Data protector Cohesity is integrating its Gaia GenAI search assistant with Google’s Gemini AI model and its Agentspace, using Google Threat Intelligence and working with Mandiant on incident response and a Google Cloud recovery environment.
These announcements came at the ongoing Google Cloud Next 2025 event in Las Vegas. Gemini is a family of multi-modal large language models (LLMs) covering text, images, audio, video and code and trained on Google’s own TPU hardware. Google subsidiary Mandiant is a cyber-security business unit in Google’s Cloud division.
Agentspace is a Gemini-powered center or hub linking LLMs, Gemini itself obviously included, with data sources such as Google Workspace, Salesforce, Jira and SharePoint. It helps with the creation of custom LoB-focussed agents to automate multi-step jobs and supports Google’s Agent2Agent (A2A) protocol for intra-agent comms. The complementary Anthropic MCP protocol supports agent-to-tool comms.
Cohesity is adding its Gaia agent to Agentspace so it can be used to analyse customer’s Cohesity-generated and other proprietary data. It says customers will be able to search across enterprise data regardless of where it’s hosted, using secure data APIs. They’ll “be able to unlock advanced reasoning capabilities powered by Google Cloud’s Gemini models, enabling deeper insights and smarter decision-making.”
This Gaia Agentspace integration will improve compliance, data security and data discovery, plus the Gaia-Gemini model combo will provide “more intelligent data analysis, discovery, and management.”
There are four security aspects to the Google-Cohesity partnership:
Google Threat Intelligence integrated in the Cohesity Data Cloud will enable customers to “rapidly detect new threats in their backup data … [and] significantly improve Cohesity’s existing threat detection and incident response capabilities.”
Cohesity’s Cyber Events Response Team (CERT) and Google’s Mandiant Incident Response teams can now work together to help customers minimise business downtime during incidents. The two provide more comprehensive incident response engagements for joint customers. Using data from Cohesity, Mandiant can expedite the containment, investigation, and mitigation of an attack from the customer’s primary infrastructure, while Cohesity secures the backup infrastructure.
Cohesity customers can work with Mandiant to establish, secure, and validate a Cloud Isolated Recovery Environment (CIRE) in Google Cloud before an incident occurs.
Cohesity Data Cloud integration with Google’s security operations “for improved data resiliency and enhanced security posture management.
The Google Threat Intelligence service uses Mandiant Frontline threat knowledge, the VirusTotal crowd-sourced malware database, Google’s own threat expertise and awareness plus Gemini AI model-powered analysis to alert users to new threats. It’s available on its own or integrated into Google Security Operations.
Cohesity’s Integrations with Google Cloud for cyber resilience, AI model data sourcing and analysis are expected to be available by the summer. Its incident response partnership with Mandiant and the integration of the Cohesity Data Cloud with Google’s Security Operations are available now.
Cohesity competitor Rubrik has also announced a way for its customers to establish a Cloud-based Isolated Recovery Environment (CIRE) by working with Mandiant. You can read about this in a Rubrik blog.
Rubrik’s Annapurna feature provides data from its Rubrik Security Cloud to large language model (LLM) AI Agents. Annapurna will use Agentspace to provide “easy, secure access to data across cloud, on-premises, and SaaS environments.” It will offer:
API-driven secure access to enterprise-wide data for AI training and retrieval
Anomaly detection and access monitoring to prevent AI data leaks and unauthorized use
Seamless AI data pipelines to combine Google Cloud AI models with enterprise data
Automated compliance enforcement to protect sensitive AI training data
Rubrik also tells us it’s been pronounced the 2025 Google Cloud Infrastructure Modernization Partner of the Year for Backup and Disaster Recovery.
Cloud data warehouser Snowflake is supporting the Apache Iceberg open table format alongside its own native data table formats.
Iceberg is an open source table format for large-scale datasets in data lakes, layered above storage systems like Parquet, ORC, and Avro, and cloud object stores such as AWS S3, Azure Blob, and the Google Cloud Store. It provides database-like features to data lakes, such as ACID support, partitioning, time travel, and schema evolution, and enables SQL querying of data lake contents.
Christian Kleinerman
Christian Kleinerman, Snowflake’s EVP of Product, stated: “The future of data is open, but it also needs to be easy.”
“Customers shouldn’t have to choose between open formats and best-in-class performance or business continuity. With Snowflake’s latest Iceberg tables innovations, customers can work with their open data exactly as they would with data stored in the Snowflake platform, all while removing complexity and preserving Snowflake’s enterprise-grade performance and security.”
Snowflake says that, until now, organizations have either relied on integrated platforms, like Snowflake, to manage their data or use open, interoperable data formats like Parquet. It says its Iceberg support means that “customers now gain the best of both worlds. Users can store, manage, and analyze their data in an open, interoperable format, while still benefiting from Snowflake’s easy, connected, and trusted platform.”
Snowflake with Iceberg accelerates lakehouse analytics, applying its compute engine to Iceberg tables with two go-faster features coming soon: a Search Optimization service and a Query Acceleration Service. It is extending its data replication and syncing to Iceberg tables; now in private preview, so that customers can restore their data in the event of a system failure, cyberattack, or other disaster.
Snowflake says it’s working with the Apache Iceberg community to launch support for VARIANT data types. It’s also focused on working with other open source projects.
In June last year Snowflake announced its Polaris Catalog, a managed service for Apache Polaris and a vendor-neutral, open catalog implementation for Apache Iceberg. Apache Polaris is an open-source catalog for Apache Iceberg, implementing Iceberg’s REST API, enabling multi-engine interoperability across a range of platforms, including an Apache trio: Doris, Flink, and Spark, plus Dremio, StarRocks, and Trino. Now Snowflake is getting even closer to Iceberg.
Other Snowflake open source activities include four recent acquisitions:
Apache NiFi: Datavolo (acquired by Snowflake in 2024) and built on NiFi, simplifies ingestion, transformation, and real-time pipeline management.
Modin: Snowflake accelerates pandas workloads with Modin (acquired by Snowflake in 2023), enabling seamless scaling without code change.
Streamlit: Snowflake’s integration with Streamlit (acquired by Snowflake in 2022) allows users to build and share interactive web applications, data dashboards, and visualizations with ease.
TruEra: TruEra (acquired by Snowflake in 2024) boosts AI explainability and model performance monitoring for bias detection, compliance, and performance insights.
