Data Management Archives - Page 10 of 16

The Heart of Digital Transformation is …

By cfheoh | June 13, 2019 - 6:30 am |June 13, 2019 Algorithm, Analytics, API, Artificial Intelligence, Backup, Business Continuity, Data Archiving, Data Availability, Data Management, Data Security, Digital Transformation, Edge Computing, High Performance Computing, Hyperconvergence, Industry 4.0, Machine Learning, Software-defined Datacenter, Virtualization

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Businesses have taken up Digital Transformation in different ways and at different pace. In Malaysia, company boardrooms are accepting Digital Transformation as a core strategic initiative, crucial to develop competitive advantage in their respective industries. Time and time again, we are reminded that Data is the lifeblood and Data fuels the Digital Transformation initiatives.

The rise of CDOs

In line with the rise of the Digital Transformation buzzword, I have seen several unique job titles coming up since a few years ago. Among those titles, “Chief Digital Officer“, “Chief Data Officer“, “Chief Experience Officer” are some eye-catching ones. I have met a few of them, and so far, those I met were outward facing, customer facing. In most of my conversations with them respectively, they projected a front that their organization, their business and operations have been digital transformed. They are ready to help their customers to transform. Are they?

Tech vendors add more fuel

The technology vendors have an agenda to sell their solutions and their services. They paint aesthetically pleasing stories of how their solutions and wares can digitally transform any organizations, and customers latch on to these ‘shiny’ tech. End users get too fixated that technology is the core of Digital Transformation. They are wrong.

Missing the Forest

As I gather more insights through observations, and more conversations and more experiences, I think most of the “digital transformation ready” organizations are not adopting the right approach to Digital Transformation.

Digital Transformation is not tactical. It is not a one-time, big bang action that shifts from not-digitally-transformed to digitally-transformed in a moment. It is not a sprint. It is a marathon. It is a journey that will take time to mature. IDC and its Digital Transformation MaturityScape Framework is spot-on when they first released the framework years ago.

IDC Digital Transformation Maturityscape

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Whither HPC, HPE?

By cfheoh | May 26, 2019 - 8:07 pm |May 26, 2019 Acquisition, Analytics, Artificial Intelligence, Big Data, Business Continuity, Cloud, Cloudian, Cohesity, Commvault, Cray Inc, Data Archiving, Data Direct Networks, Data Management, Data Protection, Datera, Deep Learning, Filesystems, High Performance Computing, HPE, HPE Simplivity, Hyperconvergence, Machine Learning, NAS, Nexenta, Object Storage, Qumulo, Scale-out architecture, Server SAN, Simplivity, Tintri, WekaIO, Zerto

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HPE is acquiring Cray Inc. Almost 3 years ago, HPE acquired SGI. Back in 2017, HPE partnered WekaIO, and invested big in the latest Series C funding of WekaIO just weeks ago.

Cray, SGI and WekaIO are all strong HPC technology companies. Given the strong uptick in the HPC market, especially commercial HPC, we cannot deny HPE’s ambition to become the top SuperComputing and HPC vendor in the industry. Continue reading →

Did Cloud Kill LTFS?

By cfheoh | May 21, 2019 - 10:39 am |May 21, 2019 Acquisition, Backup, Business Continuity, Cloud, Data, Data Archiving, Data Availability, Data Domain, Data Management, Data Protection, Deduplication, DellEMC, Disks, EMC, ExaGrid, Falconstor, Filesystems, HDS, HPE, IBM, LTO, NAS, NetApp, Object Storage, Quantum Corporation, Strongbox, Tape, Tapes, Veritas

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I like LTFS (Linear Tape File System). I was hoping it would take off but it has not. And looking at its future, its significance is becoming less and less relevant. I look if Cloud has been a factor in the possible demise of LTFS in the next few years.

What is LTFS?

