094: GreyBeards talk shedding light on data with Scott Baker, Dir. Content & Data Intelligence at Hitachi Vantara

Sponsored By:

At Hitachi NEXT 2019 Conference, last month, there was a lot of talk about new data services from Hitachi. Keith and I thought it would be a good time to sit down and talk with Scott Baker (@Kraken-Scuba), Director of Content and Data Intelligence, at Hitachi Vantara about what’s going on with data operations these days and how customers are shedding more light on their data.

Information supply chain

Something Scott said in his opening remarks caught my attention when he mentioned customer information supply chains. The information supply chain is similar to manufacturing supply chains, but it’s all about data. Just like manufacturing supply chains where parts and services come from anywhere and are used to create products/services for customers,

information supply chains are about the data used in their organization operations. Information supply chain data is A) being sourced from many places (or applications); B) being added to by supply chain processing (or other applications); and C) ultimately used by the organization to supply a product/service to customers.

But after the product/service is supplied the similarity between manufacturing and information supply chains breaks down. With the information supply chain, data is effectively indestructible, is infinitely re-useable and can live forever. Who throws data away anymore?

The problem most organizations have with information supply chains is once the product/service is supplied, data is often put away never to be seen again or as Scott puts it, goes dark.

This is where Hitachi Content intelligence (HCI) comes in. HCI is designed to take (unstructured or structured) data and analyze it (using natural language and other processing tools) to surround it with information and other metadata, so that it can become more visible and useful to the organization for the life of its existence.

Customers can also use HCI to extract and blend data streams together, automating the creation of an information rich, data repository. The data repository can readily be searched to re-discover or uncover attributes about the data not visible before.

Scott also mentioned the Hitachi Pentaho Platform which can be used to make real time decision from structured data. Pentaho information can also be fed into HCI to provide more intelligence for your structured data.

But HCI can also be used to analyze other database data as well. For instance, database blob and text elements can be fed to and analyzed by HCI. HCI analysis can include natural language processing and other functionality to tag the data by adding key:value information, all of which can be supplied back to the database or Pentaho to add further value to structured data.

Customers can also use HCI to read and transform database tables into XML files. XML files can be stored in object stores as objects or in file systems. XML data could easily be textually indexed and be searched by various tools to better understand the structured data information

We also talked about Hadoop data that can be offloaded to Hitachi Content Platform (HCP) object storage with a stub left behind. Once data is in HCP, HCI can be triggered to index and add more metadata, which can then later be used to decide when to move data back to Hadoop for further analysis.

Finally, Keith mentioned that he just got back from KubeCon and there was an increasing cry for data being used with containerized applications. Scott mentioned HCP for Cloud Scale, the newest member of the HCP object store family, focused on scale out capabilities to provide highly consistent, object storage performance for customers that need it. Customers running containerized workloads use scale-out capabilities to respond to user demand and now they have on premises object storage that can scale with them, as needs change.

The podcast ran ~24 minutes. Scott was very knowledgeable about data workflows, pipelines and the need for better discovery tools. We had a great time discussing information supply chains and how Hitachi can help customers optimize their data pipelines. Listen to the podcast to learn more.

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Scott Baker, Director of Content and Data Intelligence at Hitachi Vantara

Scott Baker is, and has been, an active member of the information technology, data analytics, data management, and data protection disciplines for longer than he is willing to admit.

In his present role at Hitachi, Scott is the Senior Director of the Content and Data Intelligence organization focused on Hitachi’s Digital Transformation, Data Management, Data Governance, Data Mobility, Data Protection and Data Analytics solutions which includes Hitachi Content Platform (HCP), HCP Anywhere, HCP Gateway, Hitachi Content Intelligence, and Hitachi Data Protection Solutions.

Scott is a VMware Certified Professional, recognized as a subject matter expert, industry speaker, and author. Scott has been a panelist on topics related to storage, cloud, information governance, data security, infrastructure standardization, and social media topics. His educational background includes an MBA, Master’s & Bachelor’s in Computer Science.

