Despite the many changes in data storage over the decades, some fundamentals remain. One of these is that storage is accessed by one of three methods – block, file and object.
This article will define and expand on the characteristics of these three, while also looking at the on-prem and cloud products you will typically find that use file, block and object storage.
What we see is that while on-prem (usually) hardware form factor block, file and object storage products are available, these types of access to storage are also offered in the cloud to serve the workloads there that require them.
The rise of the cloud has also led to hybrid – datacentre and cloud – and distributed forms of file and object storage.
So, although file, object and block are long-running fundamentals of storage, the ways they are being deployed in the cloud era are changing.
The file system has always been a mainstay of storage technology. Block and file access storage offer two ways to interact with the file system.
File access storage is when you access entire files via the file system. Usually that is via network-attached storage (NAS) or a linked grid of scale-out NAS nodes. Such products come with their own file system on board and storage is presented to applications and users in the drive letter format.
In block access, the storage product – usually deployed on-prem in storage-area network (SAN) systems, for example – only addresses blocks of storage within files, databases, etc. In other words, the file system that applications talk through resides higher in the stack.
File systems deliver all sorts of advantages. Among the most prominent is that this is how most enterprise applications are written – and that won’t go away too soon.
A key characteristic of file system-based methods is that there are methods – such as those found within the Posix command set – to lock files to ensure they cannot be simultaneously over-written, at least not in ways that corrupt the file or the processes around it.
File storage accesses entire files, so it gets used for general file storage, as well as more specialised workloads that require file access, such as in media and entertainment. And, in its scale-out NAS form, it is a mainstay of large-scale repositories for analytics and high-performance computing (HPC) workloads.
Block storage provides application access to the blocks that files comprise. This might be database access where many users work on the same file simultaneously and from possibly the same application – email, enterprise applications such as enterprise resource planning (ERP), for example – but with locking at the sub-file level.
Block storage has the great benefit of high performance, and not having to deal with metadata and file system information, etc.
File storage still exists in standalone NAS format, especially at the entry level, and scale-out NAS, intended for on-prem deployment, is commonplace.
But the advent of the cloud, and its tendency to globalise operations, has affected things has had a twofold effect.
On the one hand, there are a number of suppliers that offer global file systems that combine a file system distributed across public cloud and local network hardware, with all data in a single namespace. Providers here include Ctera, Nasuni, Panzura, Hammerspace and Peer Software.
On the other hand, all the key cloud providers – Amazon Web Services, Google Cloud Platform and Microsoft Azure – offer their own file access storage services, and also those of NetApp, in the case of AWS. IBM also offers file storage though its cloud offering.
Some storage suppliers, such as IBM and Pure, offer instances of their block storage in the cloud. And the big three all offer cloud block storage services, aimed at applications that require the lowest latency, such as databases and analytics caching, as well as virtual machine (VM) work.
Probably because of the nature of block storage and its performance requirements, no distributed block storage seems to have emerged in the way it has with file.
Object storage is based on a “flat” structure with access to objects via unique IDs, similar to the domain name system (DNS) method of accessing websites.
For that reason, object storage is quite unlike the hierarchical, tree-like file system structure, and that can be an advantage when datasets grow very large. Some NAS systems feel the strain when they get to billions of files.
Object storage accesses data at the equivalent of file level, but without file locking, and often more than one user can access the object at the same time. Object storage is not strongly consistent. In other words, it is eventually consistent between mirrored copies that exist.
Most legacy applications are not written for object storage. But far from that necessarily being a disadvantage, historically speaking, object storage is in fact the storage access method of choice for the cloud era. That is because the cloud is generally far more of a stateless proposition than the legacy enterprise environment, and also comprises probably the bulk of storage offered by the big cloud providers.
Also, objects in object storage offer a richer set of metadata than in a traditional file system. That makes data in object storage well-suited to analytics, too.
The cloud has been object storage’s natural home. Most storage services offered by cloud providers are based on object storage, and it is here that new de facto standards, such as S3, have emerged.
With its easy access to data that that can happily exist as largely stateless and eventually consistent, object is the bulk storage of the cloud era.
You can get object storage for on-prem deployment, such as Dell EMC’s Elastic Cloud Storage, which is solely for datacentre deployment. Meanwhile, Hitachi Vantara’s Hitachi Content Platform, IBM’s Cloud Object Storage and NetApp’s StorageGrid can operate in hybrid- and multicloud scenarios.
Some specialist object storage suppliers, such as Cloudian and Scality, offer on-prem and hybrid deployments.
And in the case of Scality, along with Pure Storage (and NetApp, to an extent), converged file and object storage is possible, with the rationale here being that customers increasingly want to access large amounts of unstructured data that may be in file or object storage formats.
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As the world becomes increasingly data-driven, businesses must find suitable solutions to help them achieve their desired outcomes. Data lake storage has garnered the attention of many organizations that need to store large amounts of unstructured, raw information until it can be used in analytics applications.
The data lake solution market is expected to grow rapidly in the coming years and is driven by vendors that offer cost-effective, scalable solutions for their customers.
Learn more about data lake solutions, what key features they should have and some of the top vendors to consider this year.
A data lake is defined as a single, centralized repository that can store massive amounts of unstructured and semi-structured information in its native, raw form.
