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Storage news ticker – December 9

By
Chris Mellor
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Storage news
Storage news

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Data protector and manager Cohesity has announced a new head of EMEA channels: James Worrall. He was previously VP of EMEA channel at F5 Networks and EMEA channel sales lead at Juniper Networks.

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DDN announced “dramatically improved performance” in the STAC-M3 Benchmark with its newly announced A3I AI400X2 appliance. The benchmark test was performed on a single DDN A3I AI400X2 all-flash NVMe appliance connected to 15 servers running the KX Systems kdb+4.0 database in distributed mode via a high-speed InfiniBand switch. DDN was able to exceed 10 of 17 baseline (Antuco) benchmark tests and 19 of 24 scaling (Kanaga) benchmarks, compared to the previous tests using two DDN AI400X appliances. It was able  to beat a test system using kdb+ 4.0 on 9x Dell EMC PowerEdge R640 servers accessing a Dell EMC PowerScale F900 All-Flash, 3-node cluster (tick analytics) and another test system using kdb+ 4.0 on WekaFS and AWS under STAC-M3 (tick analytics).

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SaaS data protector Druva announced its new Managed Service Provider (MSP) program, built on Amazon Web Services (AWS), has tripled its partner numbers — but no actual numbers have been revealed. This program, announced in June, provides partners with a SaaS-based delivery model with a zero-touch onboarding experience and no infrastructure to maintain. The Druva Managed Services Center is available to MSPs in North America and EMEA.

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Oceanscan, an international equipment supplier to the oil and gas, defence, petrochemical, renewables, and nuclear industries, recovered from a ransomware attack by using iland Secure Cloud DRaaS for Veeam and Secure Cloud Backup for Veeam Cloud Connect. Sukumar Panchanathan, group IT manager at Oceanscan, said: “We were in a position to recover with just the click of a button.” 

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We hear file lifecycle management company Komprise is hiring like crazy.

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Marvell announced it will ship its five billionth hard disk drive (HDD) controller next month. Its suite of Bravera HDD controller and preamplifier products leverage the company’s IP, including over 1,500 HDD-related patents and 18 generations of Read Channel IP.

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Kubernetes storage and platform supplier Ondat announced v2.5 of its software. It can synchronise data across different datacentres to provide high availability, along with failover disaster recovery. Ondat has added faster replica synchronisation — more than two times faster with even higher speeds on high-latency networks. Another new feature is execution within kubectl making it easy for administrators to continue using their existing DevOps toolset without disruption. Also, the Kubernetes operator has been rewritten to maintain consistency with the latest changes to Kubernetes.

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Quantum has updated its VS-HCI Series Acuity software, acquired from Pivot 3. V10.9 includes:

  • Orchestration Manager enhancements including automated system health assessment prior to and after an upgrade, one-click online and automated upgrades, and monitoring of upgrade status and system availability via integration with Acuity phone home capabilities.
  • 50 per cent faster drive rebuild performance with new Quick Drive Rebuild capability, which complements the previously introduced Quick Node Rebuild capability. 
  • More disk drive analytics for greater detection of potential disk drive issues that can impact system performance.
  • Additional embedded artificial intelligence (AI) that monitors and analyses disk drive performance and provides enhanced predictive drive sparing for optimum system availability and performance.
  • Support of VMware ESXi 7, including hypervisor and vCentre Server 7, along with updates to Quantum’s vCenter Server plug-in that provides management of the VS-HCI Series from the vCentre console enabling an organisation’s VMware administrators to easily manage the video surveillance infrastructure.

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Qumulo has added NFS v4.1 support to its Qumulo Core software in v4.3.0. Customers will be able to delegate fine-grained permissions control to end-users. Qumulo offers cross protocol permission (XPP) support to automatically manage complex permissions across protocols. It claims this NFS v4.1 support enables the creation of the largest multi-protocol file namespace possible for active storage compared with any competing platform or any cloud service provider (CSP) offering.

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Synology announced its FlashStation FS2500 product, delivering more than 170,000/82,000 4K random read/write performance, and features dual 10Gbit ports with room for 10/25Gbit expansion. The FS2500 has a 1U chassis and 12x 2.5-inch SATA bays. Its DiskStation Manager (DSM) operating system provides automatic drive repair, storage deduplication, and snapshots coupled with remote backup capabilities. Synology has also announced SAT5210 — enterprise-grade SATA SSDs for Synology servers. Synology FlashStation FS2500 and SAT5210 SATA SSDs are available through Synology resellers and partners

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Toshiba spread its 18TB FC-MAMR disk drive technology from its MG09 drive to an MN09 8-bay NAS drive with a 1.2 million hour MTBF rating at the 16 and 18TB levels (1 million hours for 6, 8, 10, 12 and 14TB), a 512MB cache and the same 268MiB/sec transfer rate, 6Gbit/sec SATA interface and 7,200rpm spin speed as the MG09.

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Active data replicator WANdisco has won an inaugural IoT (Internet of Things) contract with a large European automotive components supplier. The contract is valued at $6 million over five years, the client committing to a minimum of $100K of revenue per month for five years. The automotive components supplier is committed to replicating a minimum of 60PB per month, with potential for expansion.

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ReRAM startup Weebit Nano is on the greenwashing march. It and nanotechnology research institute CEA-Leti are analysing the environmental impact of Weebit’s ReRAM tech compared to other non-volatile memory (NVM) technologies. The Life Cycle Assessment (LCA) will estimate the contribution of Weebit’s NVM technology to climate change by quantifying its environmental impact. It will focus on total greenhouse gas (GHG) emissions associated with the uses of mineral, fossil and water resources, energy consumption and gases/chemistry involved with the development, manufacture and operation of the technology.

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One week into Q1 in its fiscal 2022 year WekaIO has pulled in an eight-figure order for its scale-out, parallel filesystem software, meaning a $10 million or more deal.

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Ocient’s massively parallel relational database speed machine

By
Chris Mellor
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A brief news note yesterday said startup Ocient (pronounced oh-see-ent) enabled SS8 Networks to harness petabytes of data in interactive time. Ocient is focussed on ingesting billions of rows per second, and filtering and computing aggregate results at rates up to trillions of rows per second.

Rows? As in relational databases? What was this about? We all know and understand that unstructured data volumes are rising like an ever-growing tsunami, but structured data? In a relational database with trillions of rows? Is that growing too?

Our curiosity piqued, we took a closer look at this startup — realising as we did that there had been no public discussion of how its technology worked in anything but the most bland and uninformative terms.