When he’s not working, Scott is an avid scuba diver, underwater photographer, and PADI Scuba Instructor. He has a passion for public speaking, whiteboarding, teaching, and traveling the world.

93: GreyBeards talk HPC storage with Larry Jones, Dir. Storage Prod. Mngmt. and Mark Wiertalla, Dir. Storage Prod. Mkt., at Cray, an HPE Enterprise Company

Supercomputing Conference 2019 (SC19) is coming to Denver next week and in anticipation of that show, we thought it would be a good to talk with some HPC storage group. We contacted HPE and given their recent acquisition of Cray, they offered up Larry and Mark to talk about their new ClusterStor E1000 storage system.

There are a number of components that go into Cray supercomputers and besides the ClusterStor, Larry and Mark mentioned their new SlingShot cluster interconnect which is Ethernet based with significant enhancements to congestion handling. But the call focused on ClusterStor.

What is ClusterStor

ClusterStor, is a Lustre file system hardwareappliance. Lustre has always been popular with the HPC crowd as it offered high bandwidth file services. But Lustre often took a team of (PhD) scientists to configure, deploy and run properly because of all the parameters that had to be setup for optimum performance.

Cray’s ClusterStor was designed to make configuring, deploying and running Lustre a lot simpler with a GUI and system defaults that provided an optimal running environment. But if customers still want access to all Lustre features and functionality, all the Lustre parameters can still be tweaked to personalize it.

What sort of appliance

The ClusterStore team has created a Lustre storage appliance using two systems, a 2U-24 NVMe SSD system and a 4U-106 disk drive system. Both systems use PCIe Gen 4 buses which offer 2X the bandwidth of Gen 3 and NVMe Gen 4 SSDs. Each ClusterStore E1000 appliance comes with 2 servers for HA and the storage behind it.

Larry said the 2U NVMe Gen 4 appliance offers 80GB/sec of read and 60GB/sec of write data bandwidth. And a full rack of these, could support ~2.5TB/sec of data bandwidth. One TB/sec seems like an awful lot to the GreyBeards, 2.5TB/sec, out of this world.

We asked if it supported InfiniBAND interconnects? Yes, they said it supports the latest generation of InfiniBAND but it also offers Cray’s own (SlingShot) Ethernet interconnect, unusual for HPC environments. And as in any Lustre parallel file system, servers accessing storage use Lustre client software.

ClusterStor Data Services

But on the backend, where normally one would see only LDISKFS for backend storage, ClusterStor also offers ZFS. Larry and Mark said that LDISKFS is faster but ZFS offers more functionality like snapshots and data compression.

Many of the Top 100 & Top 500 supercomputing environments are starting to deploy ML DL (machine learning-deep learning) workloads along with their normal HPC activities. But whereas HPC work has historically depended on bandwidth to read, write and move large files around, ML DL deals with small files and needs high IOPS. ClusterStor was designed to satisfy both high bandwidth and high IOPS workloads.

In previous HPC Lustre flash solutions, customers had to deal with the complexity of where to place data, such as on flash or on disk. But with net ClusterStor E1000, the system can do all this for you. That is it will move data from disk to flash when it sees an advantage to doing so and move it back again when that advantage is gone. But, just as with Lustre configuration parameters above, customers can still pre-stage data to flash.

The other challenge for HPC environments is extreme size. Cray and others are starting to see requirements for Exascale (exabyte, 10**18) byte) storage systems. In fact, Cray has a couple of ClusterStor E1000 configurations of 400PB or more already, As these systems age they may indeed grow to exceed an exabyte.

With an exabyte of data, systems need to support billions of files/inodes and better metadata services and indexing. ClusterStor offers optimized inode indexing and search to enable HPC users to quickly find the data they are looking for. Further, ClusterStor offers, data at rest encryption and supports virtual file systems, for multi-tenancy.