It’s common for an organization to store unstructured data in a data lake if it hasn’t decided how that information will be used. Some examples of unstructured data include images, documents, videos and audio. These data types are useful in today’s advanced machine learning (ML) and advanced analytics applications.
Data lakes differ from data warehouses, which store structured, filtered information for specific purposes in files or folders. Data lakes were created in response to some of the limitations of data warehouses. For example, data warehouses are expensive and proprietary, cannot handle certain business use cases an organization must address, and may lead to unwanted information homogeneity.
On-premise data lake solutions were commonly used before the widespread adoption of the cloud. Now, it’s understood that some of the best hosts for data lakes are cloud-based platforms on the edge because of their inherent scalability and considerably modular services.
A 2019 report from the Government Accountability Office (GAO) highlights several business benefits of using the cloud, including better customer service and the acquisition of cost-effective options for IT management services.
Cloud data lakes and on-premise data lakes have pros and cons. Businesses should consider cost, scale and available technical resources to decide which type is best.
Read more about data lakes: What is a data lake? Definition, benefits, architecture and best practices
It’s critical to understand what features a data lake offers. Most solutions come with the same core components, but each vendor may have specific offerings or unique selling points (USPs) that could influence a business’s decision.
Below are five key features every data lake should have:
Data lakes that offer diverse interfaces, APIs and endpoints can make it much easier to upload, access and move information. These capabilities are important for a data lake because it allows unstructured data for a wide range of use cases, depending on a business’s desired outcome.
ML engineers, data scientists, decision-makers and analysts benefit most from a centralized data lake solution that stores information for easy access and availability. This characteristic can help data professionals and IT managers work with data more seamlessly and efficiently, thus improving productivity and helping companies reach their goals.
Imagine a data lake with large amounts of information but no sense of organization. A viable data lake solution must incorporate generic organizational methods and search capabilities, which provide the most value for its users. Other features might include key-value storage, tagging, metadata, or tools to classify and collect subsets of information.
Security and access control are two must-have features with any digital tool. The current cybersecurity landscape is expanding, making it easier for threat actors to exploit a company’s data and cause irreparable damage. Only certain users should have access to a data lake, and the solution must have strong security to protect sensitive information.
More organizations are growing larger and operating at a much faster rate. Data lake solutions must be flexible and scalable to meet the ever-changing needs of modern businesses working with information.
Also read: Unlocking analytics with data lake and graph analysis
Some data lake solutions are best suited for businesses in certain industries. In contrast, others may work well for a company of a particular size or with a specific number of employees or customers. This can make choosing a potential data lake solution vendor challenging.
Companies considering investing in a data lake solution this year should check out some of the vendors below.
The AWS Cloud provides many essential tools and services that allow companies to build a data lake that meets their needs. The AWS data lake solution is widely used, cost-effective and user-friendly. It leverages the security, durability, flexibility and scalability that Amazon S3 object storage offers to its users.
The data lake also features Amazon DynamoDB to handle and manage metadata. AWS data lake offers an intuitive, web-based console user interface (UI) to manage the data lake easily. It also forms data lake policies, removes or adds data packages, creates manifests of datasets for analytics purposes, and features search data packages.
Cloudera is another top data lake vendor that will create and maintain safe, secure storage for all data types. Some of Cloudera SDX’s Data Lake Service capabilities include:
Other benefits of Cloudera’s data lake include product support, downloads, community and documentation. GSK and Toyota leveraged Cloudera’s data lake to garner critical business intelligence (BI) insights and manage data analytics processes.
Databricks is another viable vendor, and it also offers a handful of data lake alternatives. The Databricks Lakehouse Platform combines the best elements of data lakes and warehouses to provide reliability, governance, security and performance.
Databricks’ platform helps break down silos that normally separate and complicate data, which frustrates data scientists, ML engineers and other IT professionals. Aside from the platform, Databricks also offers its Delta Lake solution, an open-format storage layer that can Strengthen data lake management processes.
Domo is a cloud-based software company that can provide big data solutions to all companies. Users have the freedom to choose a cloud architecture that works for their business. Domo is an open platform that can augment existing data lakes, whether it’s in the cloud or on-premise. Users can use combined cloud options, including:
Domo offers advanced security features, such as BYOK (bring your own key) encryption, control data access and governance capabilities. Well-known corporations such as Nestle, DHL, Cisco and Comcast leverage the Domo Cloud to better manage their needs.
Google is another big tech player offering customers data lake solutions. Companies can use Google Cloud’s data lake to analyze any data securely and cost-effectively. It can handle large volumes of information and IT professionals’ various processing tasks. Companies that don’t want to rebuild their on-premise data lakes in the cloud can easily lift and shift their information to Google Cloud.
Some key features of Google’s data lakes include Apache Spark and Hadoop migration, which are fully managed services, integrated data science and analytics, and cost management tools. Major companies like Twitter, Vodafone, Pandora and Metro have benefited from Google Cloud’s data lakes.
Hewlett Packard Enterprise (HPE) is another data lake solution vendor that can help businesses harness the power of their big data. HPE’s solution is called GreenLake — it offers organizations a truly scalable, cloud-based solution that simplifies their Hadoop experiences.
HPE GreenLake is an end-to-end solution that includes software, hardware and HPE Pointnext Services. These services can help businesses overcome IT challenges and spend more time on meaningful tasks.