Chris Gladwin.

Ocient was founded in 2018 by CEO Chris Gladwin, who previously founded object storage supplier CleverSafe (which was acquired by IBM for $1.3 billion in October 2015) along with chief product officer Joe Jablonski, and chief architect George Kondiles. Several ex-CleverSafe execs are in the management team.

Ocient software

The aim of starting Ocient was to build database and analytics software to enable fast analysis of exabyte-scale datasets. The founders wanted to develop a new relational database and data analytics solution (DAS) using industry-standard interfaces such as SQL, JDBC and ODC, and commodity hardware. Funding rounds followed the classic tech formula: tech demo, prototype and then deliverable v1.0 product, with self-funding to a $10 million A-round in 2018, a kind of extended A-round for $15 million in 2020 and, this year a $40 million B-round — $65 million in all. 

The developed Ocient DAS can, the company says, hold quadrillions of rows of data, ingest billions of rows per second, and filter and compute trillions of rows per second. Imagine a relational database holding quadrillions of rows of data — that’s 1,000 trillion. How many customers would need to consider buying that big a database? 

The DAS software can be deployed on commodity hardware or in the public cloud, uses massively parallel processing on large core-count systems and is claimed to be 1,000 times faster than leading MPP, NoSQL and Hadoop-based databases when querying a large dataset (with same hardware, queries and data). Ocient says analytics that once took an hour now take 10 seconds or less.

Joe Jablonski.

The DAS system benchmarks at five to 1,000 times faster — typically around 50 times faster — than current high-performance alternatives like MPP, NoSQL, Hadoop-based databases and open source Presto. It requires 20 per cent of the data storage footprint of these alternatives. Ocient says its software supports standard ANSI SQL including aggregates, joins and count distinct through its JDBC, ODBC, and Spark connectors. There is also a Tableau connector.

Performance data

Apart from the claim it is faster than MPP, NoSQL, Hadoop and Presto, there is no solid performance data enabling a comparison against Oracle, Snowflake or any other proprietary named product.

There is no detailed public configuration data and no technology backgrounder or white paper and no consultancy review of the product, such as an ESG validation, and certainly no published benchmark data with test configurations. We know from Ocient’s website that the DAS hardware is industry-standard and utilises NVMe SSDs and 100Gbit/sec networking — but that’s all.

After four years of development Ocient is a black box. So we tried to shine a bit of light into it.

Let’s envisage a 100PB DAS database and imagine how Oracle would configure such a system, with its scale-out Exadata hardware and software.

An Exadata X9M-2 is made up of database machine and storage expansion racks. The database machine part contains database servers (2–19) and storage servers (3–18). There are up to 1,216 CPU cores and 38TB memory per rack for database processing, and up to 576 CPU cores per rack dedicated to SQL processing. 

It uses a 100Gbit/sec RoCE network and up to 27TB of persistent memory. An X9M-2 can hold up to 920TB of NAND per rack and up to 3.8PB of disk per rack — 4.7PB in total. A 100PB Exadata X9M-2 system would then need 21.3 standard racks.

Based on this we believe the Ocient DAS uses a scale-out node architecture employing tens of racks, scaling up to hundreds. It will use NVMe SSDs and, possibly, Optane drives, for RDB metadata, and nearline disk drives for the actual data. The DAS database and analytics software will be distributed across a sea of nodes — tens or even hundreds of them — and we envisage these as Exadata-type nodes, combining SQL processing and storage servers with souped up software.

Patent

We found a US patent, number 10,754,856, attributed to George Kondiles and Jason Arnold of Ocient, and entitled “System and method for optimising large database management systems using bloom filter.”

George Kondiles.

Its abstract states: “A large highly parallel database management system includes thousands of nodes storing huge volume of data. The database management system includes a query optimiser for optimising data queries. The optimiser estimates the column cardinality of a set of rows based on estimated column cardinalities of disjoint subsets of the set of rows. For a particular column, the actual column cardinality of the set of rows is the sum of the actual column cardinalities of the two subsets of rows. The optimiser creates two respective Bloom filters from the two subsets, and then combines them to create a combined Bloom filter using logical OR operations. The actual column cardinality of the set of rows is estimated using a computation from the combined Bloom filter.”

“Column cardinality” is a measure of the number of unique values in a database table column relative to the number of rows in the table. A Bloom filter is a probabilistic data entity used to test if a data element is in a data set, and it says whether the element is definitely not in the set or possibly in it.

We note the “thousands of nodes” phrase with interest.

Where we’re at

We think Ocient is developing its software in conjunction with large potential customers. It has 16 people across two advisory boards — a relatively large number in our view — and we think these may be being used to help pull in test customers as well as to offer advice.

The company has a VP Of Global Sales and Marketing, Kumar Abhijeet, and a COO, Bill McCarthy. These two positions inform us that it is talking to potential customers and taking in money, or very close to being ready to do so.

We believe it has a capable and benchmarked prototype system, and is building a v1.0 product. Once that is built and proved to deliver the claimed goods, then it will unlock C-round funding, in the 2022/23 timeframe, and a big go-to-market push.

Ocient could be a huge success if: its scalability to exabyte-size datasets is real and unique; its performance advantage over Oracle, Snowflake, etc., is real; and there are sufficient customers who want to analyse exabyte-sized relational databases. Gladwin has a great track record, with CleverSafe. Watch this Ocient space to see if he can do it again.

Pure takes the high road with throughput-optimised FlashArray models

By
Chris Mellor
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Pure Storage has added Ice Lake Xeon grunt to two new high-end FlashArray models built with TLC (3bits/cell) NAND and the PCIe 4 bus, overall delivering up to 80 per cent more throughput than the current top end //X90 FlashArray.

Updated. 10 Dec 2021. Table corrected to show chassis RU sizes for//X90. QLC flash mention removed.

There are two new models — the //XL130 and //XL170 — both with a 5RU base chassis, smaller than the //X90’s 6U enclosure (see note 1 below). They have up to 68 per cent more capacity than the //X90, up to 68 per cent more performance, near-80 per cent more IOPS, and 20 per cent better rack density. A Pure announcement said they have been designed for mission-critical, platinum-tier enterprise applications, from massive databases to containerised and cloud-native apps. 

Shawn Hansen, VP and GM, FlashArray at Pure Storage, said: “FlashArray//XL is a clear shot across the bow of legacy storage vendors by combining high-end scale of a true enterprise-class array with the scale-out agility of the cloud operating model.”

