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Podcast #147: Inside HPE’s New Model for High-Performance Data Protection

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Çin Beijing Qianxing Jietong Technology Co., Ltd. Sertifikalar
Çin Beijing Qianxing Jietong Technology Co., Ltd. Sertifikalar
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Ben sohbet şimdi

Podcast #147: Inside HPE’s New Model for High-Performance Data Protection

April 15, 2026
In this week’s podcast, Brian heads to Raleigh for a chat with HPE’s Chad Bowninger—Principal Product Manager for Solutions focused on the HPE Alletra Storage MP X10000 and the brand-new Data Protection Accelerator Node (DPAN).
Even though the topic is technical, the conversation stays casual and approachable—Chad even brought in a sofa to keep the vibe friendly and relaxed.

YouTube video player
The discussion covers all the key details: from the Alletra Storage MP B10000 and its evolution to the MP X10000, to data protection best practices and the ins and outs of the new Data Protection Accelerator Node (DPAN).

The Storagereview team has been collaborating closely with Chad on an in-depth deep dive into the HPE Alletra Storage MP X10000, set to go live on February 26. Together, this podcast and the upcoming review will give you a comprehensive, no-nonsense understanding of the MP X10000 and DPAN.

son şirket davası hakkında Podcast #147: Inside HPE’s New Model for High-Performance Data Protection  0

At just around 30 minutes long, the podcast is easy to fit into your schedule—and we’ve broken it into 5-minute segments, so you can jump straight to the topics that interest you most.

0:00–5:00: Alletra Storage MP X10000 as a Backup Target


  • Introduction to X10000: Built from scratch with HPE IP for unstructured data, serving as the next evolution of the B10000 block platform.
  • Platform Strategy: Shifting the X10000’s focus from primary storage to a high-performance, all-flash backup target.
  • AI Data Demands: AI pipelines and model training require a massive “workspace” and long-term data retention capabilities.
  • High-Speed Connectivity: Currently supports 100GbE, with 200GbE certification in progress to eliminate network bottlenecks.


5:00–10:00: Data Protection Accelerator (DPA) Architecture


  • Efficiency over Raw Speed: The DPA node prioritizes effective capacity and operational value over sheer IOPS.
  • Catalyst Technology Integration: Leverages StoreOnce Catalyst code to achieve average deduplication ratios of 20:1.
  • Offloading Heavy Lifting: The DPA handles deduplication and encryption, streaming processed data directly to the X10000.
  • Rapid Recovery Focus: Uses flash-to-flash architecture to drastically speed up rehydration and restore times.
  • Partner Ecosystem: Designed as a flexible target for existing backup software like Commvault and Veeam.


10:00–15:00: Linear Scaling and Performance Benchmarks


  • Modular Growth: Each DPA node manages roughly 2PB, enabling independent scaling of compute and capacity.
  • SLA-Driven Sizing: Adding multiple DPA nodes allows for parallel streaming to meet aggressive RTO/RPO targets.
  • Ingest Performance: Quoted rates of 1.2PB per hour with a four-accelerator configuration.
  • Market Differentiation: HPE positions the DPA as a unique hardware-accelerated solution not currently available elsewhere in the market.


15:00–20:00: Portfolio Strategy and Data Value


  • Enterprise Scale: Targets multi-petabyte environments in banking, government, and large commercial sectors.
  • Futureproofing: Disaggregated architecture avoids costly forklift upgrades, allowing simple drive or node additions.
  • Immutability and Security: Built-in immutability—table-stakes for addressing ransomware concerns.
  • Multi-Purpose Utility: The X10000 can handle backup, AI pipelines, and data lakes simultaneously.


20:00–28:00: Lab Validation and Business Impact


  • Real-World Testing: Lab setups use 30-host VMware clusters to validate performance at scale.
  • Network Evolution: Closing the gap between high-speed AI fabrics and traditionally slower backup networks.
  • The “Unspoken Reality”: Addressing the common disconnect between official SLAs and actual, slower recovery speeds.
  • Economic Justification: Tying rapid restore performance directly to minimizing revenue loss during downtime.
  • Predictable Forecasting: Linear scaling makes budget and capacity planning simpler for growing organizations.


Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
WhatsApp / WeChat: +86 13426366826
Email: yangyd@qianxingdata.com
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