Business technology leader IBM also offers data lake solutions for companies. IBM is well-known for its cloud computing and data analytics solutions. It’s a great choice if an operation is looking for a suitable data lake solution. IBM’s cloud-based approach operates on three key principles: embedded governance, automated integration and virtualization.
These are some data lake solutions from IBM:
With so many data lakes available, there’s surely one to fit a company’s unique needs. Financial services, healthcare and communications businesses often use IBM data lakes for various purposes.
Microsoft offers its Azure Data Lake solution, which features easy storage methods, processing, and analytics using various languages and platforms. Azure Data Lake also works with a company’s existing IT investments and infrastructure to make IT management seamless.
The Azure Data Lake solution is affordable, comprehensive, secure and supported by Microsoft. Companies benefit from 24/7 support and expertise to help them overcome any big data challenges they may face. Microsoft is a leader in business analytics and tech solutions, making it a popular choice for many organizations.
Companies can use Oracle’s Big Data Service to build data lakes to manage the influx of information needed to power their business decisions. The Big Data Service is automated and will provide users with an affordable and comprehensive Hadoop data lake platform based on Cloudera Enterprise.
This solution can be used as a data lake or an ML platform. Another important feature of Oracle is it is one of the best open-source data lakes available. It also comes with Oracle-based tools to add even more value. Oracle’s Big Data Service is scalable, flexible, secure and will meet data storage requirements at a low cost.
Snowflake’s data lake solution is secure, reliable and accessible and helps businesses break down silos to Strengthen their strategies. The top features of Snowflake’s data lake include a central platform for all information, fast querying and secure collaboration.
Siemens and Devon Energy are two companies that provide testimonials regarding Snowflake’s data lake solutions and offer positive feedback. Another benefit of Snowflake is its extensive partner ecosystem, including AWS, Microsoft Azure, Accenture, Deloitte and Google Cloud.
Companies that spend extra time researching which vendors will offer the best enterprise data lake solutions for them can manage their information better. Rather than choose any vendor, it’s best to consider all options available and determine which solutions will meet the specific needs of an organization.
Every business uses information, some more than others. However, the world is becoming highly data-driven — therefore, leveraging the right data solutions will only grow more important in the coming years. This list will help companies decide which data lake solution vendor is right for their operations.
Read next: Get the most value from your data with data lakehouse architecture
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The latest White Paper from analyst ESG makes it clear that defensive measures against cyber attacks are no longer sufficient. With 63% of organizations it surveyed experiencing ransomware attacks in the past year, in part due to growing IT complexity creating additional vulnerabilities, a change in thinking is necessary.
Download the paper to take a deeper look at the concept of cyber resilience, which not only defends against cyberattacks, but puts in place recovery solutions to get back to normal as quickly as possible in the event of an attack. Four key components of the cyber resilience solution IBM Cyber Vault are also explored alongside the important role storage plays in resiliency.
Don’t be on the wrong side of the statistics; download now and learn from ESG’s experts today.
In the history of information technology, there are not many companies that have reinvented themselves after missing out on seismic changes. IBM (NYSE:IBM) missed the cloud revolution, but the company has reinvented itself with the acquisition of Red Hat. The company's turnaround is just gaining speed. This decade will be an exciting one for IBM. I am looking to add to my holdings at $130, with the stock yielding 5% at that price.
In 2017, I called Red Hat the "next cloud giant" in my article on Seeking Alpha. My prophecy has come true in many ways. First, IBM paid a massive 60% premium to acquire Red Hat in 2019. Then IBM CEO Ginni Rometty had to defend the acquisition's high cost publicly. IBM then made the Red Hat open source stack the centerpiece of its hybrid cloud and AI strategy [Exhibit 1] to capture market share in the $1 Trillion hybrid cloud opportunity. IBM used its vast sales and marketing teams to increase the reach of Red Hat.
IBM's management has done a great job pivoting its strategy by acquiring Red Hat. The hybrid cloud is a general-purpose software environment that provides the foundation to run any application. After spending over 15 years in oblivion, IBM is finally relevant again. They now have a competitive set of software and services that can help companies modernize their on-premise application stack while gaining the capability to leverage the near infinite compute and storage in the cloud.
At the time of my 2017 article, the prospects for hybrid clouds were cloudy (no pun intended) at best. The IT industry expected the three significant clouds (Amazon’s (AMZN) AWS, Google’s (GOOGL) GCP, and Microsoft’s (MSFT) Azure) to take over almost all the workloads running in on-premise datacenters. Mark Hurd, the late CEO of Oracle (ORCL), even predicted that 80% of enterprise apps would move to the cloud. Maintaining on-premises data centers was considered labor-intensive and expensive. But, a migration to the cloud came with its challenges. A company can get very closely tied to a cloud vendor’s API that is incompatible with the other clouds. A migration proved expensive and time-consuming for many traditional companies, especially in banking, insurance, and financial services.
The financial services industry is highly competitive, with many highly regulated banks competing against fintechs that face no regulation. Even Jamie Dimon, the CEO of JP Morgan Chase (JPM), complained about how fintech is taking market share away from traditional banks and how the competitive scales are tilted in favor of fintech due to the heavy regulatory requirements imposed on banks. So, many traditional companies in the financial services industry needed a way to modernize their technology and provide a great customer experience without breaking the bank or taking years to deliver a digital transformation. These companies also had their data in the databases such as Oracle or IBM DB2, which can be expensive to modernize. Many traditional banks had also deployed their applications on mainframes that continue to be a mainstay technology at banks and insurance companies.