The FlashArray//XL bezel features less plastic than existing FlashArray bezels, helping to contribute in its small way to customers’ ESG improvements.

Pure Storage has recently emphasised its software credentials and focus, but the //XL models shows that its proprietary hardware roots are flourishing. Commenting on the new models, Pure’s International CTO Alex McMullan said: “The hardware team is still ruthlessly excellent at what they do.” 

A table compares the new and existing FlashArray products:

McMullan wouldn’t supply IOPS numbers or Xeon core counts for the new models but gave a percentage core count increase relative to the //X90: 50 per cent for the XL130 and 100 per cent for the XL170. The new models perform faster, helped by the PCIe 4 bus — twice as fast as PCIe 3 — with 18 slots compared to the //X90’s 12x PCIe 3 slots. That’s a 50 per cent increase in PCIe slot numbers plus a doubling in PCIe speed.

The XL models have portions of SLC NAND amongst the TLC flash, and these negate any need for separate NVRAM modules. In effect, the Direct Flash modules, with these SLC sections, have distributed on-board NVRAM.

All in all, this is significantly enhanced hardware — in compute performance, in capacity and in throughput. The //XL130 and 170, like the//X90, have latency as low as 150μs and Pure says they can be used for the most-demanding, mission-critical workloads, such as ones using SAP/HANA, Oracle, SQL Server and VMware. 

A Pure storage cluster can now handle more work in fewer racks, saving electrical energy needed for power and cooling, and the Pure Fusion control plane provides a public cloud-like operating model on top of that.

There are Standard, Performance, Premium and Ultra storage classes in Pure Fusion, with the existing FlashArray//X models placed in the Performance, Premium and Ultra classes and FlashArray//XL only in the Ultra class:

Even the power supplies have been upgraded, with four of them in a N+2 configuration rather than the previous N+1 arrangement.

Asked if Dell EMC’s PowerMax was the main competitor for the new models, McMullan demurred. “There’s no primary competitor right now,” he said, adding: “FlashArray//XL moves beyond PowerMax in terms of manageability.”

Purity software upgrade

A new release of the Purity operating software, v6.2, has SafeMode set as the default, meaning always-on data protection. SafeMode provides settable 24- hour to 30-day retention periods for snapshots of volumes, Pods, Protection Groups and files. The eradication timer, used to set a limit for retention, is now tamper-proof.

Specific volumes and volume groups can be pinned in the DMM (DirectMemory Module) cache, an Optane SSD cache, and up to 6TB of DMM capacity is supported. The software can support twice as many vVOLs with the //XL models as the //X90 and there is a higher volume count. In fact  the vVols and volume increases in Purity 6.2 apply to all //X and //XL models. Replication performance is boosted by 50 per cent over the //X90 with the XL models. When ActiveDR is ongoing, app performance can be prioritised over replication tasks.

The FlashArray//XL products are available now.

Note 1. The //X90 is 3U by itself, and only grows to 6U with one expansion shelf. So the comparable //XL130 is 8U.

Storage news ticker – December 7

By
Chris Mellor
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Data migrator moving into data management Datadobi has released new Starter Packs for DobiMigrate ranging from 1PB up to 7PB. DobiMigrate Starter Packs were originally designed for the lower end of migrations — up to 500TB — to make it easier for customers to get started on their projects. These new Starter Packs will assist customers with larger environments to do the same.

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Delphix and Unisys are supplying California State University and its 23 campuses with quick and secure access to data to support student application development. Unisys says it enables CSU to leverage the Delphix DevOps Data Platform to accelerate and simplify its Hybrid-Architecture enablement. The Delphix software provides the ability to spin up, refresh, and tear down private and public cloud-based data environments easily. The CSU can create a unified and secure data lake, populated daily with data from all 23 campuses, for data analysts, data scientists, and developers. Using Delphix and Unisys, CSU has saved over $4.5 million per year and avoided future costs of over $7 million per year in data storage expenditures.

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Komprise announced expanded support for new Amazon Web Services (AWS) file services to accelerate petabyte-scale file data migrations to the cloud, while enabling the use of native AWS data analytics and machine learning (ML) services. Komprise supports Amazon FSx for NetApp ONTAP, enabling cloud replication and cloud data migration from any network-attached storage (NAS) to Amazon FSx for NetApp ONTAP. It supports AWS Snowball for data migraion to AWS. It will support the new Amazon S3 tier and class — Amazon S3 Glacier Instant Retrieval — early next year. 

Komprise enables ingestion of files and object data into AWS AI/ML and data lake services by providing a Global File Index so you can search across all your Amazon S3 buckets and file storage. Komprise Deep Analytics Actions policies then systematically ingest just the right data into native AWS AI, ML, and data lake services.

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Micron is setting up a new memory (DRAM and NAND) design centre in Midtown Atlanta, Georgia. It’s scheduled to open in January 2022. The centre will create up to 500 jobs across various STEM disciplines including computer hardware and electrical and electronic engineering.

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Email storage and security supplier Mimecast is being taken private by Permira in an all-cash deal at an equity value of $5.8 billion. Peter Bauer, Mimecast chairman and CEO, said: “Permira has a strong track record of collaboratively supporting companies’ growth ambitions and strategic goals, and we look forward to working together to further strengthen the cybersecurity and resilience of organisations around the world. This is a great outcome for our company and our shareholders.” Mimecast shareholders will receive $80 in cash for each ordinary share they own. The transaction is expected to close in the first half of 2022, subject to customary closing conditions.

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SS8 Networks, which supplies Lawful Intercept, Lawful Intelligence and Monitoring Center platforms today has collaborated with data analytics supplier Ocient whose massively parallel software enables customers to harness petabytes of data in interactive time. Ocient is focussed on ingressing billions of rows per second, and filtering and computing aggregate results at rates up to trillions of rows per second. Its co-founders are CEO Chris Gladwin (Cleversafe founder), chief product officer Joe Jablonski, and chief architect George Kondiles.

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Rewind has launched Backups for Microsoft 365 — an automated backup and data recovery tool which backs up Exchange, SharePoint, OneDrive for Business, and Groups & Teams. Rewind says this is is another step forward in its journey to backing up the entire cloud and becoming the leading data recovery provider for SaaS solutions. This announcement comes after Rewind raised a $65 million Series B round of financing in September. Rewind currently provides backup and restoration software for BigCommerce, GitHub, QuickBooks Online, Shopify, Shopify Plus, and Trello, and plans to launch Backups for Jira in 2022.

