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CNDM Day is a free, virtual event that attracts CXOs, IT managers, System Administrators and Architects, and others interested in learning how to optimize data protection and management in Kubernetes.
CNDM Day is a free, virtual event that attracts CXOs, IT managers, System Administrators and Architects, and others interested in learning how to optimize data protection and management in Kubernetes.
Welcome: Michael Cade @MichaelCade1, Senior Technologist, Product Strategy, Kasten by Veeam
Speaker: @bhavin04890 Bhavin Shah, Technical Marketing Manager @Portwx @PureStorage
Abstract: Amazon EKS is one of the most widely used Kubernetes distributions, accounting for over 65% of container deployments. But, designing a robust solution using Amazon EKS clusters, such that there aren’t any single points of failure, is difficult. Join us for this session and we will talk about how you can architect a solution using Amazon EKS and Portworx that helps you avoid application downtime and handle failure events like Node failures, Availability Zone failures, Region failures, cluster failures, etc.
Speakers: Tim Myers @timtech4real, COTO & Geoff Burke @CloudRestore, Senior Cloud Solutions Architect @Tsunatiinc
Abstract: In this presentation we will recount the transition from providing traditional data protection to Container and Kubernetes backup.
What are differences?
What surprised us?
What are the gotchas?
What should a backup Administrator do to remain relevant in the brave new world of container data protection?
We will also perform a brief comparison of two backup jobs. The first being a traditional VM backup, the second a Kubernetes backup. This will explain the different approaches to data protection, and also help answer the question of why we need a new form of backup for container workloads.
Speakers: Randy Kerns, Senior Strategist, Janae Stow Lee, Consulting Analyst, & Camberley Bates, Managing Director/Analyst @evaluator_group
Abstract: On the past year Evaluator Group has engaged with IT Pros on Container Management Platform technology decisions. In this session we will discuss the challenges and issues the IT operations are facing, including staffing and changing roles. We will also discuss how different business drivers impact the decisions being made at the executive level.
Speakers: Audrey Reznik Guidera, Sr Principal Software Engineer & Troy Nelson, Software Engineer, @RedHat
Abstract: As a Data Scientist it is often difficult to know where to start your project. Do I test and build my model in an IDE? If I build and test my model in an IDE, how do I connect to my resources such as streaming data or databases? How can I optimize my model inference performance? Once I have optimized my model how do I easily deploy and manage it?
For a data scientist who just wants to build their model these are questions that they really don’t want to think about. Data Scientists just want to be able to sit down and write their algorithms. Is there a way to make the model development/interaction easier for Data Scientists? Yes there is! And that is through the use of managed services & applications available in the Red Hat OpenShift Data Science platform!
In this session we will discuss a painless way to build, test, train and deploy models using the Red Hat OpenShift Data Science platform. In particular we will cover:
Speaker: @bhavin04890 Bhavin Shah, Technical Marketing Manager @Portwx @PureStorage
Abstract: Amazon EKS is one of the most widely used Kubernetes distributions, accounting for over 65% of container deployments. But, designing a robust solution using Amazon EKS clusters, such that there aren’t any single points of failure, is difficult.
Join us for this session and we will talk about how you can architect a solution using Amazon EKS and Portworx that helps you avoid application downtime and handle failure events like Node failures, Availability Zone failures, Region failures, cluster failures, etc.
Speakers: Tim Myers @timtech4real, COTO & Geoff Burke @CloudRestore, Senior Cloud Solutions Architect @Tsunatiinc
Abstract: In this presentation we will recount the transition from providing traditional data protection to Container and Kubernetes backup.
What are differences?
What surprised us?
What are the gotchas?
What should a backup Administrator do to remain relevant in the brave new world of container data protection?
We will also perform a brief comparison of two backup jobs. The first being a traditional VM backup, the second a Kubernetes backup. This will explain the different approaches to data protection, and also help answer the question of why we need a new form of backup for container workloads.
Speaker: @sh0dan Klaus Post, Engineer @MinIO
This talk includes a discussion of the importance of small object support for AI/ML/DL and analytics workloads and how MinIO’s architecture makes it uniquely suited to excel where other object storage typically fails – housing a data lake of log files and streaming data.
We will discuss recent benchmarks demonstrating MinIO’s capacity for high IOPS and high throughput on small objects. We will conclude with a demo .tar and ZIP file support in an ML pipeline.
Speakers: Randy Kerns, Senior Strategist, Janae Stow Lee, Consulting Analyst, & Camberley Bates, Managing Director/Analyst @evaluator_group
Abstract: On the past year Evaluator Group has engaged with IT Pros on Container Management Platform technology decisions. In this session we will discuss the challenges and issues the IT operations are facing, including staffing and changing roles. We will also discuss how different business drivers impact the decisions being made at the executive level.