With a ZFS backend, ClusterStor can supply data compression and snapshots. Cray has tested ZFS compression on HPC scientific ( mostly already application compressed) data and still see ~30% reduction is storage footprint. At an exabyte of storage 30% can be a significant cost reduction

The podcast ran long, ~46 minutes. Larry and Mark had a good knowledge of the HPC storage space and were easy to talk with. Matt’s an old ZFS hand, so wanted to take even more about ZFS. I had a good time discussing ClusterStor and Lustre features/functionalit and how the HPC workloads are changing. Listen to the podcast to learn more. [The podcast was recorded on November 6th, not the 5th as mentioned in the lead in, Ed.]

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Larry Jones, Director Storage Product Management

Larry Jones is a director of storage product management for Cray, a Hewlett Packard Enterprise company.

Jones previously held senior product management roles at Seagate, DDN and Panasas.

Mark Wiertalla, Director Storage Product Marketing

Mark Wiertalla is a product marketing director for Cray, a Hewlett Packard Enterprise company.

Prior to Cray, Wiertalla held product manager roles at EMC and SGI.

92: Ray talks AI with Mike McNamara, Sr. Manager, AI Solution Mkt., NetApp

Sponsored By: NetApp

NetApp’s been working in the AI DL (deep learning) space for a long time now and announced their partnership with NVIDIA DGX systems, back in August of 2018. At NetApp Insight, this week they were showing off their new NVIDIA DGX systems reference architectures. These architectures use NetApp AFF A800 storage (for more info on AI DL, checkout Ray’s Learning Machine (deep) Learning posts – part 1, – part 2 and – part3).

Besides the ONTAP AI systems, NetApp also offers

  • FlexPod AI solution based on their partnership with Cisco using UCS C480 ML M5 rack servers which include 8 NVIDA Tesla V100 GPUs and also features NetApp AFF A800 storage for use in core AI DL.
  • NetApp HCI has two configurations with 2- or 3-NVIDIA GPUs that come in 1U or 2U rack servers and run VMware vSphere or RedHad OpenStack/OpenShift software hypervisors suitable for edge or core AI DL.
  • E-series reference architecture that uses the BeeGFS parallel file system and offers InfiniBAND data access for HPC or core AI DL.

On the conference floor, NetApp showed AI DL demos for automotive, financial services, Public Sector and healthcare verticals. They also had a facial recognition application running that could estimate your age and emotional state (I didn’t try it, but Mike said they were hedging the model so it predicted a lower age).

Mike said one healthcare solution was focused on radiological image scans, to identify pathologies from x-Ray, MRI, or CAT scan images. Mike mentioned there was a lot of radiological technologists burn-out due to the volume of work caused by the medical imaging explosion over the last decade or so. Mike said image analysis is something that h AI DL can perform very effectively and doing so would improve the accuracy and reduce the volume of work being done by technologists.

He also mentioned another healthcare application that uses an AI DL app to count TB cells in blood samples and estimate the extent of TB infections. Historically, this has been time consuming, error prone and hard to do in the field. The app uses a microscope with a smart phone and can be deployed and run anywhere in the world.

Mike mentioned a genomics AI DL application that examined DNA sequences and tried to determine its functionality. He also mentioned a retail AI DL facial recognition application that would help women “see” what they would look like with different makeup on.

There was a lot of discussion on NetApp Cloud services at the show, such as Cloud Volume Services and Azure NetApp File (ANF). Both of these could easily be used to implement an AI DL application or be part of an edge to core to cloud data flow for an AI DL application deployment using NetApp Data Fabric.

NetApp also announced a new, all flash StorageGRID appliance that was targeted at heavy IO intensive uses of object store like AI DL model training and data analytics.