Enter containerization, microservices architecture, and Kubernetes. Containerization and Kubernetes allow a company to move from one cloud to another or even move their application to their own data center without rewriting the code. Red Hat’s Openshift software enables the hybrid cloud. Red Hat is not the only player in the hybrid cloud business. VMware (VMW) has built hybrid cloud features into its VMware vSphere virtualization technology, and the cloud providers have launched their own set of technologies involving containers that help customers run their applications in their datacenters.
Red Hat had total revenues of $3.36 billion at the end of its fiscal year ending in February 2019. FY 2019 was the last full fiscal year before IBM acquired the company. In FY 2021, IBM declared $24.14 billion in software revenue. Hybrid Cloud and Solutions accounted for $17.75 billion or 73% of that total software revenue. The company does not break out the revenue from Red Hat but mentioned that its year-over-year revenue growth was 30.6%. IBM also said they had 3,800 customers on their hybrid cloud platform at the end of FY 2021 and added 200 more new customers in Q1 FY 2022 to bring the total number above 4,000. In 2017, Red Hat had about 300 customers.
Exhibit 1: Red Hat is the Centerpiece of IBM’s Hybrid Cloud and AI Strategy
The company suspended its share buyback program when it announced the Red Hat acquisition in 2019. The company had $62.8 billion in debt at the end of FY 2019. It ended in 2021 with a total debt of $51.7 billion. IBM issued $4 billion in debt in February to replace some of the debt due this year. This new $4 billion debt brings the total to over $54 billion. About $12.2 billion of the debt is related to its financing arm, which offers clients loans to purchase IBM products. The company’s debt to EBITDA ratio is a very high 4.6x. The company will likely not resume share buybacks until the debt to EBITDA ratio is closer to 3x. IBM’s total debt will have to fall to around $35 billion to reach the debt to EBITDA ratio of 3x. The company paid nearly $6 billion in dividends yearly and generated about $12 billion in operating cash flows in FY 2021. The stock yields a dividend of 4.7% with a payout ratio of 68%.
The company’s return on invested capital [ROIC] was 7.34% [Exhibit 2]. I have estimated that the weighted average cost of capital [WACC] for IBM is 7.16% at the current 10-year treasury rate of 3.08%. The company’s ROIC is just marginally better than its WACC. But, the company generated a good return on equity [ROE] of 26%. Hewlett Packard Enterprises (HPE) and VMware had an ROIC of 11% and an ROE of 19.6% and 29.2%, respectively [Exhibit 3].
Exhibit 2: ROIC for VMware, Hewlett Packard Enterprise, and IBM
Exhibit 3: ROE for VMware, Hewlett Packard Enterprise, and IBM
Between June 3, 2019, and July 8, 2022, IBM's daily price return data showed an average return of 0.031% or 3.1 basis points [Exhibit 5]. IBM's daily return was below 0.86% or 86 basis points 75% of the time [third quartile in Exhibit 5].
IBM has returned an average of 0.5% per month since June 2019 [Exhibit 4]. The company's monthly return was below 6.13% 75% of the time between June 3, 2019, and June 30, 2022 [third quartile in Exhibit 4].
Vanguard Information Technology ETF's (VGT) and IBM’s monthly returns have a moderate positive correlation of 0.41. IBM’s strategic and market struggles over the past decade are reflected in this moderate correlation between IBM and the rest of the IT stocks. In fact, over the past five years, the Vanguard Information Technology ETF has returned over 139%, while IBM has returned a -4%. But, IBM has returned 4.49% in the past year compared to -15% for the Vanguard Information Technology ETF.
It is safe to say that IBM’s returns have been disastrous in the past decade. IBM has now turned around its business and has a clear strategic direction. This new strategy at IBM should yield positive returns for investors in the coming years, but I do not expect to double my money in the next three to five years. I do expect that IBM will have annual total returns in the low to mid-double digits.
Exhibit 4: IBM Monthly Price Change (%) [June 3, 2019 - June 30, 2022]
Exhibit 5: IBM Daily Price Change (%) [June 3, 2019 - July 8, 2022]
IBM is long admired for its investments in R&D projects which make risky bets on technologies that have potential. But, the company has not always capitalized on the fruits of its R&D projects. The company’s CEO discussed their quantum computing efforts in the latest earnings call by deploying “the world’s first 127-qubit processor”. Recently, McKinsey & Company highlighted various thorny problems in sustainability and green energy that can be researched using quantum computing. The management consultancy thinks quantum computing will arrive in the second half of this decade.
It also unveiled the first 2-nanometer chip technology that has the potential to pack 50 billion transistors on a chip the size of a fingernail. Many companies in IT invest their R&D dollars in making incremental product improvements. That conservative R&D investment does not yield any groundbreaking technologies or help the company gain much-needed hands-on experience in a specific area. In the end, those companies succumb to technology disruptions. Even though IBM has missed many shifts in computing in the past, the company has a better chance of winning in areas such as quantum computing and the latest semiconductor technologies when it has conducted deep-dive research.
IBM (IBM) has been a disastrous investment for the past decade. But, the Red Hat merger is a success, and the management has executed brilliantly. I am willing to bet that this decade will be prosperous for IBM. IBM's monthly returns have varied widely over the past three years, with a standard deviation of nearly 7%. I will add to my IBM holdings at $130, which is, coincidently, 7% below current prices. The stock would yield an excellent dividend of 5% at $130. Also, economists expect the U.S. to enter a recession, so there is a reasonable probability that the price may get to $130 or below in the next few months.