Competitor Databricks acquired Tabular, and its data management software layer based on Iceberg tables, last year. Iceberg and Databricks’ Delta Lake are both based on Apache Parquet. Snowflake has now, like Databricks, recognized Iceberg is beginning to dominate.
The Ultra Accelerator Link Consortium has released its 200G v1.0 spec – meaning competition for Nvidia’s BasePOD and SuperPOD GPU server systems from pods containing AMD and Intel GPUs/accelerators is coming closer.
The UALink consortium was set up in May last year to define a high-speed, low-latency interconnect specification for close-range scale-up communications between accelerators and switches in AI pods and clusters. It was incorporated in October 2024 by AMD, Astera Labs, AWS, Cisco, Google, HPE, Intel, Meta, and Microsoft. Alibaba Cloud Computing, Apple and Synopsis joined at board level in January this year. Other contributor-level members include Alphawave Semi, Lenovo, Lightmatter and, possibly, Samsung. We understand there are more than 65 members in total.
The members want to foster an open switch ecosystem for accelerators as an alternative to Nvidia’s proprietary NVLink networking. This v1.0 spec enables 200G per lane scale-up connection for up to a theoretical 1,024 accelerators in a pod. Nvidia’s NVLink supports up to 576 GPUs in a pod.
Kurtis Bowman
Kurtis Bowman, UALink Consortium Board Chair and Director, Architecture and Strategy at AMD, stated: “UALink is the only memory semantic solution for scale-up AI optimized for lower power, latency and cost while increasing effective bandwidth. The groundbreaking performance made possible with the UALink 200G 1.0 Specification will revolutionize how Cloud Service Providers, System OEMs, and IP/Silicon Providers approach AI workloads.”
This revolution depends first and foremost on UALink-supporting GPUs and other accelerators from AMD and Intel being used in preference to Nvidia products by enough customers to make a dent in the GPU/accelerator market.
NVLink is used by Nvidia as a near-or close-range link between CPUs and GPUs and between its GPUs. It’s a point-to-point mesh system which can also use an NVSwitch as a central hub.
UALink v1.0 provides a per-lane bidirectional data rate of 200 GTps (200 GBps) and allows 4 lanes per accelerator connection, meaning the total connection bandwidth is 800 GBps.
NVLink4.0, the Hopper GPU generation, delivers 900 GBps aggregate bidirectional bandwidth across 18 links, each running at 50 GBps. This is 100 GBps more than UALink v1.0.
NVLink v5.0, as used with Blackwell GPUs, provides 141 GBps per bidirectional link and up to 18 links per GPU connection, meaning a total of 2,538 GBps per connection, more than 3 times higher than UALink v1.0.
NVLink offers higher per-GPU bandwidth by supporting more links (lanes) than UALInk, which can, in theory, scale out to support more GPUs/accelerators than NVLink.
Should Nvidia be worried about UALink? Yes, if UALink encourages customers to use non-Nvidia GPUs. How might it respond? A potential Rubin GPU generation NVLink 6.0 could increase per-link bandwidth to match/exceed UALink’s 200 GBps and also extend scalability out to the 1,024 area. That could be enough to prevent competitive inroads into Nvidia’s customer base, unless its GPUs fall behind those of AMD and Intel.
UALink v1.0 hardware is expected in the 2026/2027 period, with accelerators and GPUs from, for example, AMD and Intel supporting it along with switches from Astera Labs and Broadcom.
You can download an evaluation copy of the UALInk v1.0 spec here.
Google Cloud and NetApp are extending the NetApp Volumes storage service to work better with Vertex AI, support larger data sets, separately scale capacity and performance, and meet regional compliance needs,
Google Cloud NetApp Volumes, GCNV for short, is a fully managed file service based on NetApp’s ONTAP operating system running on the Google Cloud Platform as a native GCP service. It supports NFS V3 and v4.1, and SMB, and provides snapshots, clones, replication, and cross-region backup. Google’s Vertex AI is a combined data engineering, data science, and ML engineering workflow platform for training, deploying, and customizing large language models (LLMs), and developing AI applications. It provides access to Google’s Gemini models, which work with text, images, video, or code, plus other models such as Anthropic’s Claude and Llama 3.2.
Pravjit Tiwana.
NetApp SVP and GM for Cloud Storage, Pravjit Tiwana, states: ”Our collaboration with Google Cloud is accelerating generative AI data pipelines by seamlessly integrating the latest AI innovations with the robust data management capabilities of NetApp ONTAP.”
He reckons: “The new capabilities of NetApp Volumes help customers scale their cloud storage to meet the demands of the modern, high-performance applications and datasets.”
The new capabilities in detail are:
Coming NetApp Volumes integration with Google Cloud’s Vertex AI Platform: so customers will be able to build custom agents without needing to build their own data pipeline management for retrieval augmented generation (RAG) applications.
Improvements for Premium and Extreme Service Levels in all 14 regions where the Premium and Extreme service levels are offered. Customers can now provision a single volume starting at 15TiB that can be scaled up to 1PiB with up to 30 GiB/s of throughput. This means customers can move petabyte-scale datasets for workloads like EDA, AI applications, and content data repositories to NetApp Volumes without partitioning data across multiple volumes.
Flex Service Level previewing of independent scaling of capacity and performance to avoid over-provisioning of capacity to meet their performance needs with the NetApp Volumes Flex service level. Users can create storage pools by individually selecting capacity, throughput and IOPS with the ability to scale throughput up to 5 GiB/s and IOPS up to 160K to optimize costs.
NetApp Volumes will soon support the Assured Workloads framework that Google Cloud customers use to configure and maintain controlled environments operating within the parameters of a specific compliance regime, meeting the data residency, transparent access control, and cloud key management requirements specific to their region.
GCNV flex, standard, premium and extreme service level offerings can be researched here. The GCNV-Vertex AI integration is coming “soon.”
Proprietary data in GCNV will be able to be used via Vertex AI to implement model agent RAG capabilities.
NetApp has received the 2025 Google Cloud Infrastructure Modernization Partner of the Year for Storage award, which is a nice pat on the back.
Sameet Agarwal, Google Cloud Storage GM and VP, said: “Organizations can leverage their NetApp ONTAP on-premises data and hybrid cloud environments. By combining the capabilities of Google Cloud’s Vertex AI platform with Google Cloud NetApp Volumes, we’re delivering a powerful solution to help customers accelerate digital transformation and position themselves for long-term success.”
DDN is partnering Google Cloud with its Google Cloud Managed Lustre, powered by DDN offering.
