Name: Strata Data Conference NYC –Pepperdata –HDFS on Kubernetes: Lessons Learned
Start: 2017-09-28T16:05:00Z
End: 2017-09-28T16:05:40.000Z
Location: BrightTALK

Pepperdata Capacity Optimizer delivers 30-47% greater cost savings for data-intensive workloads, eliminating the need for manual tuning by optimizing CPU and memory in real time with no application changes. Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing utilization, and freeing developers from manual tuning to focus on innovation.

You’ve migrated your workloads to Amazon EMR, and you want to optimize the environment and do more for less. With cloud cost optimization critical in establishing an innovative big data framework, AWS and Pepperdata gathered multiple experts to discuss how to maximize your savings with Amazon EMR and Pepperdata. In this webinar, you will learn:
- AWS customer best practices to optimize your Amazon EMR utilization 
- How Autodesk, a global leader offering design and manufacturing software, lowered operational expenses while enhancing its Amazon EMR scalability and reliability.
- How implementing Pepperdata Capacity Optimizer can help you see an immediate cost reduction in your Amazon EMR infrastructure bill while maintaining performance SLAs. 

Webinar Speakers:
Kiran Guduguntla, AWS Worldwide Business Leader for Amazon EMR 
Kirk Lewis, Pepperdata Senior Solutions Architect
and special guest Mark Kidwell, Autodesk Chief Data Architect, Data Platforms and Services 

Who Should Attend:
CIO, CFO, VP of IT, FinOps Engineer, Platform Engineer, IT Ops, Cloud Architect, Data Platform, Big Data Architect

Maximize Your Savings: How Autodesk Optimized its Amazon EMR with Pepperdata

Containers have revolutionized application development and deployment, but optimizing Kubernetes cloud costs and achieving better performance is nearly impossible without proper visibility, a thorough understanding of how to optimize your container environment, and automation. Capacity management is a critical and required discipline for companies that want to run reliable and efficient cloud-native infrastructure. 
 
Join Pepperdata Engineers Alex Pierce and Shekhar Gupta for a discussion on reducing your overall cloud spend with Kubernetes and Pepperdata Capacity Optimizer. Learn how to achieve automatic cost savings through real-world examples and the following best practices:

- Automatic optimization
- Efficiently managing capacity by identifying mismanaged resources 
- Eliminating waste and recapturing unused capacity
- Observability, alerts, and job-specific recommendations for Spark

Reduce Your Cloud Spend on Kubernetes with Pepperdata Capacity Optimizer

When cloud costs start to spiral out of control, how do you make sense of it? How do you optimize your big data performance and manage runaway cloud costs? And finally, how do you ensure you’re meeting the needs of your cloud users? Not having the answers to these questions can be a nightmare.

As a big data architect, you’re responsible for quickly resolving complex issues, improving platform performance and efficiency, managing a smooth cloud migration, determining new technology adoptions, and reducing cloud costs.

Face and eliminate your worst big data nightmare with Pepperdata Field Engineer Kirk Lewis as he presents best practices for automatic optimization, cloud efficiency, and cost optimization. Topics include:

- Optimizing your modern big data architecture
- Delivering deep insight from across more data sources and types
- Managing the increased velocity of analytics requests, across the cloud and on premises
- Controlling costs and resources while supporting more users who want self-service capabilities
- Meeting the needs of data scientists and data analysts

When Cloud Costs Run Amok: Big Data Architect’s Worst Nightmare

There is growing interest in running Spark natively on Kubernetes. Spark applications often access data in HDFS, and Spark supports HDFS locality by scheduling tasks on nodes that have the task input data on their local disks. Kimoon Kim demonstrates how to run HDFS inside Kubernetes to speed up Spark.

Strata Data Conference NYC –Pepperdata –HDFS on Kubernetes: Lessons Learned

kubernetes

HDFS

Docker

Containers

Cloud

DevOps

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Strata Data Conference NYC –Pepperdata –HDFS on Kubernetes: Lessons Learned

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