Finally, Mike mentioned NetApp’s ecosystem of partners working in the AI space to help customers deploy AI DL algorithms in their industries. Some of these include:

  1. Flexential, Try and Buy AI so that customers could bring them in to supply AI DL expertise to generate an AI DL application using customer data and deploy it on customer cloud or on prem infrastructure .
  2. Core Scientific, AI-as-a-Service, so that customers could purchase a service to implement an AI DL application using customer data and running on Core Scientific infrastructure..
  3. Scale Matrix, Mobile data center AI, so that customers could create an AI DL application and run it on Scale Matrix infrastructure that was transported to wherever the customer wanted it to be run.

We recorded the podcast on the show floor, in a glass booth, so there’s some background noise (sorry about that, but can’t be helped). The podcast is ~27 minutes. Mike is a long time friend and NetApp product expert, recently working in AI DL solutions at NetApp. When I saw Mike at Insight, I just had to ask him about what NetApp’s been doing in the AI DL space. Listen to the podcast to learn more.

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Mike McNamara, Senior Manager AI Solution Marketing, NetApp

With over 25 years of data management product and solution marketing experience, Mike’s background includes roles of increasing responsibility at NetApp (10+ years), Adaptec, EMC and Digital Equipment Corporation. 

In addition to his past role as marketing chairperson for the Fibre Channel Industry Association, he was a member of the Ethernet Technology Summit Conference Advisory Board, a member of the Ethernet Alliance, and a regular contributor to industry journals, and a frequent speaker at events.

85: GreyBeards talk NVMe NAS with Howard Marks, Technologist Extraordinary and Plenipotentiary, VAST Data Inc.

As most of you know, Howard Marks was a founding co-Host of the GreyBeards-On- Storage podcast and has since joined with VAST Data, an NVMe file and object storage vendor headquartered in NY with R&D out of Israel. We first met with VAST at StorageFieldDay18 (SFD18, video presentation). Howard announced his employment at that event. VAST was a bit circumspect at their SFD18 session but Howard seems to be more talkative, so on the podcast we learn a lot more about their solution.

VAST Data is essentially an NFS-S3 object store, scale out solution with both stateless, VAST Data storage servers and JBoF drive enclosures with Optane and NVMe QLC SSDs. Storage servers or JBoFs can be scaled independently. They don’t support tiering or DRAM caching of data but instead seem to use the Optane SSDs as a write buffer for the QLC SSDs.

At the SFD18 event their spokesperson said that they were going to kill off disk storage media. (Ed’s note: Disk shipments fell 18% y/y in 1Q 2019, with enterprise disk shipments at 11.5M units, desktop at 24.5M units and laptops at 37M units).

The hardware

The VAST Data storage servers are in a 2U/4 server configuration, that runs interface protocols (NFS & S3), data reduction (see below), data reformating/buffering etc. They are stateless servers with all the metadata and other control state maintained on JBoF Optane drives.

Each drive enclosure JBoF has 12 Optane SSDs and 44 U.2 QLC (no DRAM/no super cap) SSDs. This means there are no write buffers on the QLC SSDs that can lose data when power failures occur. The interface to the JBoF is NVMeoF, either RDMA-RoCE Ethernet or InfiniBand (customer selected). Their JBoFs have high availability, with dual fabric modules that support 2-100Gbps Ethernet/InfiniBand ports per module, 4 per JBoF.

Minimum starting capacity is 500TB and they claim support up to Exabytes. Although how much has actually been tested is an open question. They also support billions of objects/files.

Guaranteed better data reduction

They have a rather unique, multi-level, data reduction scheme. At the start, data is chunked in variable length chunks. They use heuristics to determine the chunk size that fits best. (Ed note, unclear which is first in this sequence below so presented in (our view of) logical order)

  • 1st level computes a similarity hash (56 bit not SHA1), which is used to determine a similarity level with any other currently stored data chunk in the system.
  • 2nd level uses a ZSTD compression algorithm. If a similarity is found, the new data chunk is compressed with the ZSTD compression algorithm and a reference dictionary used by the earlier, similar data chunk. If no existing chunk is similar to this one, the algorithm identifies a semi-unique reference dictionary that optimizes the compression of this data chunk. This semi-unique dictionary is stored as metadata.
  • 3rd level, If it turns out to be a complete duplicate data chunk, then the dedupe count for the original data chunk is incremented, a pointer is saved to the original unique data and the data discarded. If not a complete duplicate of other data, the system computes a delta from the closest “similar’ block and stores just the delta bytes, includes a pointer to the original similar block and increments a delta block counter.