The Lustre parallel file system enables Google Cloud to offer file storage and fast access services for enterprises and startups building AI, GenAI, and HPC applications. It provides up to to 1 TB/s throughput and can scale from terabytes to petabytes.
Alex Bouzari, Co-Founder and CEO of DDN, bigged this deal up by stating: “This partnership between DDN and Google Cloud is a seismic shift in AI and HPC infrastructure—rewriting the rules of performance, scale, and efficiency. … we’re not just accelerating AI—we’re unleashing an entirely new era of AI innovation at an unprecedented scale. This is the future, and it’s happening now.”
DDN says on-prem Lustre customers “can now extend their AI workloads to the cloud effortlessly.”
You might think that this is a revolution but, one, Google already has Lustre available on its cloud, just not as a managed service, and, two, its main competitors also offer Lustre services.
Google’s existing Lustre on GCP can be set up using deployment scripts or through DDN’s EXAScaler software, built on Lustre, which is available through the Google Cloud marketplace. Now it has moved on with this fully managed Lustre service offering which makes it easier for its customers to use Lustre.
AWS offers FSx for Lustre as well as FSx for OpenZFS and BeeGFS on AWS. Azure also offers Azure Managed Lustre plus BeeGFS on Azure and GlusterFS on Azure. You are spoilt for choice.
Google Cloud Managed Lustre (GCML) links to Google Cloud’s Compute Engine, GKE (Google Kubernetes Engine), Cloud Storage and other services for an integrated deployment. DDN and Google say it can speed up data pipelines for AI model training, tuning and deployment, and enable real-time inferencing.
The Google Cloud also has DAOS-powered ParallelStore available, DAOS being the open source Distributed Asynchronous Object Storage parallel file system.
GCML comes with 99.999 percent uptime and has a scalable pricing scheme. It can be seen at the Google Cloud Next 2025 event at the Mandalay Bay Convention Center, Las Vegas, April 9 to 11, where DDN is also demoing its Infinia object storage software.
Against a background of disaggregated IT and rising AI trends, Dell has announced refreshes of its PowerEdge, PowerStore, ObjectScale, PowerScale, and PowerProtect storage systems.
Dell is announcing both server and storage advances. It says its customers need to support existing and traditional workloads as well as provide IT for generative AI tasks. A disaggregated server, storage, and networking architecture is best suited for this and builds on three-tier and hyperconverged infrastructure designs, with separate scaling for the three components collected together in shared resource pools.
Arthur Lewis
Dell Infrastructure Solutions Group president Arthur Lewis stated: “From storage to servers to networking to data protection, only Dell Technologies provides an end-to-end disaggregated infrastructure portfolio that helps customers reduce complexity, increase IT agility, and accelerate datacenter modernization.”
Dell’s PowerEdge R470, R570, R670, and R770 servers are equipped with Intel Xeon 6 processors with performance cores. These are single and double-socket servers in 1U and 2U form factors designed for traditional and emerging workloads like HPC, virtualization, analytics, and AI inference.
Our focus here is on the storage product announcements, which cover the unified file and block PowerStore arrays, cloud-native ObjectScale, scale-out clustered PowerScale filer system, and Dell’s deduplicating backup target PowerProtect systems developed from prior Data Domain arrays.
PowerStore
A PowerStore v4.1 software release provides AI-based analytics to detect potential issues before they occur, auto support ticket opening, carbon footprint forecasting, DoD CAC/PIV smart card support, automated certificate renewal and improved PowerProtect integration through Storage Direct Protection. This enables up to 4x faster backup restores and support for the latest PowerProtect systems; the DD6410 appliance and All-Flash Ready Nodes (see below).
Dell PowerStore node
The software provides better storage efficiency tracking, now covering both file and block data, and ransomware-resistant snapshots, supplementing the existing File Level Retention (FLR) and other local, remote, and cloud-based protection methods.
It offers file system QoS with more granular performance controls. Dell Unity customers migrating to PowerStore can preserve their existing Cloud Tiering Appliance (CTA) functionality. Archived files remain fully accessible, and customers can create new archiving policies for migrated file systems on PowerStore.
ObjectScale is scale-out, containerized object storage software running on ECS hardware nodes. Up until now there were three ECS hardware boxes: EX500 (12-24 HDDs, to 7.68 PB/rack), EX5000 (to 100 HDDs, to 14 PB/rack) and all-flash EXF900 (12-24 NVMe SSDs, to 24.6 PB/rack).
New ObjectScale v4.0 software boasts smart rebalancing, better space reclamation, and capacity utilization. It also has expanded system health metrics, alerting, and security enhancements. Dell claims it offers “the world’s most cyber-secure object storage.”
There are two new ObjectScale systems. The all-flash XF960 is said to be designed for AI workloads and is an evolution of the EXF900. It has extensive hardware advances based on PowerEdge servers and delivers up to 2x greater throughput per node than the closest but unnamed competitor, and up to 8x more density than the EXF900.
ObjectScale X560 top and XF960 bottom
The HDD-based X560 accelerates media, backup, and AI model training ingest workloads with 83 percent higher small object read throughput than the EX500 running v3.8 software.
Dell is partnering with S3-compatible cloud storage supplier Wasabi to introduce Wasabi with Dell ObjectScale, a hybrid cloud object storage service with tiers starting from 25 TB of reserved storage per month. Wasabi has a global infrastructure, with more than 100,000 customers in 15 commercial and two government cloud regions worldwide.
More ObjectScale news is expected at the upcoming Dell Technologies World conference.
PowerScale
PowerScale all-flash F710 and F910 nodes get 122 TB Solidigm SSD support, doubling storage density. This, with 24 bays in their 2RU chassis and 2:1 data reduction, provides almost up to 6 PB of effective capacity per node. Dell says it’s the first supplier to offer an enterprise storage system with such SSDs.
The PowerScale archive A and hybrid H series nodes – H710, H7100, A310, A3100 – have lower latencies and faster performance with a refreshed compute module for HDD-based products. Dell says the A-Series is optimized for TCO, while the H-series provides a balanced cost/performance mix. The updated nodes feature:
Fourth-generation Intel Xeon Sapphire Rapids CPUs
DDR5 DRAM with up to 75 percent greater speed and bandwidth
Improved thermal operation reducing heat and stress on components
Updated drive carrier with 100 percent greater speed for SAS drives
Dell will introduce support for 32 TB HAMR disk drive technology later this year with “extended useful life.”