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Storage Made Easy (SME) is partnering PoINT Software & Systems to integrate SME’s Enterprise File Fabric software with PoINT’s Storage Manager tiering and archive software. Files and objects in the SME Enterprise File Fabric can be moved to a PoINT archive according to set policies. Users also access the archived data transparently via the Enterprise File Fabric, regardless of whether the data is stored on a local file server or in the cloud. 

SME and PoINT Software & Systems diagram.

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StorMagic announced today that its SvSAN hyperconverged vSAN software and Commvault Backup & Recovery software have been validated with HPE’s server portfolio, ranging from Edgeline to ProLiant. HPE servers running SvSAN protected by Commvault software can replicate workloads on the edge, back to core, or to the cloud, while restoring to any infrastructure. The combo system, which includes two HPE servers, Commvault Backup & Recovery and StorMagic SvSAN, is available from HPE channel partners. 

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WekaIO has developed a scalable, parallel, high-performance file system offering with HPE for AI and ML workloads. The validated system is available in HPE’s Ezmeral marketplace. This is in addition to the existing HPE ProLiant validated Weka software systems available from HPE. Ezmeral Runtime Enterprise is a Kubernetes platform supporting cloud-native and non-cloud-native apps in on-premises, public cloud and hybrid environments. Weka+Ezmeral provides software for shipping data fast to AI and ML apps in the Ezmeral environment. Weka can be found in the HPE Ezmeral Marketplace here.

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Amazon migrates offline tape data to Glacier with Snowball Edge VTL-in-a-box

By
Chris Mellor
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Amazon Web Services suitcase-sized Snowball Edge box can now collect ingest data from tape cartridges and be shipped to an AWS centre for file import into Glacier object storage.

The idea is to offer a tape data migration service via a shipped disk drive container rather than by sending the tape cartridges direct to Amazon or networking the data. 

AWS chief evangelist Jeff Barr has written a blog outlining the process. ”We are taking another step forward by making it easier for you to migrate data stored offline on physical tapes. You can get rid of your large and expensive storage facility, send your tape robots out to pasture, and eliminate all of the time and effort involved in moving archived data to new formats and mediums every few years, all while retaining your existing tape-centric backup and recovery utilities and workflows.”

AWS Snowball Edge device.

Sending tape data across a network link can take much, much longer than the time needed for AWS to despatch a Snowball Edge device to a customer, have it filled with data and then returned to AWS. 

For example, transmitting 80TB of data — the Snowball Edge tape import limit — across a WAN link can take about 41 days if sent across a 200Mbit/sec link, and around 82 days if the link speed is halved. Then there are the network costs to take into account, plus any latency issues.

By having the customer copy the files from tape and import them into the Edge box, AWS can be sure that the tape can be read, the data is in the right format, and the customer deals with any data ingest problems.

Storage gateway software is set up on the Snowball Edge box and the tape files read in as if coming into virtual tape files in a virtual tape library (VTL). A example in Barr’s blog shows tape-held backups from Veeam Backup being moved across into the Edge box. We don’t know what other backup file formats are supported. The data is tagged to be stored in S3 Glacier Flexible Retrieval or Glacier Deep Archive storage classes.

The net result is that an online tape gateway and VTL is set up in AWS for customers. Access can be from backup and recovery software running either on-premises or in the public cloud. The on-premises route requires the VTL to run in a virtual machine or hardware appliance.

Snowball Edge can be used for offline tape migration in the US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Europe (Ireland), Europe (Frankfurt), Europe (London), Asia Pacific (Sydney) AWS regions. 

Comment

The big issue with AWS Glacier is egress costs. But, if restores are done infrequently, that cost will be manageable. If you get hit by a ransomware attack or disaster and have to do a bulk restore back on premises then your level of cost and time-taken unhappiness could be quite high. But you could use AWS’s reverse Snowball service to have the Glacier-held data returned to you.

Rubrik builds its own ransomware threat hunter

By
Chris Mellor
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Rubrik has updated its data protection software with an in-house ransomware threat hunter, a Cloud Vault SaaS archival service built on Azure, extended source data coverage, and faster backups and recoveries.

Threat hunting software scans backup files looking for ransomware attack patterns or signatures to identify corrupted backups, and suppliers such as Check Point Software Technologies offer it. Rubrik has developed its own threat-hunting capability and its announcement says it enables customers to “more accurately identify the last known clean copy of data in order to prevent reinfection during and after recovery.” 

Dan Rogers, president of Rubrik, said in a statement: “Ransomware attacks pose an increased danger to every business around the world, regardless of industry. [Cloud Vault] is a key milestone in our commitment to integrate Rubrik Data Security capabilities with Microsoft to deliver Zero Trust Data Security to global enterprise customers.” 

Rubrik’s threat hunting capabilities integrate with Palo Alto Networks’ Cortex XSOAR threat hunting playbooks for easier identification of compromised data within backup snapshots. This should help in post-incident reviews and reporting to external regulatory agencies. 

A set of Rubrik blogs discuss this winter software update.

The new software release also includes:

  • Globally-enforced multi-factor authentication (MFA) across the software to repel unauthorised users. 
  • An expanded Sensitive Data Discovery service to include some 60 pre-defined analysers that automatically identify and classify more data types, including certain types of Personal Identifiable Information (PII).
  • Protection for Azure SQL with Fully-Managed SaaS Support — Expanded Rubrik coverage in Azure cloud ensures Azure SQL can be secured alongside other cloud and on-premises workloads for unified visibility and streamlined policy management. Cloud Vault enables customers to survive cyber-attacks and avoid ransom payments by maintaining both immutable and instantly recoverable copies of critical data in a secured and isolated cloud location, using Azure Blob storage, fully-managed by Rubrik.
  • Reduced Blast Radius with Archives for AWS S3 — In the event of an AWS production account being compromised by ransomware, cloud data can be recovered through a bunkered account with new credentials and limited access and deletion rights. 
  • Low-cost daily snapshots for Azure VMs and AWS EC2 instances can save customers up to 40 per cent in cloud fees.
  • Expanded protection for Microsoft 365 with up to 100,000 users, and restores for Exchange contacts and calendars, SharePoint lists and Teams channel posts.
  • Protection for SAP/HANA Databases on IBM Power Systems, reducing a need for manual scripting and job scheduling across on-premises and the cloud. 
  • Faster recovery of Oracle and Microsoft SQL databases with a large number of files. SQL customers can see up to 3x improvement in restores and Oracle customers can see up to 25 per cent improvement for database recoveries.
  • Quicker backups for Nutanix AHV by excluding selected non-critical individual disks from a backup to free up time and storage. The entire Nutanix AHV backup can be sent over a separate and isolated iSCSI Data Services network to optimise network bandwidth and provide an extra security layer. 