Speakers: Audrey Reznik Guidera, Sr Principal Software Engineer & Troy Nelson, Software Engineer, @RedHat
Abstract: As a Data Scientist it is often difficult to know where to start your project. Do I test and build my model in an IDE? If I build and test my model in an IDE, how do I connect to my resources such as streaming data or databases? How can I optimize my model inference performance? Once I have optimized my model how do I easily deploy and manage it?
For a data scientist who just wants to build their model these are questions that they really don’t want to think about. Data Scientists just want to be able to sit down and write their algorithms. Is there a way to make the model development/interaction easier for Data Scientists? Yes there is! And that is through the use of managed services & applications available in the Red Hat OpenShift Data Science platform!
In this session we will discuss a painless way to build, test, train and deploy models using the Red Hat OpenShift Data Science platform. In particular we will cover:
Our upcoming Cloud Native Data Management Day Tales & Teachings is 100% virtual and takes place Tuesday March 29th beginning at 9AM PT. Join us for great stories and lessons learned on all things data and storage with everything in between! Interested in being a speaker? Submit an abstract today. https://cndmday.com/abstract-form/ Become a member and receive notifications of all our events: https://cndmday.com/
AGENDA
Welcome: Michael Cade @MichaelCade1, Senior Technologist, Product Strategy, Kasten by Veeam
Title: How to avoid any single point of failure in your Amazon EKS clusters
Speaker: @bhavin04890 Bhavin Shah, Technical Marketing Manager @Portwx @PureStorage
Abstract: Amazon EKS is one of the most widely used Kubernetes distributions, accounting for over 65% of container deployments. But, designing a robust solution using Amazon EKS clusters, such that there aren’t any single points of failure, is difficult. Join us for this session and we will talk about how you can architect a solution using Amazon EKS and Portworx that helps you avoid application downtime and handle failure events like Node failures, Availability Zone failures, Region failures, cluster failures, etc.
Title: We backed up virtual Machines and Physical computers for years, and then Containers came along.
Speakers: Tim Myers @timtech4real, COTO & Geoff Burke @CloudRestore, Senior Cloud Solutions Architect @Tsunatiinc
Abstract: In this presentation we will recount the transition from providing traditional data protection to Container and Kubernetes backup.
What are differences?
What surprised us?
What are the gotchas?
What should a backup Administrator do to remain relevant in the brave new world of container data protection?
We will also perform a brief comparison of two backup jobs. The first being a traditional VM backup, the second a Kubernetes backup. This will explain the different approaches to data protection, and also help answer the question of why we need a new form of backup for container workloads.
Title: Improving the Data Lake Experience by Optimizing Small Object Storage and Retrieval
Speaker: Klaus Post @sh0dan, Engineer, @MinIO
Abstract: This talk includes a discussion of the importance of small object support for AI/ML/DL and analytics workloads and how MinIO’s architecture makes it uniquely suited to excel where other object storage typically fails – housing a data lake of log files and streaming data. Speaker will discuss recent benchmarks demonstrating MinIO’s capacity for high IOPS and high throughput on small objects, and will conclude with a demo .tar and ZIP file support in an ML pipeline.
Title: State of IT adoption of Containers -Perspectives from Evaluator Group
Speakers: Randy Kerns, Senior Strategist, Janae Stow Lee, Consulting Analyst, & Camberley Bates, Managing Director/Analyst @evaluator_group
Abstract: On the past year Evaluator Group has engaged with IT Pros on Container Management Platform technology decisions. In this session we will discuss the challenges and issues the IT operations are facing, including staffing and changing roles. We will also discuss how different business drivers impact the decisions being made at the executive level.
Title: Data Science as a Managed Service on Red Hat OpenShift @openshift
Speakers: Audrey Reznik Guidera, Sr Principal Software Engineer & Troy Nelson, Software Engineer, @RedHat Abstract: As a Data Scientist it is often difficult to know where to start your project. Do I test and build my model in an IDE? If I build and test my model in an IDE, how do I connect to my resources such as streaming data or databases? How can I optimize my model inference performance? Once I have optimized my model how do I easily deploy and manage it? For a data scientist who just wants to build their model these are questions that they really don’t want to think about. Data Scientists just want to be able to sit down and write their algorithms. Is there a way to make the model development/interaction easier for Data Scientists? Yes there is! And that is through the use of managed services & applications available in the Red Hat OpenShift Data Science platform!
In this session we will discuss a painless way to build, test, train and deploy models using the Red Hat OpenShift Data Science platform. In particular we will cover:
1. A Model’s role in an intelligent application
2. What are Managed Services & Who uses them?
3. What do Managed Services have to do with model development & delivery?
4. Demo of using the Red Hat OpenShift Data Science platform
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