A PowerScale 1RU A110 Accelerator Node is a successor to the previous generation P100 and B100 performance and backup accelerators. It’s designed to solve CPU bottlenecks and boost overall cluster performance with higher cluster bandwidth. The A110 can be independently scaled in single node increments.
PowerProtect
There are three main developments here. First, the PowerProtect DD6410 is a new entry-level system with a capacity of 12 TB to 256 TB. It’s aimed at commercial, small business, and remote site environments, with up to 91 percent faster restores than the DD6400, up to 65x deduplication, and scalability for traditional and modern workloads.
Secondly, the PowerProtect All-Flash Ready Node has 220 TB capacity with over 61 percent faster restore speeds, up to 36 percent less power, and a 5x smaller footprint than the PowerProtect DD6410 appliance. It does not support the 122 TB SSDs, built with QLC 3D NAND, because their write speed is not fast enough.
Both the DD6410 and All-Flash Ready Node support the Storage Direct Protection integration with PowerStore and PowerMax, providing faster, efficient, and secure backup and recovery.
PowerProtect DD6410 (top) and All-Flash Ready Node (bottom)
Thirdly, a PowerProtect DataManager software update reduces cyber-security risks with anomaly detection. This has “machine learning capabilities to identify vulnerabilities within the backup environment, enabling quarantine of compromised assets. It provides early insights in detecting threats in the backup environment while complementing the CyberSense deep forensics analysis of isolated recovery data in the Cyber Recovery vault, providing end-to-end cyber resilience of protected resources.”
As well as VMware, DataManager now manages Microsoft Hyper-V and Red Hat OpenShift Virtualization virtual machine backups. A suggestion of future Nutanix AHV support to Dell received a positive acknowledgement as a possibility.
DataManager archives data to ObjectScale for long-term retention. This is not tiering with a stub left behind. The archived data can be restored directly without first being rehydrated to a PowerProtect system. The archiving is to S3-compatible object stores.
DataManager also has Multi-System Reporting which offers centralized visibility and control across up to 150 PowerProtect Data Manager instances.
Availability
PowerProtect Data Manager updates are available now.
PowerEdge R470, R570, R670, and R770 servers are available now.
PowerStore software updates are available now.
ObjectScale is available now as a software update for current Dell ECS environments.
HDD-based ObjectScale X560 will be available April 9, 2025.
All-Flash ObjectScale XF960 appliances will be available beginning in Q3 2025.
The Wasabi with Dell ObjectScale service is available in the United States. UK availability begins this month, with expansion into other regions planned in the coming months.
PowerScale HDD-based nodes will be available in June 2025.
PowerScale with 122 TB drives will be available in May 2025.
PowerProtect DD6410 and All-Flash Ready Node will be available in April 2025.
As the on-premises backup target market grows, so too does ExaGrid – which just posted its best ever Q1.
The company supplies deduplicating backup appliances with a non-deduped landing zone for faster restores of recent data. Deduped data is moved to a non-network-facing area for further protection. Its appliances can be grouped with cross-appliance deduplication raising storage efficiency.
Bill Andrews
At the end of 2025’s first quarter ExaGrid was was free cash flow (FCF) positive, P&L positive, and EBITDA positive for its 17th consecutive quarter and has no debt. CEO Bill Andrews emphasized this, telling us: “We have paid off all debt. We have zero debt. We don’t even have an account receivable line of credit (don’t need it).”
It recruited 155 new logos, taking its total well past 4,600 active upper mid-market to large enterprise customers. The company says it continues to have 75 percent of its new logo customer bookings come from six- and seven-figure purchase orders. Andrews tells us: “For the last 8 quarters, each quarter 75 percent of our new logo customer bookings dollars come from deals over $100K and over $1M. Only 25 percent of new customer bookings dollars come from deals under $100K.”
Andrews stated: “ExaGrid continues to profitably grow as it keeps us on our path to eventually becoming a billion-dollar company. We are the largest independent backup storage vendor and we’re very healthy … ExaGrid continues to have an over 70 percent competitive win rate replacing primary storage behind the backup application, as well as inline deduplication appliances such as Dell Data Domain and HPE StoreOnce.”
The company has a 95 percent net customer retention rate and an NPS score of +81. Andrews tells us: “Our customer retention is growing and is now at 95.3 percent. We think perfection is 96 percent because you can’t keep every customer as some go out of business, some get acquired, some move everything to the cloud, etc.”
For ExaGrid’s top 40 percent customers, its largest, “we have a 98 percent retention which is very high for storage.” He adds: “99 percent of our customers are on maintenance and support, also very high for the industry.”
The 5,000 customer level is in sight and Andrews left us with this thought: “Things are going well and shy of the tariffs throwing us into a depression, we should have yet another record bookings and revenue year. … The goal is to keep growing worldwide as there is a lot of headroom in our market.”
Bootnote
For reference Dell has more than 15,000 Data Domain/Power Protect customers.
Qumulo has added a performance-enhancing NeuralCache predictive caching feature to its Cloud Data Fabric.
The Cloud Data Fabric (CDF) was launched in February and has a central file and object data core repository with coherent caches at the edge. The core is a distributed file and object data storage cluster that runs on most systems, vendors, or public cloud infrastructures. Consistency between the core and edge sites comes from file system awareness, block-level replication, distributed locking, access control authentication, and logging.
NeuralCache uses a set of supervised AI and machine learning models to dynamically optimize read/write caching, with Qumulo saying it’s “delivering unparalleled efficiency and scalability across both cloud and on-premises environments.”
Kiran Bhageshpur
CTO Kiran Bhageshpur states: “The Qumulo NeuralCache redefines how organizations manage and access massive datasets, from dozens of petabytes to exabyte-scale, by adapting in real-time to multi-variate factors such as users, machines, applications, date/time, system state, network state, and cloud conditions.”
NeuralCache, Qumulo says, “continuously tunes itself based on real-time data patterns. Each cache hit or miss refines the model, improving efficiency and performance as more users, machines, and AI agents interact with it.”
It “intelligently stacks and combines object writes, minimizing API charges in public cloud environments while optimizing I/O read/write cycles for on-premises deployments – delivering significant cost savings without compromising durability or latency.”
The NeuralCache software “automatically propagates changed data blocks in response to any write across the Cloud Data Fabric” and “users, machines, and AI agents always access the most current data.”