Rubrik Cloud Vault will be generally available globally in the coming months on the Azure Marketplace. Rubrik’s latest release is expected to become available in the coming months through its partner network. The integration with Cortex XSOAR will also be available through the Cortex XSOAR Marketplace.

Compose yourself: Liqid gets unicorn-class funding boost

By
Chris Mellor
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Composable systems startup Liqid has had a massive $100 million funding round to take its datacentre do-more-with-less message mainstream.

The company sells composable systems software and hardware with which users separate or disaggregate server, networking and storage resources into pools, and construct dynamic server configurations with accurately sized components from the pools to run applications. They are torn down afterwards and the components returned to the resource pools for re-use. Liqid has found success in the high-performance market with several prominent customers.

Co-founder and CEO Sumit Puri provided a statement: “Liqid is excited to announce this funding to drive extended hypergrowth as software-defined infrastructure solutions like ours quickly become mainstream alongside the expansion of next-generation workloads like AI.”

Liqid was started up in 2015 and has had four funding events (see chart), with the previous one being a $28 million B-round in 2019. 

The founders were Puri, the original CEO Jay Breakstone who left in April 2018, COO Bryann Schramm, CTO J. Scott Cannata and Supply Chain VP Sandeep Rao. Its technology is based around its CDI (Composable Disaggregated Infrastructure) orchestration software which composes server CPUs+DRAM, GPUs, FPGAs, NVMe SSDs, networking, and storage-class memory using the PCIe bus (generations 3 and 4) as its connectivity, to create software-defined, bare metal servers on demand. 

Liqid founders. Left to right: J. Scott Canata, Sumit Puri, Bryan Schramm and Sandeep Rao.

The CDI software includes the Matrix fabric and a Liqid Command Centre facility.

The justification for composing systems is that individual resource class utilisation — such as CPU, GPU, NVMe SSDs, etc. — will be higher in its composed systems than in a fixed configuration where resources are sized and costed for the largest-possible workload, meaning wasted resources when smaller workloads can’t run.

Customers include the US Army Corps of Engineers with a $20.6 million supercomputer deal, the Electronic Visualisation Lab at the University of Illinois, and two DoD contracts In August 2020, valued at about $32 million for the US High Performance Computing Modernisation Program.

Liqid has a partnership with Dell whereby Dell’s MX7000 composable system can have GPUs, FPGAs and NVMe storage added to it via the CDI software and a PCIe extension chassis. It has another partnership with Western Digital to include its openFlex drive systems in the CDI environment. It also works with VMware as vCentre users can compose server systems using Liqid’s Matrix fabric software via a plug-in.

In 2021 Liqid more than doubled its workforce, achieved a 100 customer count and recorded 1,034 per cent revenue growth from 2017 to 2020. Stats such as this would have emboldened the VCs to pump more money into the company to get it growing faster.

It is also firmly located in the reduce-climate-change-effects camp, saying its products provide 2–3x improvements in datacentre utilisation rates compared to legacy fixed config architectures. Its Matrix sotware can, it claims, significantly reducing energy consumption and save millions of litres of cooling water and tonnes of global CO2 emissions per year.

New funding

The new funding round was co-led by Lightrock and affiliates of DH Capital, with participation from current investors Panorama Point Partners and Iron Gate Capital. We asked Liqid if it is now valued at $1 billion or more — meaning it’s a unicorn — but a spokesperson said: “Unfortunately we do not discuss our valuation.”

We think that $100 million VC rounds come at a 10:1 or greater valuation and Liqid is a unicorn.

It will use the cash to:

  • Expand internationally;
  • Grow its salesforce; 
  • Enhance the support function;
  • Increase awareness; 
  • Provide global marketing support for additional OEM and channel partners; 
  • Strengthen its operations team to scale across all business functions;
  • Accelerate the software development roadmap.

Above all it wants to maintain its claimed market leadership position.

Liqid has already started building out its business infrastructure, hiring Tintri’s EMEA boss Paul Silver as its VP and GM for the EMEA region last month.

Comment

Liqid software runs in a server and composes PCIe-connected server component resources. Competitors include Fungible ($311 million funding), with its own data processing unit (DPU) chip and network fabric, and GigaIO ($19.25 million in funding), another PCIe-focussed supplier. Funding-wise GigaIO is a round behind Liqid and Fungible, who are both on C-rounds, and its investors surely need to pony up to prevent GigaIO being left behind.

Liqid’s PCIe focus positions it for using the CXL bus as that comes into play when the base PCIe 5 link is introduced. It can already compose so-called Liqid Memory (DRAM + Optane) so composing external CXL-linked DRAM pools should be no problem.

A looming challenge/opportunity is how to compose SmartNICs — plug-in server offload cards that run a mix of network, storage and security functions for servers. Fungible offers its own version of a SmartNIC with its Storage Initiator cards. It would seem logical for Liqid to support Nvidia’s BlueField 2 and 3 SmartNICs and rely on Nvidia market momentum to blunt Fungible’s charge into the enterprise datacentre.

Storage news ticker – December 6

By
Chris Mellor
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Now that Backblaze is publicly owned it is getting covered by financial analysts. William Blair’s Jason Ader says its “Computer Backup and B2 services are built on top of its proprietary Storage Cloud — a highly redundant, low-cost datacentre architecture that allows it to charge prices well below the competition yet maintain healthy gross margins (around 75 per cent gross margin excluding depreciation).” He states “DC forecasts a 27 per cent CAGR for the public cloud storage market, reaching $91 billion in 2025, with roughly 60 per cent of this spend expected to come from midmarket customers (Backblaze’s target market). Given the size of the opportunity and the increasing centrality of data to businesses of all sizes, we expect the cloud storage market to support multiple winners.”

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Box revenues in its third FY2022 quarter were $224 million, up 14 per cent year-on-year. There was a loss of $13.8 million, a 162.3 per cent increase on the year-ago $.3 million loss. Aaron Levie, co-founder and CEO, said: “Our strong third quarter results show the continued momentum of our long-term growth strategy, as more customers are turning to the Box Content Cloud to deliver secure content management and collaboration built for the new way of working.” It was the third consecutive quarter of accelerating revenue growth. Box raised its revenue outlook for FY2022. It seems that Starboard Value’s activist investor involvement is producing positive results.