So data is chunked, compressed with a optimized dictionary, be delta-diffed or deduped. All data reduction is done post data write (after the client is ACKed), and presumably, re-hydrated after being read from SSD media. VAST Data guarantees better data reduction for your stored data than any other storage solution.

New data protection

They also supply a unique Locally Decodable Erasure Coding with 4 parity (-like) blocks and anywhere from 36 (single enclosure leaving 4 spare u.2 SSDs) to 150 data blocks per stripe all of which support up to 4 device failures per stripe. 

The locally decodable erasure coding scheme allows for rebuilds without having to read all remaining data blocks in a stripe. In this scheme, once you read the 4 parity (-like) blocks, one has all the information calculated from up to ¾ of the remaining drives in the stripe, so the system only has to read the remaining ¼ drives in the stripe to reconstruct one, two, three, or four failing drives.  Given their data stripe width, this cuts down on the amount of data needing to be read considerably. Still with 150 data drives in a stripe, the system still has to read 38 drives worth of QLC SSD data to rebuild a data drive.

In addition to all the above, VAST Data also reblocks the data into much larger segments, (it writes 1MB segments to the QLC drives) and uses a heat map along with other heuristics to separate actively written data from less actively written data, thus reducing garbage collection, write amplification.

The podcast is a long and runs over ~43 minutes. Howard has always been great to talk with and if anything, now being a vendor, has intensified this tendency. Listen to the podcast to learn more.

Howard Marks, Technologist Extraordinary and Plenipotentiary, VAST Data, Inc.

Howard Marks brings over forty years of experience as a technology architect for hire and Industry observer to his role as VAST Data’s Technologist Extraordinary and Plienopotentary. In this role, Howard demystifies VAST’s technologies for customers and customer requirements for VAST’s engineers.

Before joining VAST, Howard ran DeepStorage an industry test lab and analyst firm. An award-winning speaker, he has appeared at events on three continents including Comdex, Interop and VMworld.

Howard is the author of several books (all gratefully out of print) and hundreds of articles since Bill Machrone taught him journalism at PC Magazine in the 1980s.

Listeners may also remember that Howard was a founding co-Host of the Greybeards-on-Storage Podcast.


80: Greybeards talk composable infrastructure with Tom Lyon, Co-Founder/Chief Scientist and Brian Pawlowski, CTO, DriveScale

We haven’t talked with Tom Lyon (@aka_pugs) or Brian Pawlowski before on our show but both Howard and I know Brian from his prior employers. Tom and Brian work for DriveScale, a composable infrastructure software supplier.

There’s been a lot of press lately on NVMeoF and the GreyBeards thought it would be good time to hear from another way to supply DAS like performance and functionality. Tom and Brian have been around long enough to qualify as greybeards in their own right.

The GreyBeards have heard of composable infrastructure before but this was based on PCIe switching hardware and limited to a rack or less of hardware. DriveScale is working with large enterprises and their data center’s full of hardware.

Composable infrastructure has many definitions but the one DriveScale probably prefers is that it manages resource pools of servers and storage, that can be combined, per request, to create any mix of servers and DAS storage needed by an application running in a data center. DriveScale is targeting organizations that have from 1K to 10K servers with from 10K to 100K disk drives/SSDs.

Composable infrastructure for large enterprises

DriveScale provides large data centers the flexibility to better support workloads and applications that change over time. That is, these customers may, at one moment, be doing big data analytics on PBs of data using Hadoop, and the next, MongoDB or other advanced solution to further process the data generated by Hadoop.