Bhageshpur says this “enhances application performance and reduces latency while ensuring data consistency, making it a game-changer for industries relying on data-intensive workflows, including AI research, media production, healthcare, pharmaceutical discovery, exploratory geophysics, space and orbital telemetry, national intelligence, and financial services.”
Qumulo says NeuralCache excels at dataset scales from 25 PB to multiple exabytes, “learning and improving as data volume and workload complexity grows.”
This predictive caching software was actually included in the February CDF release, but a Qumulo spokesperson told us it “wasn’t fully live and we were just referring to it generically as ‘Predictive Caching.’ Since then, we have had a customer test it out and provide feedback like a Beta test. And we formally named it NeuralCache.”
Interestingly, high-end storage array provider Infinidat has a caching feature that is similarly named but based on its array controller’s DRAM. Back in June 2020, we wrote that its array software has “data prefetched into a memory cache using a Neural Cache engine with predictive algorithms … The Neural Cache engine monitors which data blocks have been accessed and prefetches adjacent blocks into DRAM.” It enables more than 90 percent of the array data reads to be satisfied from memory instead of from much slower storage drives.
Despite the similarity in naming, however, Qumulo’s NeuralCache tech is distinct from Infinidat’s patented Neural Cache technology
Qumulo’s NeuralCache is available immediately as part of the vendor’s latest software release and is seamlessly integrated into the Qumulo Cloud Data Fabric. Existing customers can upgrade to it with no downtime. Find out more here.
Interview: Startup Starburst develops and uses Trino open source distributed SQL to query and analyze distributed data sources. We spoke to CEO Justin Borgman about the company’s strategy.
A little history to set the scene, and it starts with Presto. This was a Facebook (now Meta) open source project from 2012 to provide analytics for its massive Hadoop data warehouses by using a distributed SQL query engine. It could analyze Hadoop, Cassandra, and MySQL data sources and was open sourced under the Apache license in 2013.
The four Presto creators – Martin Traverso, Dain Sundstrom, David Phillips, and Eric Hwang – left in 2018 after disagreements over Facebook’s influence on Presto governance.
They then forked the Presto code to PrestoSQL. Facebook donated Presto to the Linux Foundation in 2019, which then set up the Presto Foundation. By then, thousands of businesses and other organizations were Presto users. PrestoSQL was rebranded to Trino to sidestep potential legal action after Facebook obtained the “Presto” trademark. The forkers set up Starburst in 2019, with co-founder and CEO Justin Borgman, to supply Trino and sell Trino connectors and support.
Borgman co-founded SQL-on-Hadoop company Hadapt in 2010. Hadapt was bought by Teradata in 2014 with Borgman becoming VP and GM of its Hadoop portfolio unit. He resigned in 2019 to join the other Starburst founders.
Eric Hwang is a distinguished engineer at Starburst. David Phillips and Dain Sundstrom both had CTO responsibilities, but they left earlier this year to co-found IceGuard, a stealth data security company. Martin Traverso is Starburst’s current CTO.
Starburst graphic
Starburst has raised $414 million over four rounds in 2019 ($22 million A-round), 2020 ($42 million B-round), 2021 ($100 million C-round), and 2022 ($250 million D-round).
It hired additional execs in early 2024 and again later that year to help it grow its business in the hybrid data cloud and AI areas.
Earlier this year, Starburst reported its highest global sales to date, including significant growth in North America and EMEA, with ARR per customer over $325,000. There was increased adoption of Starburst Galaxy, its flagship cloud product, by 94 percent year-over-year, and it signed its largest ever deal – a multi-year, eight-figure contract per year, with a global financial institution.
Blocks and Files: Starburst is, I think, a virtual data lakehouse facility in that you get data from various sources and then feed it upstream to whoever you need to.
Justin Borgman
Justin Borgman: Yeah, I like that way of thinking about it. We don’t call ourselves a virtual lakehouse, but it makes sense.
Blocks and Files: Databricks and Snowflake have been getting into bed with AI for some time, with the last six to nine months seeing frenetic adoption of large language models. Is Starburst doing the same sort of thing?
Justin Borgman: In a way, yes, but maybe I’ll articulate a couple of the differences. So for us, we’re not focusing on the LLM itself.
We’re basically saying customers will choose their own LLM, whether that’s OpenAI or Anthropic or whatever the case may be. But where we are playing an important role is in those agentic RAG workflows that are accessing different data sources, passing that on to the LLM to ensure accurate contextual information.
And that’s where we think we actually have a potential advantage relative to those two players. They’re much larger than us, so I can see they’re further along. But as you pointed out, we have access to all the data in an enterprise, and I think in this era of agents and AI, it’s really whoever has the most data that wins, I think, at the end of the day. And so that’s really what we provide is access to all of the data in the enterprise, not just the data in one individual lake or one individual warehouse, but all of the data.
Blocks and Files: That gives me two thoughts. One is that you must already have a vast number of connectors connecting Starburst to data sources. I imagine an important but background activity is to make sure that they’re up to date and you keep on connecting to as many data sources as possible.
Justin Borgman: That’s right.
Blocks and Files: The second one is that you are going to be, I think, providing some kind of AI pipeline, a pipeline to select data from your sources, filter it in some way. For instance, removing sensitive information and then sending it upstream, making it available. And the point at which you send it upstream and say Starburst’s work stops could be variable. For example, you select some filters, some data from various sources, and there it is sitting in, I guess, some kind of table format. But it’s raw data, effectively, and the AI models need it tokenized. They need it vectorized, which means the vectors have to be stored someplace and then they use it for training or for inference. So where does Starburst activity stop?
Justin Borgman: Everything you said is right. I’m going to quantify that a little bit. So we have over 50 connectors to your earlier point. So that covers every traditional database system you can think of, every NoSQL database, basically every database you can think of. And then where we started to expand is adding large SaaS providers like Salesforce and ServiceNow and things of that nature as well. So we have access to all those things.
You’re also correct that we provide access control across all of those and very fine grain. So row level, column level, we can do data masking and that is part of the strength of our platform, that the data that you’re going to be leveraging for your AI can be managed and governed in a very fine-grained manner. So that’s role-based and attribute-based access controls.
To address your question of where does it stop, the reason that’s such a great question is that actually in May, we’re going to be making some announcements of going a bit further than that, and I don’t want to quite scoop myself yet, but I’ll just say that I think in May you will see us doing pretty much the entire thing that you just described today. I would say we would stop before the vectorization and that’s where we stop today.