In a nutshell, Linear Tape File System makes LTO tapes look like a disk with a file system. It takes a tape and divides it into 2 partitions:

Index Partition (XML Index Schema with file names, metadata and attributes details)
Data Partition (where the data resides)

Diagram from https://www.snia.org/sites/default/orig/SDC2011/presentations/tuesday/DavidPease_LinearTape_File_System.pdf

It has a File System module which is implemented in supported OS of Unix/Linux, MacOS and Windows. And the mounted file system “tape partition” shows up as a drive or device.

Assassination attempts

There were many attempts to kill off tapes and so far, none has been successful.

Among the “tape-killer” technologies, I think the most prominent one is the VTL (Virtual Tape Library). There were many VTLs I encountered during my days in mid-2000s. NetApp had Alacritus and EMC had Clariion Disk Libraries. There were also IBM ProtecTIER, FalconStor VTL (which is still selling today) among others and Sepaton (read in reverse is “No Tapes’). Sepaton was acquired by Hitachi Data Systems several years back. Continue reading →

Scaling new HPC with Composable Architecture

By cfheoh | May 10, 2019 - 4:50 pm |May 10, 2019 100Gigabit Ethernet, Analytics, API, Appliance, Artificial Intelligence, Big Data, Cloud, Clusters, Composable Infrastructure, Containers, Data Fabric, Data Management, Deep Learning, DellEMC, Drivescale, High Performance Computing, Hyperconvergence, Infiniband, Liqid, Machine Learning, nVidia, NVMe, PCIe, RDMA, Scale-out architecture, Software Defined Storage, Software-defined Datacenter, Tech Field Day, Unified Storage, Virtualization

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[Disclosure: I was invited by Dell Technologies as a delegate to their Dell Technologies World 2019 Conference from Apr 29-May 1, 2019 in the Las Vegas USA. Tech Field Day Extra was an included activity as part of the Dell Technologies World. My expenses, travel, accommodation and conference fees were covered by Dell Technologies, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Deep Learning, Neural Networks, Machine Learning and subsequently Artificial Intelligence (AI) are the new generation of applications and workloads to the commercial HPC systems. Different from the traditional, more scientific and engineering HPC workloads, I have written about the new dawn of supercomputing and the attractive posture of commercial HPC.

Don’t be idle

From the business perspective, the investment of HPC systems is high most of the time, and justifying it to the executives and the investors is not easy. Therefore, it is critical to keep feeding the HPC systems and significantly minimize the idle times for compute, GPUs, network and storage.

However, almost all HPC systems today are inflexible. Once assigned to a project, the resources pretty much stay with the project, even when the workload processing of the project is idle and waiting. Of course, we have to bear in mind that not all resources are fully abstracted, virtualized and software-defined whereby you can carve out pieces of the hardware and deliver a percentage of that resource. Case in point is the CPU, where you cannot assign certain clock cycles of CPU to one project and another half to the other. The technology isn’t there yet. Certain resources like GPU is going down the path of Virtual GPU, and into the realm of resource disaggregation. Eventually, all resources of the HPC systems – CPU, memory, FPGA, GPU, PCIe channels, NVMe paths, IOPS, bandwidth, burst buffers etc – should be disaggregated and pooled for disparate applications and workloads based on demands of usage, time and performance.

Hence we are beginning to see the disaggregated HPC systems resources composed and built up the meet the diverse mix and needs of HPC applications and workloads. This is even more acute when a AI project might grow cold, but the training of AL/ML/DL workloads continues to stay hot

Liqid the early leader in Composable Architecture

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Connecting ideas and people with Dell Influencers

By cfheoh | May 10, 2019 - 8:54 am |May 10, 2019 Analytics, Artificial Intelligence, Big Data, Big Switch Networks, Cloud, Composable Infrastructure, Data Management, Data Privacy, Data Protection, Data Security, Deep Learning, DellEMC, High Performance Computing, Hyperconvergence, Kemp Technologies, Liqid, Machine Learning, Microsoft, Software-defined Datacenter, Tech Field Day, VMware

AI Tweetup

In the razzmatazz, the most memorable moments were one of the Tweetups organized by Dr. Konstanze Alex (Konnie) and her team, and Tech Field Day Extra.