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Arrow Electronics is adding Commvault’s Metallic DMaaS (data management-as-a-service) Backup and Recovery solutions to ArrowSphere, its cloud management platform. Metallic delivers scalable, affordable backup and recovery of data stored in on-premises, the cloud, and hybrid environments, with coverage that includes Microsoft 365 and Dynamics 365, Salesforce, VMs, containers, databases, and file and object data, as well as laptops and desktops. ArrowSphere helps channel partners to manage, differentiate and scale their cloud business. Its marketplace includes all the leading hyperscale providers, as well as public and private IaaS, PaaS, SaaS, HaaS and cloud software offerings.

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HPE has just announced the SimpliVity 325 Gen10 Plus v2 offering, its latest and AMD-based hyperconverged system with stronger data protection features and InfoSight enhancements. The system uses AMD’s 3rd generation EPYC 7003 Series processor. The  SimpliVity 4.1.1 software features additional data protection features and end-to-end ransomware protection through integration with HPE’s StoreOnce (immutability), Cloud Bank Storage (object storage on-premises or in the cloud), and Zerto (DR). There’s more information in an HPE blog by its storage experts team.

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Quest has announced the availability of its Toad Data Point 5.6 software — a cross-platform, self-service, data preparation tool to retrieve and work with data from disparate sources. Analysts can connect to and query multiple data sources, including Snowflake, in order to bring data together for reporting rather than relying on separate spreadsheets. They can prepare their own data sets and reports without relying on IT, and also securely share dynamic data sets with colleagues.

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Microfluidic channel SlipChip DNA storage is glacially slow

By
Chris Mellor
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Chinese scientists have stored and retrieved data in DNA on a single electrode instead of using complicated chemical laboratory equipment for synthesising and manipulating DNA in the storage process. This is an enormous advance in DNA storage equipment size and convenience, yet the data rates are excruciatingly slow. 

The Southeastern University research team’s paper was published in Science Advances and entitled “Electrochemical DNA synthesis and sequencing on a single electrode with scalability for integrated data storage.”

The paper’s abstract talks about “the synthesis and sequencing of DNA on a single electrode with scalability for an integrated DNA-based data storage system.” A key advance was using a so-called SlipChip, a microfluidic device to hold the DNA chemicals and the various reagents.

There are two plates or layers in a SlipChip, with the lower plate having holes or wells in it to hold the chemical liquids or microdroplets. The top plate acts as a lid for the lower plate’s wells but it also has its own wells and ducts. By moving or “slipping” the top plate one or more of its wells can be lined up via ducts with wells in in the bottom plate and liquids in the wells can then react via the ducts.

No pumps or valves are needed to move the chemicals and bring them into contact with each other. A single SlipChip can be an electrode and its electrical charge altered by the presence or absence of DNA sequences. The paper’s abstract states: “The synthesis of DNA is based on phosphoramidite chemistry and electrochemical deprotection. The sequencing relies on charge redistribution originated from polymerase-catalyzed primer extension, leading to a measurable current spike.”

The SlipChip device simplifies the liquid introduction involved in DNA synthesis and sequencing. This is because current DNA storage methods “usually involve complicated liquid manipulations in each step and manual operations in between. Adding one phosphoramidite nucleotide monomer in the synthesis step generally requires the introduction of at least four kinds of liquid solutions, not to mention the sequencing step. These limit the scale-up capability of this technique and increase the error probability.” Data error likelihood requires data redundancy — extra data in other words. Random access capabilities can mean adding address tags to the DNA sequences “which can become markedly cumbersome with scaling up” and contribute to data loss due to possible biased amplification.

The equipment needed is large, cumbersome and the process requires many steps, often manual and hence error-prone. The Chinese researchers effectively invented a DNA synthesis storage and retrieval system on a chip, a SlipChip, using a gold electrode.

Binary data was encoded into the quaternary (A, T, C and G) DNA base sequences which are then synthesised into DNA. A sequencing process is used to read the data. All of the liquid manipulations for the synthesis and sequencing are accomplished with the SlipChip.

Schematic illustration of the principle of the SlipChip device for integrated DNA synthesis and sequencing. Four phosphoramidite nucleotide monomers for synthesis (or four dNTPs ( deoxyribonucleoside triphosphate) for sequencing) are preloaded in the reservoirs. A washing solution and other reagents are introduced using the fluidic channel. Liquid manipulation is accomplished by sliding the top plate.

The researchers wrote the Southeastern University motto — “Rest in the highest excellence!” — in the DNA and then read it back with acceptable accuracy: 87.22 per cent. Error correction based on data redundancy added in the encoding step served to achieve 100 per cent.

Here, for us at Blocks & Files, is the kicker: “It took about nine hours to write and read 4.5 bytes of data on the single electrode, which can be further improved by scaling up using an electrode array.” That’s 0.5 bytes/hour.

The researchers “developed a SlipChip-based microfluidic device for integrated DNA-based data storage. Briefly, a 2×2 Au electrode array was fabricated on a glass slide by standard photolithography and physical vapour deposition. A block of polydimethylsiloxane (PDMS) with a fluidic channel and reservoirs was fabricated and then assembled with the glass slide. The reservoirs were preloaded with reagents for DNA synthesis and sequencing on the electrode.”

The data writing process is fascinating. “To initiate the DNA synthesis, we slipped the top PDMS relative to the bottom glass slide to expose the electrodes to phosphoramidite nucleotide monomers in the reagent reservoirs. Then, washing, oxidation, and electrochemical deprotection steps were sequentially conducted by aligning the electrodes with corresponding reagent reservoirs and the fluidic channel, respectively.”

Reading the data via DNA synthesis involved deoxyribonucleoside triphosphate (dNTP). “Sequencing on the same electrode was accomplished by incubating the electrodes in the polymerase solution in the fluidic channel and then sequentially exposing the electrode to four reservoirs containing different dNTPs. During the process, the current from the electrode was measured for sequencing.”

Overall accuracy was 89.17 per cent, which was bumped up to 100 per cent by error correction through data redundancy again.

