AI & Analytics at Scale: Accelerating Data Insights on Nutanix and Dell PowerFlex

Introduction

The digital era is marked by an explosion of data generated from applications, devices, and user interactions. IT teams face a pressing mandate: deliver actionable insights faster, even as data volumes multiply and analytics requirements become more complex. Traditional siloed infrastructure often becomes a bottleneck, hindering agility and slowing time-to-insight.

Modern enterprises need an integrated, high-performance foundation that simplifies analytics at scale. This is where the combined strengths of Nutanix hyperconverged infrastructure (HCI) and Dell PowerFlex software-defined storage (SDS) shine. Together, they enable seamless scaling of AI, machine learning (ML), and analytics workloads—so teams can unlock business value, not just manage storage and compute.

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Explosion of Data and the Need for Fast Analytics

Every organization is grappling with surging volumes of structured and unstructured data. Edge, IoT, SaaS, and transactional systems all contribute to this rapid growth. Analytics pipelines are now expected to support diverse workloads, from real-time dashboards to multi-stage ML model training. Meeting these demands requires:

• Scalable, elastic compute and storage
• High-throughput parallel data access
• Fast data ingestion, transformation, and retrieval
• Consistent performance under heavy, mixed workloads

Legacy architectures struggle with these requirements due to resource silos, complex management, and unpredictable scaling. By converging Nutanix and PowerFlex, organizations can deploy a future-ready analytics stack with operational simplicity, high performance, and flexibility.

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Integrated Architecture

High-Level Design

Below is a conceptual diagram representing the joint Nutanix + PowerFlex solution for analytics:

• Compute: Nutanix HCI nodes run virtualized analytics workloads (Spark, Hadoop, containerized ML).
• Storage: PowerFlex delivers high-performance, scalable, and resilient block storage over standard Ethernet or Fibre Channel.
• Integration: Nutanix clusters connect to PowerFlex storage pools for both primary data and analytics scratch spaces.

Deploying Hadoop/Spark, AI/ML Workloads

Nutanix HCI is certified for mainstream analytics and AI stacks. You can deploy clusters using native VM orchestration, Kubernetes, or through automated tools (such as Nutanix Calm blueprints).

Example: Deploying a Spark Cluster on Nutanix HCI (Vendor-Agnostic Steps)

1. Provision Compute:
   • Spin up VM or container clusters (via Nutanix Prism/Calm or standard CLI/API)
2. Connect to PowerFlex Storage:
   • Attach PowerFlex volumes to compute nodes using iSCSI or Fibre Channel.
   • Format and mount volumes for data (e.g., /data/hadoop).
3. Install Analytics Stack:
   • Deploy Hadoop/Spark, set data paths to the attached PowerFlex storage.
   • Configure data locality parameters for optimal performance.

Sample Linux Commands for Attaching PowerFlex Volume:

# Discover PowerFlex storage via iSCSI
sudo iscsiadm -m discovery -t sendtargets -p <powerflex_ip>
sudo iscsiadm -m node --login

# Identify and format the new volume
lsblk
sudo mkfs.xfs /dev/sdX
sudo mkdir /data/hadoop
sudo mount /dev/sdX /data/hadoop

Spark/Hadoop config (example core-site.xml):

<property>
  <name>fs.defaultFS</name>
  <value>file:///data/hadoop</value>
</property>

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Leveraging PowerFlex’s Parallel Storage for Performance

Dell PowerFlex stands out for its distributed, shared-nothing architecture. Every node contributes compute and storage resources, with automatic data balancing and parallel access. This enables:

• High throughput for data ingest and transformation
• Consistent low-latency for AI/ML model training and inference
• Dynamic scaling as new workloads are added

Key PowerFlex Features:

• Linearly scalable performance (add nodes, get more IOPS and bandwidth)
• Flexible, policy-driven volume provisioning for different analytics stages (raw, staging, hot, archive)
• Advanced caching and tiering (see Best Practices below)

Sample PowerFlex CLI for Creating a Volume:

# Authenticate with PowerFlex
scli --login --username admin --password <password>

# Create a new storage volume for analytics workloads
scli --add_volume --volume_name AnalyticsData --size_gb 5000

# Map volume to cluster hosts
scli --map_volume_to_sdc --volume_name AnalyticsData --sdc_id <host_id>

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Best Practices

Data Locality

• Co-locate compute and storage as much as possible for minimal network hops.
• Use Nutanix Data Locality features (e.g., VM-optimized storage) to keep frequently accessed data near compute.

Tiering and Caching

• Leverage PowerFlex’s automated tiering to keep hot data on NVMe/SSD, while older or less-frequently accessed data can move to capacity-optimized tiers.
• Use analytics software settings to align temporary/scratch data with fast storage volumes.

Cache Management

• Monitor cache hit rates and adjust cache size or tiering policy to match workload patterns.
• Consider Nutanix’s integrated caching for VMs running frequent, repetitive queries.

Configuration Tuning

• For Hadoop/Spark, configure data block and replication factors based on workload type and cluster size.
• Monitor and tune JVM heap sizes, thread pools, and disk queue depths for analytics VMs.

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Sample Workflows

End-to-End Data Pipeline: Ingest to Insights

1. Ingest:
   • Data arrives via bulk load, streaming, or direct API into PowerFlex-backed storage.
2. Transform:
   • ETL jobs run on Spark/Hadoop within Nutanix VMs, using PowerFlex for staging and temp data.
3. Model/Analytics:
   • ML workloads train or score models, reading/writing directly from/to high-performance storage.
4. Visualize/Export:
   • Results exported to business intelligence (BI) tools or delivered to consumers.

Example: Streaming Ingest Using Apache NiFi

# NiFi processor pulls data and writes directly to PowerFlex volume
/data/ingest -> /data/analytics/staging

Workflow Diagram:

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Monitoring and Cost Control

Resource Tracking

• Use Nutanix Prism for real-time analytics on compute, memory, storage utilization per VM and container.
• PowerFlex Manager offers insights into storage pool health, IOPS, bandwidth, and latency.

Showback/Chargeback Models

• Map analytics jobs or departments to specific Nutanix/PowerFlex resources.
• Use built-in reporting and APIs to track usage, enabling showback or chargeback billing.
• Example: Tag VMs and storage volumes by project, generate monthly reports via Nutanix Prism Central or PowerFlex REST API.

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Customer Example (Hypothetical Scenario)

A global retailer deploys Nutanix HCI for its analytics and AI workloads. PowerFlex provides a high-performance storage backbone for ingest, model training, and dashboarding.

• Challenge: Inconsistent analytics performance as data volumes grow
• Solution: Move analytics VMs and data pipelines to Nutanix HCI with PowerFlex storage
• Outcome: Time-to-insight drops from hours to minutes, operational costs stabilize due to efficient resource pooling and showback billing

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Conclusion

Organizations seeking to unlock business value from their data need a foundation that can scale, perform, and simplify operations. Integrating Nutanix with Dell PowerFlex delivers the agility, parallel performance, and operational visibility needed to run modern analytics and AI at scale. Whether you are modernizing data lakes, accelerating ML, or powering real-time dashboards, this joint solution positions you for rapid insights and data-driven success.

Disclaimer: The views expressed in this article are those of the author and do not represent the opinions of Dell, Nutanix, or any affiliated organization. Always refer to the official Dell and Nutanix documentation before production deployment.