Organisations have stepped up their focus on backup and recovery as they face an ever increasingly likelihood of falling victim to cyber crime. This is according to speakers participating in an IBM EMEA webinar on boosting cyber resilience with IBM Storage and Predatar, the cyber recovery platform that adds another dimension to IBM’s Spectrum Protect and Spectrum Protect Plus.
Roland Leins, Business Development Executive for Storage Software at IBM Europe, said data protection had to be transformed as organisations made cyber resiliency a top priority. “Modernising data protection for resilience has become crucial. A common mistake is to architect for backup, but organisations must architect for quick recovery to meet the SLAs for the data – this can be the difference between getting the business up again or going out of business completely,” he said. Automation is also necessary to ensure that the necessary recovery happens in a repeatable consistent manner to meet the business SLAs.”
Ben Hodge, Head of Marketing at Predatar, said while the NIST best practice framework covers identify, protect, detect, respond and recover, many organisations have focused on identifying, protecting and detecting in the past. “Organisations are increasingly realising it is quite likely their defences will be breached. There’s a refocusing on response and recovery for a fast and effective response. As they refocus, they are realising they have big challenges to overcome. Predatar is all about the response and recovery, working hand in hand with IBM storage and defences. It is the final piece of the puzzle,” he said.
Built for IBM Spectrum Protect and Spectrum Protect Plus environments, the Predatar cyber recovery orchestration platform takes resiliency to the next level with capabilities to rival any enterprise backup and recovery solution. Predatar’s cyber analytics and real-time alerts have been built to help infrastructure and security teams cut through the noise of complex backup systems to show them their recoverability risk factors on a configurable dashboard. Predatar IQ instantly notifies users of anomalies, changes and issues in their backup environment as they occur, while Data Explorer lets organisations explore the environment to discover new recoverability insights.
Hodge noted that around two-thirds of backup recoveries failed to meet the recovery time objectives for business continuity. “Recovering cleanly is becoming increasingly difficult because of the dwell time malware can sit inside the storage environment before being discovered; being replicated into backup and storage. If organisations are infected deep and wide, recovery will reinfect the infected environment. Around 10% of backup recoveries fail to recover – there could be critical business data in there and organisations can’t afford to have holes in their data,” he said.
“Our cyber recovery orchestration uses automated workflows and AV tools and XDR/EDR tools to continually recover backup workloads, scan them to ensure they are clean. The only way you can be certain you can recover quickly, cleanly and completely is by testing it continually. We also have cyber analytics built in to serve users with data and insights to understand the overall health and recoverability of the backup environments. With machine learning and artificial intelligence overlaid on these analytics, Predatar learns and, over time, it becomes smarter and capable of finding more infections, faster. With Spectrum Protect, Predatar is running in the background, running tests and plugs into SEIM platforms such as QRadar. Predatar continuously searches across backups to look for known infection signatures and identify dormant malware, and will recover suspicious backup workloads to an isolated CleanRoom, scan them for viruses, clean them and restore them to production. By continually scanning your backups in the background, Predatar finds, quarantines and eliminates dormant viruses and malware before they can wreak havoc.”
In SA through Axiz
Craig Botha, Business Development Manager: Advanced Technologies: IBM at Axiz, says Predatar is a compelling solution for anyone tasked with protecting, backing up and recovering data.
“With Predatar running continuous recovery testing and backup data validation, scheduled testing, randomised testing with ML behaviour-based testing, organisations will be able to recover backups quickly, cleanly and completely. What’s exciting is that IBM has taken tried and trusted technology and packaged it with Predatar for an all-in-one solution: you get the full muscle of QRadar enterprise security information and event management (SIEM) in a modern, midrange storage device. It’s new and exciting thinking from IBM. An impressive differentiator is that it’s continuously learning – the AI built into it is phenomenal,” says Botha. “It gets to a point where it knows exactly what team needs the backup, and which data is most important to the company, and adapts to cater for priorities.
He notes that Predatar brings key data protection and backup features into one solution, enabling cyber security and storage teams to do more with less. “There’s a massive cyber skills problem, and there’s a lot of burnout among those with too much to do. Restoring data using traditional methods can be a nightmare – backups might not work, or tapes may be damaged. But Predatar is the future come early, making restoring an immutable copy quick and easy. It’s the future come early. For South African businesses, it addresses challenges around skills and cost,” he says.
“We have been asking for this for some time, and now we have it as part of our portfolio, along with Spectrum Protect and Protect Plus for a complete cyber resiliency story.”
SingleStore, a provider of databases for cloud and on-premises apps and analytical systems, today announced that it raised $116 million in an extension of its Series F. The new cash brings the company’s total raised to $382 million at “unicorn status” (i.e., a valuation of $1 billion or more post-money), which CEO Raj Verma says is being put toward product development and expanding SingleStore’s headcount by the end of the year.
Goldman Sachs led SingleStore’s Series F extension with participation from Sanabil, Dell Technologies Capital, Google Ventures, Hewlett Packard Enterprise, IBM and Insight Partners. It arrives as SingleStore brings on a new chief financial officer, Brad Kinnish, who came by way of Aryaka Networks and Deutsche Bank, where he was the managing director of software investment banking.