Integrated data storage based on an electrode array. (A) Photograph of the SlipChip device with a fluidic channel (orange) and reagent reservoirs (blue) on the top PDMS plate as well as a 2×2 Au electrode array on the bottom glass slide. Scale bar: 5mm. (B) Photographs showing the DNA synthesis process using the SlipChip device. Phosphoramidite coupling, washing, oxidation, and deprotection steps were performed by aligning the reagent reservoirs or fluidic channel with the electrodes, respectively. For electrochemical deprotection, a potential was applied to the electrodes using a CHI900D workstation. Scale bars: 5mm. (C) Surface densities of the synthesized DNA on microfabricated electrodes of two sizes (d = 260 and 500μm) and a commercial 2mm disk electrode. (D) Current signals and corresponding peak area/charge (Q) for sequencing. Photo credit: Chengtao Xu, Southeast University.

The researchers found it took about 14 hours to write and read 20 bytes of data on the four-electrode array. That’s 1.43 bytes/hour — better than 0.5 bytes/hour but still devastatingly slow.

Western Digital’s latest 20TB Gold disk drives transfer data at 269MB/sec — 269 million bytes a second. Uprate this to a per minute rate and then a per hour rate and we arrive at a speed that is 677 billion times faster than the SlipChip DNA storage process. If we start thinking about NAND storage, then a Toshiba FL6 writes data at 5,800 MB/sec. That means it’s upward of 14 trillion times faster than the SlipChip 4-electrode system.

It seems to us that, although DNA storage has potentially a fantastically high storage density — up to 455EB per gram — its speed is catastrophically slow compared to disk and worse still compared to NAND. So much so that it could well prevent DNA storage ever becoming a general commercial storage mechanism, despite its estimated million-year-plus lifespan. 

Intel building tri-partner SmartNIC/IPU ecosystem around Oak Springs Canyon

By
Chris Mellor
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Intel is partnering a Chinese server vendor, a Chinese network system supplier and an Israeli server NIC supplier to push its Oak Springs Canyon IPU server offload technology into the market.

The IPU, or Infrastructure Processing Unit, is Intel’s term for a SmartNIC — a plug-in server card that handles low-level network, security and storage tasks, relieving the server CPU so it can focus on application processing. Intel publicised two IPU technologies back in August: Oak Springs Canyon and Mount Evans. The former is based on an Agilex FPG linked to a Xeon-D CPU with16-cores supporting 2x 100Gbit links, while the latter is an ASIC-based IPU on a chip that supports 4x 100Gbit links.

Now Intel says it is collaborating on Oak Springs Canyon-based IPUs with server supplier Inspur, network systems vendor Ruijie Networks and NIC supplier Silicom. Patrick Dorsey, a VP  in Intel’s Programmable Solutions Group, provided a somewhat bland statement: “Collaborating with these innovative partners extends the secure and programmable benefits of the IPU platform to a broader set of customers.”

Intel Oak Springs Canyon diagram.

Well, yes, indeed. But, apart from Inspur, which is a huge server and cloud service vendor, the others are less well known and don’t have the impact of, say, an Arista or Juniper or even Cisco. Also, Mellanox has snaffled Dell EMC and HPE server interest with its BlueField SmartNICs and the VMware Project Monterey to have the vSphere hypervisor run on BlueField.

Nevertheless, Intel is Intel and has an awesome presence in the server market so these three partnerships — coming four months after Oak Springs Canyon was first revealed — are good signs of progress.

The Oak Springs Canyon IPU supports OVS (Open Virtual Switch), NVMe over Fabrics and RoCE, and has a hardware cryptographic function to enhance security. Inspur will design and build IPU offerings using Oak Springs Canyon. So too will Ruijie Networks, with 1Gbit and 200Gbit options, and Silicom, but with 200Gbit links.

The SmartNIC/IPU plug-in card market is a segment of the general data processing unit (DPU) market, with vendors not yet agreeing on how and where SmartNICs, IPUs and DPUs overlap. A common attribute is that they all offload host x86 server processors, but they can also help develop a secure DPU-based networking fabric connecting general application processors and specific processors — such as GPUs and AI-focussed processors — with external storage resources.

It is not yet clear how a composable datacentre infrastructure would aid in the dynamic set-up and tear-down of servers — either CPU and/or GPU and AI-processor based ones — network and storage resources to such a DPU-based fabric. Fungible, an Intel DPU competitor, has its own vision of how this will take place. 

Were Intel able to integrate its IPUs on Xeon dies, then that might give it a convincing future answer to plug-in card SmartNICs and competitor IPUs: re-integrate the offload functions in silicon attached to the Xeon cores.

Intel is holding a virtual FPGA technology event from December 6 to 9, and will, it says, showcase its FPGAs, SmartNICs and IPUs with presentations and developer-focussed webinars.

First mile data, petabyte-scale observability pipelines explained

By
Chris Mellor
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Blocks & Files received an announcement from a startup called Calyptia that discussed data observability pipelines for petabytes of first mile data from thousands of servers. Bless. We hadn’t a clue what Calyptia was on about, and so we asked it.

Calyptia’s co-founder, Anurag Gupta, kindly answered our (no doubt to him very basic) questions.

Anurag Gupta.

Blocks & Files: I know that storage systems receive incoming data — write I/O. I know that containerised apps can write data to storage. I know about block, file and object data. But what is “first mile data”? What are data observability pipelines?

Anurag Gupta: “First mile” is about the journey of solving the challenges of collecting and preparing applications and infrastructure data to be delivered to its final destination. If we talk about this journey, consider a straight line from left to right, where in the left all the data collection happens. But it’s not as easy as it sounds: data comes from different sources and in different formats. If your final goal is to perform data analysis, there are a lot of tasks that need to be done before that — like data sanitization, context enrichment and most of the cases “bring the business logic to the data”. 

When talking about a data pipeline, it means this journey of collecting, pre-processing but also offers the ability to route the data to the desired destination. 

I would love the equivalent of a Wikipedia entry for an almost-clever 12-year-old explaining what’s going on here. It could say what log data is. How does it differ from other data — like a mail message, file, database record, or a stored object?

In this context, log data refers to system generated data that records “either events that occur in an operating system or other software runs — or messages between different users of a communication software.”  And I followed your suggestion by taking that definition from Wikipedia 🙂

An example of log data could be I’m using a phone app. As I browse the app there is data generated that shows what buttons I might click or what action is taking place — this data is log data as it records what’s going on around the application. It does not include the actual email, or content that I am interacting with in the app. 

