s official top 10 list here. Or if you want to see in action how Pepperdata keeps pace with these trends, request a demo here.

2019: The Year in Pepperdata

2019 was a turbulent and exciting year in the world of Big Data. And here at Pepperdata, we’ve been right in the thick of it. Before we leave 2019 behind, we wanted to look back on the awesome numbers we’ve posted this year.

We couldn’t have put up these numbers without the support and trust of our customers. So if you’re part of the Pepperdata clan: thank you! and clients, and for that, we are extremely grateful.

Expect more from Pepperdata next year, as we are continually improving our products and developing more features for even more efficient big data analytics management.