The fundraising perhaps reflects the growing demand for platforms that enable flexible data storage and processing. One increasingly popular application is big data analytics, or the process of examining data to uncover patterns, correlations and trends (e.g., customer preferences). According to one latest survey, the number of firms investing more than $50 million a year in big data and AI initiatives rose to 33.9% in 2019. The same survey found that the vast majority of companies believe data and analytics are key to their organization’s digital transformation initiatives.
There’s been a corollary growth in interest within the enterprise in extracting insights from data in real time — a capability afforded by SingleStore. According to a Fivetran poll, 82% of companies are making decisions based on stale information. Eighty-five percent of companies responding to the poll said it’s leading to incorrect decisions and lost revenue. Real-time databases promise to resolve this.
SingleStore was founded as MemSQL in 2011 by Eric Frenkiel, Adam Prout and Nikita Shamgunov. Frenkiel was an engineer at Meta focused on partnership development specifically on the Facebook platform. Prout was a software engineer at Microsoft contributing to the company’s SQL Server product, while Shamgunov spent time both at Microsoft and Meta as a senior software development engineer and infrastructure engineer, respectively.
Verma became the CEO in 2020 after a year in a co-CEO role.
SingleStore’s technology is what’s known as a relational database, meaning it uses a structure of rows and columns to identify and access data in relation to other pieces of data in the database. That’s opposed to a nonrelational database, which has a storage model optimized for the type of data that it’s storing. Otherwise, like any database system, SingleStore accepts requests (e.g., for a user profile, image, video or document) in the form of queries for data contained within the database and processes these queries — which can come from apps, websites or elsewhere — to return the results.
The provider allows customers to run real-time transactions and analytics in a single database. Customers leveraging the platform can integrate, monitor, and query their data as a single entity, regardless of whether that data is stored across one or multiple repositories. By separating storage and compute, SingleStore claims that its database platform can process a trillion rows per second, ingest billions of rows of data an hour and host databases with tens of thousands of tables.
“SingleStore provides unparalleled scalability by decoupling compute across individual applications such as operational analytics and real-time machine learning without these workloads interfering with one another. The workloads run on shared databases which minimizes unnecessary data movement, costs, and complexity,” Verma told TechCrunch in an email interview. “[It also] allows customers to use all the cores on their machines to process a single query … by maximizing CPU utilization.”
Among other rivals, SingleStore competes with Imply, Oracle, Snowflake and MongoDB for relational database service market share. But the company has done well for itself, launching joint solutions with major enterprise partners like Dell and IBM. Verma says that SingleStore is approaching $100 million in annual recurring revenue with a customer base of roughly 300 brands, including Hulu, Uber and Comcast.
Partly driving SingleStore’s growth is the widespread, continued move to the cloud — Gartner predicts that 75% of all databases will be migrated to a cloud service by 2022. According to Verma, new customer acquisition increased 300% for the company’s cloud-based service while cloud revenue climbed 150%.
Increasing Adoption of Cloud Computing to Boost MLaaS Market Growth
New York, US, July 05, 2022 (GLOBE NEWSWIRE) -- According to a comprehensive research report by Market Research Future (MRFR), “Machine Learning as a Service Market Analysis by Component (Software tools, Cloud APIs, Web-based APIs), By Application (Network analytics, Predictive maintenance, Augmented reality), By Deployment, By End-User (Manufacturing, Healthcare, BFSI, Transportation, Government, Retail)- Forecast 2030” valuation is poised to reach USD 302.66 Billion by 2030, registering an 36.2% CAGR throughout the forecast period (2021–2030).
MLaaS Market Overview
Technological developments coupled with the increase in innovation and research activities across the globe will offer robust opportunities for the Mlaas market over the forecast period.
Machine Learning as a Service Market Report Scope:
USD 302.66 Billion by 2030
36.2% From 2021 To 2030
2021 To 2030
Value (USD Billion)
Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
Component, Application and Region
North America, Europe, Asia-Pacific, and Rest of the World (RoW)
Yottamine Analytics (U.S.), Fuzzy.ai (Canada), AT&T (U.S.), Amazon Web Services (U.S.), IBM (U.S.), Microsoft (U.S.), BigML (U.S.), Google (U.S.), Ersatz Labs, Inc. (U.S.), and Sift Science, Inc. (U.S.)
Key Market Opportunities
Technological Developments to offer Robust Opportunities
Key Market Drivers
Increasing Adoption of Cloud Computing to Boost Machine Learning as a Service Market Growth
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Increasing Adoption of Cloud Computing to Boost Market Growth
The increasing adoption of cloud computing technology coupled with the use of social media platforms will boost market growth over the forecast period. Cloud computing is now widely used by all companies that supply enterprise storage solutions. Data analysis is performed online using cloud storage, giving the advantage of evaluating real-time data collected on the cloud. Cloud computing enables the analysis of data from any location and any time. Furthermore, using the cloud to execute machine learning allows businesses to get useful data, such as consumer behaviour and purchasing trends, virtually from linked data warehouses, lowering infrastructure and storage costs. As a result, the sector for machine learning as a service sector is growing as cloud computing technology becomes more widely adopted.
Compliance Issues to act as Market Restraint
Compliance and government issues and dearth in the availability of skilled consultants for deploying machine learning services may act as market restraints over the forecast period.