This log data is essential for systems owners to understand what is happening in their environment and how to fix problems or optimise performance. Historically, because the amount of data is so large, the challenge has been how to quickly and efficiently parse and analyse the data to uncover any valuable insight within all the noise. Solutions have evolved to address this issue, but they have traditionally required expensive, time-consuming, data transport and centralised analysis and reporting. While they do provide some value, they cannot scale to support the current explosion in log data created from an equal explosion of distributed devices across every organisation’s environment. 

Calyptia diagram.

Why is it important? What can people learn from it that makes it necessary to have special software to deal with it?

It’s about data hygiene. With the explosion of data over the past decade, and now within Kubernetes, customers need the ability to spin up an endless number of compute environments in a matter of seconds — meaning more services, more applications, and more data load. Additionally, IoT means more devices in more places all creating data and needing to be monitored, maintained, and secured. All of this means that it is getting more and more challenging to observe and understand what is going on in one’s environment. It becomes difficult to determine what is working and what is not, and what is secure and what is not, and the cost and time needed to stay ahead of the data becomes exorbitant.  

Here’s a little history. Back in 2011, at Treasure Data, we saw that there was an inherent problem with taking many sources of data, machine data, network data application data, and sending that to many backends — whether that’s Splunk, Elastic, Data Dog, Amazon or Google services. What typically happens is you have these proprietary ways to send data to one back end with one or two sources, and there’s no way to multiplex or make that very flexible for many sources in many datas. And that’s really where the Fluent projects with Treasure Data came about. Fluentd was created 10 years ago in June of 2011, and then Fluent Bit was created in 2015. 

Our goal today with Calyptia is to give customers a single, vendor-neutral solution to send data from multiple locations to multiple destinations, to do it in an open source accessible way with a plug-in ecosystem, and do it with very high performance, very full fidelity transformations parsing filtering routing, etc.

What is first mile (as opposed to second and third etc. mile) data?

First mile observability is the initial analysis of event data created by an organisation’s applications, networks, and machines. In a typical process, after creation, that data would flow through their IT systems via data pipelines such as Kafka or Confluent, and eventually end up in back-end data observability systems such as Splunk, Datadog, Elastic, or New Relic, where it is then processed and stored. “First mile” refers to that first step in this process — where data such as log files or performance metrics is created and collected.

Calyptia screen shot.

What is an observability pipeline and why is it needed?

For us, it’s really about developers and practitioners being able to get insights about their systems, being able to diagnose, troubleshoot, respond, and become proactive about all of it. The earlier in the process one is able to gain insight, the quicker they can respond and the better their systems will perform — and as a result the business as a whole will benefit.

Why would you need to gather petabytes of first mile data across thousands of servers per day? What’s the reason for doing this?

Say I’m a large bank that is running thousands of servers a day to support all my users’ transaction needs. In a traditional observability system I will know when a server goes down or if something in my app malfunctions. However, with a strong first mile observability strategy, you can also understand if the data that is generated is being collected properly. You can understand what sources of data might be ballooning up your data charges. You can understand how data is flowing, and if it is going to the right destinations.

This problem compounds as we add infrastructure like Kubernetes, cloud workloads, ephemeral deployments, and additional backends. 

Comment

Now we understand. First mile data is log data — metadata — about what system components are doing and how they are performing. Because the complexity and granularity of systems is increasing at a high rate — with thousands of servers each running thousands of containers in unbelievably complicated overall systems — diagnosing what is going on if there is a problem is difficult. So getting all that data from multiple sources in one place through a pipeline becomes critical. Then you can observe it, filter it and analyse it. Step forward Calyptia.

Storage news ticker – December 3

By
Chris Mellor
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An AWS blog contains a great summary of re:Invent 2021 announcements.

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Amazon DevOps Guru for RDS helps developers using Amazon Aurora databases to detect, diagnose, and resolve database performance issues fast and at scale. AWS says devs will have enough information to determine the exact cause for a database performance issue. This will save them potentially many hours of work diagnosing and fixing performance-related database issues.

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Amazon Kinesis Data Streams is an on-demand, capacity mode for streaming data, a fully-managed, serverless service for real-time processing of streamed data at a massive scale. AWS states “Shards are the way to define capacity in Kinesis Data Streams. Each shard can ingest 1MB/sec and 1,000 records/sec and egress up to 2MB/sec. You can add or remove shards of the stream using Kinesis Data Streams APIs to adjust the stream capacity according to the throughput needs of their workloads.” This ensures that producer and consumer applications don’t experience any throttling.

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AWS Redshift Serverless is in public preview and enables customers to just load their structured and semi-structured data across data warehouses, operational databases, and data lakes and start querying it with Redshift without having to set up and manage Redshift server clusters. The software automatically provisions and scales the right compute resources for the job. Customers pay for the time in seconds when their data warehouse is in use, but not when it is idle.

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AWS has added S3 Event Notifications. These are sent to a defined destination when changes are made to an S3 bucket. Amazon states this makes it “easier for developers to build applications that react quickly and efficiently to changes in S3 objects. Moreover, developers no longer need to make additional copies of objects or write specialised, single-purpose code to process events.”

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AWS announced two new storage-optimised EC2 instances using the Graviton2 Arm-powered processors. It said “Both instances offer up to 30TB of NVMe storage using AWS Nitro SSD devices … custom-built by AWS … [The] Nitro SSDs reduce I/O latency by up to 60 per cent and also reduce latency variability by up to 75 per cent when compared to the third generation of storage-optimised instances. As a result you get faster and more predictable performance for your I/O-intensive EC2 workloads.” 

There are Im4gn instances for applications that require large amounts of dense SSD storage and high compute performance, but are not especially memory-intensive. The s4gen instances are for for applications that do large amounts of random I/O to large amounts of SSD storage. This includes shared file systems, stream processing, social media monitoring, and streaming platforms. Details are available here.

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We hear that Delphix CFO Steward Grierson is leaving after a six-year, nine-month run to join a publicly quoted company. Previously he was at ArcSight and then Coraid. A statement from Delphix CEO and founder Jedidiah Yueh read: “After a successful career at Delphix, Stewart Grierson has made a planned transition to join another company. We have an open search for a CFO, and our VP of Finance, Steve Carbone, will serve as interim CFO. We have a large market opportunity and our mission remains the same: to help businesses radically accelerate application innovation with our secure DevOps Data Platform.” 

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NetApp TV sure has a lot of content:

NetApp TV landing page.

The newsreel stuff seems to be two-minute short items, headlines sentences only. Others are longer — “Test drive next generation AI infrastructure to accelerate time to insight” is a 47-minute video. Take a look and see what you think.

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