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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

Browse related courses

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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  3. Enjoy personalized learning journeys aligned to your company’s needs.
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  5. Sharpen your skills with access to real environments in virtual labs .

Explore our simplified purchase options, including HPE Education Learning Credits .

  • 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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