Tweetup was alien to me. I didn’t know how the concept work and I did google tweetup before that. There were a few tweetups on the topics of data protection and 5G, but the one that stood out for me was the AI tweetup.

Continue reading →

Dell go big with Cloud

By cfheoh | May 2, 2019 - 12:23 am |May 2, 2019 Acquisition, Amazon Web Services, API, Appliance, Artificial Intelligence, Backup, Cloud, Data Domain, Data Management, Data Protection, Data Security, Dell, DellEMC, Edge Computing, High Performance Computing, Hyperconvergence, Industry 4.0, Intel, IoT, Machine Learning, Software Defined Storage, Software-defined Datacenter, Tech Field Day, Virtualization, VMware

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[Disclaimer: I have been invited by Dell Technologies as a delegate to their Dell Technologies World 2019 Conference from Apr 29-May 1, 2019 in the Las Vegas USA. My expenses, travel and accommodation are covered by Dell Technologies, the organizer and I am not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Talk about big. Dell Technologies just went big with the Cloud.

The Microsoft Factor

Day 1 of Dell Technologies World 2019 (DTW19) started with a big surprise to many, including yours truly when Michael Dell, together with Pat Gelsinger invited Microsoft CEO, Satya Nadella on stage.

There was nothing new about Microsoft working with Dell Technologies. Both have been great partners since the PC days, but when they announced Azure VMware Solutions to the 15,000+ attendees of the conference, there was a second of disbelief, followed by an ovation of euphoria.

VMware solutions will run native on Microsoft Azure Cloud. The spread of vSphere, VSAN, vCenter, NSX-T and VMware tools and environment will run on Azure Bare Metal Infrastructure at multiple Azure locations. How big is that. Continue reading →

Lift and Shift Begone!

By cfheoh | April 25, 2019 - 5:30 pm |April 25, 2019 Amazon, Amazon Web Services, Cloud, Composable Infrastructure, Data Availability, Data Fabric, Data Management, High Performance Computing, Hyperconvergence, Mellanox Technologies, NetApp, NVMe, Server SAN, Software Defined Storage

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I am excited. New technologies are bringing the data (and storage) closer to processing and compute than ever before. I believe the “Lift and Shift” way would be a thing of the past … soon.

Data is heavy

Moving data across the network is painful. Moving data across distributed networks is even more painful. To compile the recent first image of a black hole, an amount of 5PB or more had to shipped for central processing. If this was moved over a 10 Gigabit network, it would have taken weeks.

Furthermore, data has dependencies. Snapshots, clones, and other data relationships with applications and processes render data inert, weighing it down like an anchor of a ship.

When I first started in the industry more than 25 years ago, Direct Attached Storage (DAS) was the dominating storage platform. I had a bulky Sun MultiDisk Pack connected via Fast SCSI to my SPARCstation 2 (diagram below):

Then I was assigned as the implementation engineer for Hock Hua Bank (now defunct) retail banking project in their Sibu HQ in East Malaysia. It was the first Sun SPARCstorage 1000 (photo below), running a direct attached Fibre Channel 0.25 Gbps FCAL (Fibre Channel Arbitrated Loop). It was the cusp of the birth of SAN (Storage Area Network).

Photo from https://www.cca.org/dave/tech/sys5/

The proliferation of SAN over the next 2 decades pushed DAS into obscurity, until SAS (Serial Attached SCSI) came about. Added to the mix was the prominence of Cloud Storage. But on-premises storage and Cloud Storage didn’t always come together. There was always a valley between the 2, until the public clouds gained a stronger foothold in the minds of IT and businesses. Today, both on-premises storage and cloud storage are slowly cosying as one Data Singularity, thanks to vision and conceptualization of data fabrics. NetApp was an early proponent of the Data Fabric concept 4 years ago. Continue reading →

Is AI my friend?

By cfheoh | April 19, 2019 - 7:29 am |April 19, 2019 Algorithm, Analytics, Artificial Intelligence, Data, Data Corruption, Data Management, Data Privacy, Data Protection, Data Security, Deep Learning, Google, Machine Learning

I am sorry, Dave …

Let’s start this story with 2 supposed friends – Dave and Hal.