Blocks and Files: I could see Starburst, thinking we are not a database company, but we do access stored vaults of data, and we probably access those by getting metadata about the data sources. So when we present data upstream, we could either present the actual data itself, in which case we suck it up from all our various sources and pump it out, or we just use the metadata and send that upstream. Who does it? Do you collect the actual data and send it upstream or does your target do that?
Justin Borgman: So we actually do both of the things you described. First of all, what we find is a lot of our customers are using an aspect of our product that we call data products, which is basically a way of creating curated datasets. And because, as you described it, we’re this sort of virtual lakehouse, those data products can actually be assembled from data that lives in multiple sources. And that data product is itself a view across those different data sources. So that’s one layer of abstraction. And in that case, no data needs to be moved necessarily. You’re just constructing this view.
But at the end of the day, when you’re executing your RAG workflows and you’re passing data on, maybe as a prompt, to an LLM calling an LLM function, in those cases, we can be moving data.
Blocks and Files: If you are going to be possibly vectorizing data, then the vectors need storing someplace, and you could do that yourself or you could ring up Pinecone or Milvus or Weaviate. Is it possible for you to say which way you are thinking?
Justin Borgman: Your questions are spot on. I’m trying to think of what I should say here … I’ll say nothing for today. Other than that, that is a perfect question and I will have a very clear answer in about six weeks.
Blocks and Files: If I get talking to a prospect and the prospect customer says, yes, I do have data in disparate sources within the individual datacenters and across datacenters and in the public cloud and I have SaaS datasets, should I then say, go to a single lakehouse data warehouse supplier, for example, Snowflake or Databricks or something? Or should I carry on using where my data currently is and just virtually collect it together as and when is necessary with, for example, Starburst? What are the pros and cons of doing that?
Justin Borgman: Our answer is actually a combination of the two, and I’ll explain what I mean by that. So we think that storing data in object storage in a lake in open formats like Iceberg tables is a wonderful place to store large amounts of data. I would even say as much as you reasonably can because the economics are going to be ideal for you, especially if you choose an open format like Iceberg, because the industry has decided that Iceberg is now the universal format, and that gives you a lot of flexibility as a customer. So we think data lakes are great. However, we also don’t think it is practical for you to have everything in your lake no matter what. Right? It is just a fantasy that you’ll never actually achieve. And I say this partly from my own experience…
So we need to learn from our past mistakes. And so I think that the approach has to have both. I think a data lake should be a large center of gravity, maybe the largest individual center of gravity, but you’re always going to have these other data sources, and so your strategy needs to take that into account.
I think that the notion that you have to move everything into one place to be able to have an AI strategy is not one that’s going to work well for you because your data is always going to be stale. It’s never going to be quite up to date. You’re always going to have purpose-built database systems that are running your transactional processing and different purposes. So our approach is both. Does that make sense?
Blocks and Files: It makes perfect sense, Justin. You mentioned databases, structured data. Can Starburst support the use of structured data in block storage databases?
Justin Borgman: Yes, it can.
Blocks and Files: Do you have anything to do or any connection at all with knowledge graphs for representing such data?
Justin Borgman: We do have connectors to a couple of different graph databases, so that is an option, but I wouldn’t say it’s a core competency for us today.
Blocks and Files: Stepping sideways slightly. Backup data protection companies such as Cohesity and Rubrik will say, we have vast amounts of backed-up data in data stores, and we’re a perfect source for retrieval-augmented generation. And that seems to me to be OK, up to a point. If you met a prospect who said, well, we’ve got lots of information in our Cohesity backup store, we’re using that for our AI pipelines, what can you do there? Or do you think it is just another approach that’s got its validity, but it’s not good enough on its own?
Justin Borgman: From our customer base, I have not seen a use case that was leveraging Cohesity or Rubrik as a data source, but we do see tons of object storage. So we have a partnership in fact with Dell, where Dell is actually selling Starburst on top of their object storage, and we do work with Pure and MinIO and all of these different storage providers that have made their storage really S3 compatible. It looks like it’s S3, and those are common data sources, but the Cohesity and Rubriks of the world, I haven’t seen that. So I’m not sure if the performance would be sufficient. It’s a fair question, I don’t know, but probably the reason that I haven’t seen it would suggest there’s probably a reason I haven’t seen it, is my guess.
Blocks and Files: Let’s take Veeam for a moment. Veeam can send its backups to object storage, which in principle gives you access to that through an S3-type connector. But if Veeam sends its backups to its own storage, then that becomes invisible to you unless you and Veeam get together and build a connector to it. And I daresay Veeam at that point would say, nice to hear from you, but we are not interested.
Justin Borgman: Yes, I think that’s right.
Blocks and Files: Could I take it for granted that you would think that although a Cohesity/Rubrik-style approach to providing information for RAG would have validity, it’s not real-time and therefore that puts the customers at a potential disadvantage?
Justin Borgman: That’s my impression. Yes, that’s my impression.
Analysis Trump’s tariffs will affect US companies with multinational supply chain components and finished products imported to America, and products from foreign suppliers imported to the US. They will also affect US storage suppliers exporting to tariff-raising countries. There are three groups facing different tariff-related problems.
“This tariff policy would set the US tech sector back a decade in our view if it stays.
Wedbush
Wedbush financial analyst Daniel Ives is telling subscribers to brace themselves: “Investors today are coming to the scary realization this economic Armageddon Trump tariff policy is really going to be implemented this week and it makes the tech investing landscape the most difficult I have seen in 25 years covering tech stocks on the Street. Where is the E in the P/E? No one knows….what does this do to demand destruction, Cap-Ex plans halted, growth slowdown, and damaging companies and consumers globally. Then there is the cost structure and essentially ripping up a global supply chain overnight with no alternative….making semi fabs in West Virginia or Ohio this week? Building hard drive disks in Florida or New Jersey next month?”
And: “…this tariff policy would set the US tech sector back a decade in our view if it stays.”
Here is a survey of some of the likely effects, starting with a review of Trump’s tariffs on countries involved in storage product supply.
China gets the top overall tariff rate of 54 percent, followed by Cambodia on 49 percent, Laos on 48 percent, and Vietnam on 46 percent. Thailand gets a 37 percent tariff imposed, Indonesia and Taiwan 32 percent. India gets 27 percent, South Korea 26 percent, and Japan 24 percent. The EU attracts 18.5 percent and the Philippines 18 percent.
