H54GBS | WEKA Data Platform—Deployment, Management, and Performance Engineering

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WEKA Data Platform—Deployment, Management, and Performance Engineering

H54GBS

Course ID	H54GBS
Duration	2 days
Format	ILT/VILT
Schedule, pricing & registration
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Overview

This course provides a comprehensive introduction to the HPE WEKA data platform for high-performance AI, machine learning (ML), and high performance computing (HPC) workloads. You learn the core architecture of WEKA, including its front-end and back-end design, data striping mechanisms, and scalability principles. The course covers end-to-end deployment, cluster management, security configuration, tiering to object storage, and integration with compute workloads. You also gain hands-on experience with automation, API-driven operations, and performance optimization in a distributed WEKA environment.

Course ID	H54GBS
Duration	2 days
Format	ILT/VILT
Schedule, pricing & registration
Browse related courses

Audience

This course is ideal for:

Data infrastructure and storage professionals working with high-performance computing or AI/ML workloads
System architects and technical leads responsible for designing scalable data platforms
DevOps and infrastructure engineers managing distributed or hybrid data environments
MLOps, HPC, and AI platform engineers integrating storage systems with GPU and compute clusters
IT administrators and solution engineers interested in data tiering, performance tuning, and automation

Prerequisites

There are no prerequisites for this course, but we recommend you have the following before attending:

Linux scripting knowledge:
- Familiarity with SSH, networking commands, and basic shell scripting
- Awareness of mounting file systems, and managing storage volumes
- Understanding of users, permissions, and process management
Basic understanding of storage and data
- Knowledge of file, block, and object storage
- Awareness of RAID, NFS and S3-compatible storages
- Understanding of redundancy, replication, and throughput concepts.
Exposure to big data, AI/ML, or HPC workloads
- Appreciation of GPU acceleration
- Understanding of big data
- Awareness of AI/ML lifecycle

Objectives

After completing this course, you should be able to:

Understand the HPE WEKA Data Platform architecture and its role in high-performance AI, ML, and HPC environments
Deploy, configure, and manage WEKA clusters for distributed file and object storage
Integrate WEKA with compute workloads, including GPU-accelerated systems, to achieve optimized throughput
Secure WEKA environments through authentication, encryption, and access control policies
Automate cluster operations and monitoring using the WEKA CLI and REST APIs
Implement data tiering and lifecycle management using file-object storage strategies

Course outline

Module 1: Introduction to WEKA Data Platform and Architecture	Overview of WEKA architecture and design principles Frontend and backend node functions Supported interfaces: POSIX, NFS, SMB, and S3 High-level data flow within the WEKA cluster
Module 2: System Architecture and Cluster Design	Logical vs. physical WEKA architecture Data striping and replication strategies Frontend-backend separation and balancing Planning for scalability and high availability
Module 3: Setting Up the Cluster	Node and disk preparation steps Cluster initialization and joining backend nodes Client driver installation and configuration Verification and cluster health checks
Module 4: Cluster Ops and Management	Node management and capacity expansion Monitoring tools and dashboards Snapshotting and version control Data protection, recovery, and maintenance operations
Module 5: Integration with Compute Workloads	Configuring WEKA clients on compute nodes Data locality and caching mechanisms Benchmarking throughput and latency Running distributed workloads and performance validation
Module 6: Tiering and Object Store	Tiering architecture and benefits Configuring object storage backends Automating data movement and rehydration Cost-performance optimization strategies
Module 7: Security and Access Management	User and group management Role-based access control (RBAC) Encryption in transit and at rest Audit logging and security best practices
Module 8: Automation and Performance Optimization	Using WEKA CLI and REST API for operations Scripting backups, scaling, and monitoring Performance diagnostics and tuning Troubleshooting and capacity planning best practices

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Lab outline

Lab 1: WEKA Cluster Deployment and Initialization	Deploy compute nodes to form a WEKA cluster Configure networking, validate connectivity, and ensure node discovery Initialize the WEKA cluster, join nodes, and verify cluster health Mount the WEKA filesystem on the control node and confirm accessibility
Lab 2: Cluster Management and Monitoring	Explore WEKA’s management console and CLI for monitoring performance metrics Add and remove nodes dynamically to simulate scaling operations Create and restore snapshots to demonstrate data protection Monitor throughput, latency, and disk usage
Lab 3: Client Integration and Workload Simulation	Install WEKA client drivers on compute nodes Mount the WEKA filesystem for application access. Run sample I/O tests to measure read/write throughput Simulate machine learning workload performance over the WEKA filesystem
Lab 4: Tiering and Object Store Configuration	Connect the WEKA cluster to an object store for tiering Configure automatic data movement policies for cold and hot data Move datasets between tiers and verify retrieval using rehydration commands
Lab 5: Security and Access Control Implementation	Configure role-based access control (RBAC) for users and groups Enable data encryption Set up audit logging for administrative actions and access attempt
Lab 6: Automation, API Integration, and Performance Tuning	Use WEKA CLI and REST API for automated management tasks Script cluster health checks, capacity monitoring, and scaling workflows Run diagnostic tools to identify I/O bottlenecks Apply performance tuning parameters and validate improvements through benchmarking

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a50014290enw, H54GBS A.00, November 2025

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