In these environments, having standard servers with embedded DAS infrastructure may be overkill and will cost too much. For example., because one has no way to reconfigure (1000) server’s storage for each application that comes along, without exerting lots of person-power, enterprises typically over provision storage for those servers, which leads to higher expense.

But if one had some software that could configure 1 logical server or a 10,000 logical servers, with the computational resources, DAS disk/SSDs, or NVMe SSDs needed to support a specific application, then enterprises could reduce their server and storage expense while at the same time provide applications with all the necessary hardware resources.

When that application completes, all those hardware resources could be returned back to their respective pools and used to support the next application to be run. It’s probably not that useful when an enterprise only runs one application at a time, but when you have 3 or more running at any instant, then composable infrastructure can reduce hardware expenses considerably.

DriveScale composable infrastructure

DriveScale is a software solution that manages three types of resources: servers, disk drives, and SSDs over high speed Ethernet networking. SAS disk drives and SAS SSDs are managed in an EBoD/EBoF (Ethernet (iSCSI to SAS) bridge box) and NVMe SSDs are managed using JBoFs and NVMeoF/RoCE.

DriveScale uses standard (RDMA enabled) Ethernet networking to compose servers and storage to provide DAS like/NVMe like levels of response times.

DriveScale’s composer orchestrator self-discovers all hardware resources in a data center that it can manage. It uses an API to compose logical servers from server, disk and SSD resources under its control available, throughout the data center.

Using Ethernet switching any storage resource (SAS disk, SAS SSD or NVMe SSD) can be connected to any server operating in the data center and be used to run any application.

There’s a lot more to DriveScale software. They don’t sell hardware. but have a number of system integrators (like Dell) that sell their own hardware and supply DriveScale software to run a data center.

The podcast runs ~44 minutes. The GreyBeards could have talked with Tom and Brian for hours and Brian’s very funny. They were extremely knowledgeable and have been around the IT industry almost since the beginning of time. They certainly changed the definition of composable infrastructure for both of us, which is hard to do. Listen to the podcast to learn more. .

Tom Lyon, Co-Founder and Chief Scientist

Tom Lyon is a computing systems architect, a serial entrepreneur and a kernel hacker.

Prior to founding DriveScale, Tom was founder and Chief Scientist of Nuova Systems, a start-up that led a new architectural approach to systems and networking. Nuova was acquired in 2008 by Cisco, whose highly successful UCS servers and Nexus switches are based on Nuova’s technology.

He was also founder and CTO of two other technology companies. Netillion, Inc. was an early promoter of memory-over-network technology. At Ipsilon Networks, Tom invented IP Switching. Ipsilon was acquired by Nokia and provided the IP routing technology for many mobile network backbones.

As employee #8 at Sun Microsystems, Tom was there from the beginning, where he contributed to the UNIX kernel, created the SunLink product family, and was one of the NFS and SPARC architects. He started his Silicon Valley career at Amdahl Corp., where he was a software architect responsible for creating Amdahl’s UNIX for mainframes technology.

Brian Pawlowski, CTO

Brian Pawlowski is a distinguished technologist, with more than 35 years of experience in building technologies and leading teams in high-growth environments at global technology companies such as Sun Microsystems, NetApp and Pure Storage.

Before joining DriveScale as CTO, Brian served as vice president and chief architect at Pure Storage, where he focused on improving the user experience for the all-flash storage platform provider’s rapidly growing customer base. He also was CTO at storage pioneer NetApp, which he joined as employee #18.

Brian began his career as a software engineer for a number of well-known technology companies. Early in his days as a technologist, he worked at Sun, where he drove the technical analysis and discussion on alternate file systems technologies. Brian has also served on the board of trustees for the Anita Borg Institute for Women and Technology as well as a member of the board at the Linux Foundation.

Brian studied computer science at Arizona State University, physics at the University of Texas at Austin, as well as physics at MIT.