US storage component and product importers
US suppliers with multinational supply chains import components and even complete products to the US. At the basic hardware level, the Trump tariffs could affect companies supplying DRAM, NAND, SSDs, tape, and tape drives, as well as those making storage controllers and server processors.
However, ANNEX II of the of the Harmonized Tariff Schedule of the United States (HTSUS) applies to a presidential proclamation or trade-related modification that amends or supplements the HTSUS. Currently it says that semiconductors are exempt from tariffs.
Semiconductor chips are exempt but not items that contain them as components.
Micron makes DRAM, NAND, and SSDs. The DRAM is manufactured in Boise, Idaho, and in Japan, Singapore, and Taiwan. The exemption could apply to the DRAM and NAND chips but not necessarily to the SSDs that contain NAND, as there is no specific exemption for them. They face the appropriate country of origin tariffs specified by the Trump administration.
Samsung makes DRAM and NAND in South Korea with some NAND made in China. SSD assembly is concentrated in South Korea. The SSDs will likely attract the South Korea 26 percent tariff.
SK hynix makes its DRAM and NAND chips and SSDs in Korea, while subsidiary Solidigm makes its SSD chips in China, implying their US import prices will be affected by the 54 percent tariff on Chinese SSDs and a 26 percent tariff on South Korean ones.
Kioxia NAND and SSDs are made in Japan and the SSDs bought in America will attract a 24 percent tariff – which suppliers will pass on to US consumers, in part or in full. SanDisk NAND is made in Japan (with Kioxia), but we understand some of its SSDs are manufactured in China – which means a 54 percent tariff might apply. That means Kioxia SSDs, Samsung and SK hynix SSDs, but not Solidigm ones, could cost less than Sandisk SSDs.
Consider Seagate and its disk drives. It has component and product manufacturing and sourcing operations in an integrated international supply chain involving China, Thailand, Singapore, and Malaysia.
It makes disk drive platters and some finished drives – Exos, for example – in China, and spindle motors, head gimbal assemblies, and other finished drives in Thailand. Platters and some other drives are assembled in Singapore and Malaysia. Trump’s tariffs will apply to finished drives imported into the US, with rates depending on country of origin.
The tariff rates for China, Malaysia, Singapore, and Thailand are 54 percent, 24 percent, 10 percent, and 36 percent respectively. If Seagate raised its prices to US customers by the tariff amounts, the effect would be dramatic.
Western Digital will be similarly affected as it assembles its disk drives in Malaysia and Thailand, and so face tariffs of 24 and 36 percent respectively imposed on these drives.
Toshiba HDDs are made in China, the Philippines, and Japan, implying US import tariffs of 54, 18, and 24 percent respectively.
IBM makes tape drives for itself and the LTO consortium in Tucson, Arizona, so there are no Trump tariffs applying to them, only to whatever foreign-made components IBM might be importing.
LTO tape media is made by Japan’s Fujifilm and Sony. Fujifilm makes its tape in the US, in Bedford, Massachusetts, but Sony makes its tape in Japan, meaning it will get a 24 percent tariff applied to tape imports into the US. Fujifilm wins while Sony loses.
Recordable Blu-ray and DVD discs are made in China, India, Japan, and Taiwan, and will have US import tariffs imposed on them depending upon the country of origin.
Storage controllers and server processors are mostly made by Intel with some by AMD.
Intel has CPU fabs in Oregon (Hillsboro), Arizona (Chandler), and New Mexico (Rio Rancho). There are processor assembly, test, and packaging facilities in Israel, Malaysia, Vietnam, China, and Costa Rica. The Leixlip plant in County Kildare, Ireland, also produces a range of processors. This is a complex manufacturing supply chain and Intel will avoid a tariff hit on all its CPUs, and other semiconductor products because of the Annexx II exemptions above. The same applies to AMD processors and Arm chips.
Storage arrays are typically made in the US, with Dell, HPE, and NetApp all manufacturing inside the US. However, Hitachi Vantara makes storage arrays in Japan, so they will receive a 24 percent import tariff. Lenovo’s storage is mostly based on OEM’d NetApp arrays so it might share NetApp’s US country of origin status and so avoid tariffs.
Infinidat outsources its array manufacturing to Arrow Electronics, which has a global supply chain, with the US as a global hub. The actual country of origin of Infinidat’s arrays has not been publicly revealed and lawyers may well be working on its legal location.
Hitachi Vantara looks likely to be the most disadvantaged storage array supplier, at the moment.
Non-US storage suppliers
Non-US storage suppliers exporting to the US will feel the tariff pain depending upon their host country. We understand the country of origin of manufactured storage hardware products will be the determining factor.
EU storage suppliers will be affected – unless they maintain a US-based presence.
One tactic suppliers might use is to transfer to a US operation and so avoid tariffs altogether – although critics have said investing in the US at present, with construction costs up and consumer spending down, is far from a safe bet.
US storage exporters
The third group of affected storage suppliers are the US storage businesses exporting goods to countries including China, which is raising its own tariffs in response. There is now a 34 percent tariff on US goods imported into China, starting April 10. This will affect all US storage suppliers exporting there. For example, Intel, which exports x86 CPUs to China.
We understand that China’s tariffs in reaction to Trump’s apply to the country of origin of the US-owned supplier’s manufactured products and not to the US owning entity. So Intel’s US-made semiconductor chips exported to China will have the tariff imposed by Beijing, but not its products made elsewhere in the world. Thus foreign-owned suppliers exporting storage products to China from the US will have the 34 percent tariff applied but this will not apply to their goods exported to China from the rest of the world.
If other countries outside the US were to follow China’s lead and apply their own import tariffs on US-originated goods, US-based exporters would feel the pain, too.
We believe that one of the general storage winners from this tariff fight is Huawei. It doesn’t import to the US anyway, and is thus unaffected by Trump’s tariff moves. As a Chinese supplier, it is also not affected by China’s tariffs on US-made goods, unlike Lenovo if it imports its NetApp OEM’d arrays into China.
Analysis: Pure Storage has won a deal to supply its proprietary flash drive technology to Meta, with Wedbush financial analysts seeing this as “an extremely positive outcome for PSTG given the substantially greater EB of storage PSTG will presumably ship.” The implication is that hyperscaler HDD purchases will decline as a result of this potentially groundbreaking deal.
The storage battleground here is for nearline data that needs to have fast online access while being affordable. Pure says its Direct Flash Modules (DFMs), available at 150 TB and soon 300 TB capacity points, using QLC flash, will save significant amounts of rack space, power, and cooling versus storing the equivalent exabytes of data in 30-50 TB disk drives.