How do we become friends?

We have friends and we have enemies. We become friends when trust is established. Trust is established when there is an unsaid pact, a silent agreement that I can rely on you to keep my secrets private. I will know full well that you will protect my personal details with a strong conviction. Your decisions and your actions towards me are in my best interest, unbiased and would benefit both me and you.

I feel secure with you.

AI is my friend

When the walls of uncertainty and falsehood are broken down, we trust our friends more and more. We share deeper secrets with our friends when we believe that our privacy and safety are safeguarded and protected. We know well that we can rely on them and their decisions and actions on us are reliable and unbiased.

AI, can I count on you to protect my privacy and give me security that my personal data is not abused in the hands of the privileged few?

AI, can I rely on you to be ethical, unbiased and give me the confidence that your decisions and actions are for the benefit and the good of me, myself and I?

My AI friends (maybe)

As I have said before, I am not a skeptic. When there is plenty of relevant, unbiased data fed into the algorithms of AI, the decisions are fair. People accept these AI decisions when the degree of accuracy is very close to the Truth. The higher the accuracy, the greater the Truth. The greater the Truth, the more confident people are towards the AI system.

Here are some AI “friends” in the news:

But we have to careful here as well. Accuracy can be subjective, paradoxical and enigmatic. When ethics are violated, we terminate the friendship and we reject the “friend”. We categorically label him or her as an enemy. We constantly have to check, just like we might, once in a while, investigate on our friends too.

In Conclusion

AI, can we be friends now?

[Apology: sorry about the Cyberdyne link 😉 ]

[This blog was posted in LinkedIn on Apr 19th 2019]

Figuring out storage for Kubernetes and containers

By cfheoh | April 16, 2019 - 6:44 pm |April 16, 2019 Amazon Web Services, API, Cloud, Containers, Data, Data Archiving, Data Availability, Data Management, Data Protection, Data Security, Docker, Edge Computing, Elastifile, Fibre Channel, Filesystems, Google, Hyperconvergence, Kubernetes, NFS, Openstack, Performance Benchmark, Performance Caching, Robin.io, Snapshots, Software Defined Storage, Storage Optimization, Virtualization, VMware

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Oops! I forgot about you!

To me, containers and container orchestration (CO) engines such as Kubernetes, Mesos, Docker Swarm are fantastic. They scale effortlessly and are truly designed for cloud native applications (CNA).

But one thing irks me. Storage management for containers and COs. It was as if when they designed and constructed containers and the containers orchestration (CO) engines, they forgot about the considerations of storage and storage management. At least the persistent part of storage.

Over a year ago, I was in two minds about persistent storage, especially when it comes to the transient nature of microservices which was so prevalent and were inundating the cloud native applications landscape. I was searching for answers in my blog. The decentralization of microservices in containers means mass deployment at the edge, but to have the pre-processed and post-processed data stick to the persistent storage at the edge device is a challenge. The operative word here is “STICK”.

Two different worlds

Containers were initially designed and built for lightweight applications such as microservices. The runtime, libraries, configuration files and dependencies are all in one package. They were meant to do simple tasks quickly and scales to thousands easily. They could be brought up and brought down in little time and did not have to bother about the persistent data stored by the host. The state of the containers were also not important to the application tasks at hand.

Today containers like Docker have matured to run enterprise applications and the state of the container is important. The applications must know the state and the health of the container. The container could be in online mode, online but not accepting data mode, suspended mode, paused mode, interrupted mode, quiesced mode or halted mode. Each mode or state of the container is important to the running applications and the container can easily brought up or down in an instance of a command. The stateful nature of the containers and applications is critical for the business. The same situation applies to container orchestration engines such as Kubernetes.

Container and Kubernetes Storage

Docker provides 3 methods to local storage. In the diagram below, it describes:

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Malaysia, when will you take data privacy seriously?

By cfheoh | April 4, 2019 - 3:15 pm |April 4, 2019 Backup, Data, Data Management, Data Privacy, Data Protection, Data Security

Category Archives: Data Management