Lack of Knowledge to Remain Market Challenge
The lack of knowledge and stringent compliance difficulties may act as market challenges over the forecast period.
Machine Learning as a Service Market Segments
The machine learning as a service market is bifurcated based on component, application, deployment, end user, and organization size.
By component, the machine learning as a service market is segmented into web-based APIs, cloud APIs, and software tools.
By application, network analytics will lead the market over the forecast period.
By deployment, the machine learning as a service market is segmented into on premise and cloud.
By end user, retail will dominate the market over the forecast period.
By organization size, the SME will spearhead the market over the forecast period.
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Machine Learning as a Service Market Regional Analysis
North America to Head MLaaS Market
Because of increased growth in nations like Canada and the United States, the North American area holds the highest proportion of the Mlaas business. These countries are home to a diverse range of small and large start-ups. As a result, the market for machine learning as a service in North America is growing. North America is the fastest-developing region in the global machine learning as a service business in terms of technology advances and utilization. It has the infrastructure and financial resources to invest in machine learning as a service. In addition, higher defence spending and technological advancements in the telecommunications industry are expected to drive market growth throughout the forecast period.
Government data security requirements are predicted to be a major driver of the machine learning services market. The market is predicted to be fueled by services like security information & cloud applications. Furthermore, the presence of industry titans such as IBM, Google, Microsoft, & Amazon Web Services, as well as a diverse product offering, has boosted demand for machine learning in this region. Furthermore, the market players are likely to benefit from the expansion of artificial intelligence and cognitive computing by leveraging various industry applications like natural language processing, predictive analytics, fraud detection and management, and computer vision.
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Because of the robust innovation ecosystem, which is fueled by strategic federal investments in advanced technology and complemented via the presence of entrepreneurs and visionary scientists coming together from renowned research institutions, North America is expected to grasp a significant share of the market. A huge expansion of 5G, internet of things, and linked devices is also occurring in the region. Because each of the major technology companies has a sizable public cloud infrastructure and machine learning platforms, machine learning-as-a-service is now a reality for those looking to use AI for everything from customer service to the robotic process automation, analytics, marketing, and predictive maintenance, among other things.
APAC to Have Admirable Growth in Machine Learning as a Service Market
With the greatest CAGR, Asia-Pacific is predicted to be the quickest developing regional segment over the forecast period. Leading companies are concentrating their efforts in Asia-Pacific to expand their operations, as the region is likely to see rapid development in the deployment of security services, particularly in the BFSI sector. To provide better customer service, industry participants are realizing the significance of providing multi-modal platforms. The rise in AI application adoption is likely to be the primary trend driving market growth in the area. Furthermore, government organizations have taken important steps to accelerate the adoption of machine learning & related technologies in this region. During the forecast period, Asia-Pacific is expected to have the highest CAGR. This is due to an increase in the use of security services, particularly in the BFSI sector, as well as increased awareness and long-term expansion of the IT sector in the region.
Artificial intelligence is likely to aid in the fight against the COVID-19 pandemic. COVID-19 cases are being tracked and traced in several countries utilizing population monitoring approaches. Researchers in South Korea, for example, track coronavirus cases using surveillance camera footage & geo-location data. Data scientists use machine intelligence algorithms for anticipating the location of the next outbreak and notify the appropriate authorities, allowing for real-time illness tracking. During the projection period, such active endeavours are projected to increase need for machine intelligence solutions.
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Machine Learning as a Service Market Competitive Analysis
Dominant Key Players on Machine Learning as a Service Market Covered are:
Apple Inc. (US)
IBM Corporation (US)
BAE Systems (UK)
LG Electronics (South Korea)
Google Inc. (US)
Microsoft Corporation (US)
Digital Reasoning Systems Inc. (US)
ABB Limited (Switzerland)
General Electric Co. (US)
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DUBLIN--(BUSINESS WIRE)--Jul 14, 2022--
The “Global Cloud Computing Market, By Deployment Type, By Service Model, Platform as a Service, Software as a Service, By Industry Vertical & By Region - Forecast and Analysis 2022 - 2028” report has been added to ResearchAndMarkets.com’s offering.
The Global Cloud Computing Market was valued at USD 442.89 Billion in 2021, and it is expected to reach a value of USD 1369.50 Billion by 2028, at a CAGR of more than 17.50% over the forecast period (2022 - 2028).
Cloud computing is the delivery of hosted services over the internet, including software, servers, storage, analytics, intelligence, and networking. Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS), and Platform-as-a-Service (PaaS) are three types of cloud computing services (PaaS).
The expanding usage of cloud-based services and the growing number of small and medium businesses around the world are the important drivers driving the market growth. Enterprises all over the world are embracing cloud-based platforms as a cost-effective way to store and manage data. Commercial data demands a lot of storage space. With the growing volume of data generated, many businesses have moved their data to cloud storage, using services like Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
The growing need to regulate and reduce Capital Expenditure (CAPEX) and Operational Expenditure (OPEX), as well as the increasing volume of data generated in websites and mobile apps, are a few drivers driving the growth of emerging technologies. Emerging technologies like big data, artificial intelligence (AI), and machine learning (ML) are gaining traction, resulting in the global cloud computing industry growth. The cloud computing market is also driven by major factors such as data security, Faster Disaster Recovery (DR), and meeting compliance standards.