A Pure blog by co-founder and Chief Visionary Officer John Colgrove says: “Our DirectFlash Modules drastically reduce power consumption compared to legacy hard disk storage solutions, allowing hyperscalers to consolidate multiple tiers into a unified platform.”
He adds: “Pure Storage enables hyperscalers and enterprises with a single, streamlined architecture that powers all storage tiers, ranging from cost-efficient archive solutions to high-performance, mission-critical workloads and the most demanding AI workloads.” That’s because “our unique DirectFlash technology delivers an optimal balance of price, performance, and density.”
A Meta blog states: “HDDs have been growing in density, but not performance, and TLC flash remains at a price point that is restrictive for scaling. QLC technology addresses these challenges by forming a middle tier between HDDs and TLC SSDs. QLC provides higher density, improved power efficiency, and better cost than existing TLC SSDs.”
It makes a point about power consumption: “QLC flash introduced as a tier above HDDs can meet write performance requirements with sufficient headroom in endurance specifications. The workloads being targeted are read-bandwidth-intensive with infrequent as well as comparatively low write bandwidth requirements. Since the bulk of power consumption in any NAND flash media comes from writes, we expect our workloads to consume lower power with QLC SSDs.”
Meta says it’s working with Pure Storage “utilizing their DirectFlash Module (DFM) and DirectFlash software solution to bring reliable QLC storage to Meta … We are also working with other NAND vendors to integrate standard NVMe QLC SSDs into our datacenters.”
It prefers the U.2 drive form factor over any EDSFF alternatives, noting that “it enables us to potentially scale to 512 TB capacity … Pure Storage’s DFMs can allow scaling up to 600 TB with the same NAND package technology. Designing a server to support DFMs allows the drive slot to also accept U.2 drives. This strategy enables us to reap the most benefits in cost competition, schedule acceleration, power efficiency, and vendor diversity.”
The bloggers say: “Meta recognizes QLC flash’s potential as a viable and promising optimization opportunity for storage cost, performance, and power for datacenter workloads. As flash suppliers continue to invest in advanced fab processes and package designs and increase the QLC flash production output, we anticipate substantial cost improvements.” That’s bad news for the HDD makers who must hope that HAMR technology can preserve the existing HDD-SSD price differential.
Wedbush analysts had a briefing from Colgrove and CFO Kevan Krysler, who said that Pure’s technology “will be the de facto standard for storage except for certain very performant use cases” at Meta.
We understand that Meta is working with Pure for its flash drive, controller, and system flash drive management software (Purity). It is not working with Pure at the all-flash array (AFA) level, suggesting other AFA vendors without flash-level IP are wasting their time knocking on Meta’s door. Also, Meta is talking to Pure because it makes QLC flash drives that are as – or more – attractive than those of off-the-shelf vendors such as Solidigm. Pure’s DFMs have higher capacities, lower return rates, and other advantages over commercial SSDs.
The Wedbush analysts added this thought, which goes against Pure’s views to some extent, at least in the near-term: “We would note that while PSTG likely displaces some hard disk, we also believe Meta’s requirements for HDD bits are slated to grow in 2025 and 2026.” Flash is not yet killing off disk at Meta, but it is restricting HDD’s growth rate.
Generalizing from the Pure-Meta deal, they add: “Any meaningful shift from HDD to flash in cloud environments, should seemingly result in a higher longer term CAGR for flash bits, a result that should ultimately prove positive for memory vendors (Kioxia, Micron, Sandisk, etc.)”
Auwau provisions multi-tenant backup services for MSPs and departmental enterprise with automated billing and stats.
It is a tiny Danish firm, just three people, with a mature Cloutility software stack and highly valued and easy-to-use functionality by its 50 or so customers, which is why we’re writing about it. Auwau’s web-based software enables MSPs and companies to deliver Backup-as-a-Service (BaaS) and S3-to-tape storage as a service; Cloutility supporting IBM’s Storage Protect; and Storage Defender, Cohesity Data Protect, Rubrik and PoINT’s (S3 to tape endpoint-based) Archival Gateway. IBM is an Auwau reseller.
Thomas Bak
CEO Thomas Bak founded Auwau in Valby, Denmark in 2016, basing it around a spin-out of acquired backup-as-a-service software while he was a Sales Director and Partner at Frontsafe. Cloutility runs on a Windows machine and has a 30 min install. It doesn’t support Linux, with Bak saying he “never meets a SP who doesn’t have Windows somewhere.”
Although BaaS provisioning is a core service the nested multi-tenancy automated billing is equally important, and the two functions are controlled through a single software pane of glass. Users canactivate new backups and schedule backups from Cloutility via self service.
Bak told an IT Press Tour audience: “Multi-tenancy is a big draw [with] tenants in unlimited tree structures. … We automate subscription-based billing.” Cloutility provides price by capacity by tenant and customers can get automated billing for their tenants plus custom reporting and alerting. He said: “We sell to enterprises who are internal service providers and want data in their data centers.” On-premises and not in the cloud in other words.
Cloutility single pane of glass
Universities could invoice per department and/or by projects for example. Role-based access control, single sign-on and two-factor authentication are all supported.
Auwau offers OEM/white label branding capability so every reselling tenant of an MSP could be branded. Their recurring bills and reports will reflect this branding. An MSP can set up partners and resellers in their multi-tenant tree structure who can add and operate their own subset of customers as if the system was their own.
Development efforts are somewhat limited; there are only two engineers. Bak says Auwau will add new backup service provisioning connectors and billing when customers request them. He doesn’t have a build-it-and-they-will-come approach to product development. It’s more of a case of being able to depend upon a future cash flow from customers requesting a new BaaS offering which would spur any development.
He has Veeam support as a roadmap item but with no definite timescale. There are no plans to generate IBM COS to general S3 target capability nor support for Cohesity NetBackup.
In the USA and some other geographies N-able would be a competitor, but Bak says he never meets N-able in the field.
Bak is very agile. He finished his presentation session by doing a handstand and walking around on his hands. That will make for unforgettable sales calls.
Bootnote
IBM Storage Defender is a combination of IBM Storage Protect (Spectrum Protect as was), FlashSystem, Storage Fusion and Cohesity’s DataProtect product. This will run with IBM Storage’s DS8000 arrays, tape and networking products.
There is no restore support for IBM StorageProtect as it is not API-driven. Rubrik and Cohesity are OK for restore.