Aspects covered in this report
The global cloud computing market is segmented on the basis of deployment type, service model, and industry vertical. Based on the deployment type, the market is segmented as: private cloud, public cloud, and hybrid cloud. Based on the service model, the market is segmented as: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Based on industry vertical, the market is segmented as: Government, Military & Defense, Telecom & IT, Healthcare, Retail, and Others. Based on region it is categorized into: North America, Europe, Asia-Pacific, Latin America, and MEA.
Key Market Trends
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INDUSTRY KEYWORD: SOFTWARE TECHNOLOGY NETWORKS DATA MANAGEMENT
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Jul 11, 2022 (The Expresswire) -- Global “Data Storage Market” report provides a complete examination of market dynamics, size, share, current developments, and trending business strategies. This report gives a complete analysis of different segments on the basis of type, application, and region. The report outlines the market characteristics, and market growth Data Storage industry, categorized by type, application, and consumer sector. In addition, it provides a comprehensive analysis of aspects involved in market development before and after the Covid-19 pandemic.
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Market Analysis and Insights: Global Data Storage Market
This report focuses on global Data Storage Market Share, also covers the segmentation data of other regions in regional level and county level.
Due to the COVID-19 pandemic, the global Data Storage market analysis is estimated to be worth USD million in 2022 and is forecast to a readjusted size of USD million by 2028 with a CAGR of during the review period. Fully considering the economic change by this health crisis, by Type, Data Storage accounting for the Data Storage global market in 2021, is projected to value USD million by 2028, growing at a revised CAGR in the post-COVID-19 period. While by Application, leading segment, accounting for over percent market share in 2021, and altered to an CAGR throughout this forecast period.
In United States the Data Storage market size is expected to grow from USD million in 2021 to USD million by 2028, at a CAGR of during the forecast period.
List of TOP KEY PLAYERS in Data Storage Market Report are -● HPE ● NetApp ● Dell EMC ● IBM ● Pure Storage ● Hitachi ● Fujitsu ● Huawei ● Western Digital
Global Data Storage Market: Segment Analysis
The research report includes specific segments by region (country), by company, by Type and by Application. This study provides information about the sales and revenue during the historic and forecasted period of 2017 to 2028. Understanding the segments helps in identifying the importance of different factors that aid the market growth.
The Data Storage Market is Segmented by Types:● All-Flash Arrays ● Hybrid Storage Arrays ● HDD Arrays
The Data Storage Market is Segmented by Applications:● IT and Telecom ● BFSI ● Healthcare ● Education ● Manufacturing ● Media and Entertainment ● Energy and Utility ● Retail and e-Commerce ● Others
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Geographically, this report is segmented into several key regions, with sales, revenue, market share and growth Rate of Data Storage in these regions, from 2022 to 2028, covering● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam) ● South America (Brazil, Argentina, Columbia etc.) ● Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)
Important Features that are under Offering and Key Highlights of the Reports:● To get insights into the countries in Data Storage market. ● To get extensive-ranging information about the top key players in this industry, their product portfolios, and key strategies embraced by the players. ● To get a complete overview of the Data Storage market. ● To know the future view for the market. ● To learn about the market plans that are being adopted by top organizations. ● To understand the supreme affecting driving and restraining forces in the market and their influence on the global market.
Key Questions Addressed by the Report● New products/service competitors are exploring? ● Key players in the Data Storage market and how extreme is the competition? ● What are the future market trends that manufacturers are emphasizing on in the future updates? ● For each segment, what are the crucial opportunities in the market? ● What are the key growth strategies embraced by key market players in the market? ● What are the key success strategies adopted by major competitors in the market?
An exhaustive and professional study of the global Data Storage market report has been completed by industry professionals and presented in the most particular manner to present only the details that matter the most. The report mainly focuses on the most dynamic information of the global market.
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Major Points from Table of Contents:
1 Data Storage Market Overview
1.1 Data Storage Product Scope
1.2 Data Storage Segment by Type
1.3 Data Storage Segment by Application
1.4 Data Storage Market Estimates and Forecasts (2017-2028)
2 Data Storage Estimates and Forecasts by Region
2.1 Global Data Storage Market Size by Region: 2017 VS 2021 VS 2028
2.2 Global Data Storage Market Scenario by Region (2017-2021)
2.3 Global Market Estimates and Forecasts by Region (2022-2028)
2.4 Geographic Market Analysis: Market Facts and Figures
3 Global Data Storage Competition Landscape by Players
3.1 Global Top Data Storage Players by Sales (2017-2021)
3.2 Global Top Data Storage Players by Revenue (2017-2021)
3.3 Global Data Storage Market Share by Company Type (Tier 1, Tier 2 and Tier 3) and (based on the Revenue in Data Storage as of 2020)
3.4 Global Data Storage Average Price by Company (2017-2021)
3.5 Manufacturers Data Storage Manufacturing Sites, Area Served, Product Type
3.6 Manufacturers Mergers and Acquisitions, Expansion Plans
4 Global Data Storage Market Size by Type
4.1 Global Data Storage Historic Market Review by Type (2017-2021)
4.2 Global Market Estimates and Forecasts by Type (2022-2028)
4.2.3 Global Price Forecast by Type (2022-2028)
5 Global Data Storage Market Size by Application
5.1 Global Data Storage Historic Market Review by Application (2017-2021)
5.2 Global Market Estimates and Forecasts by Application (2022-2028)
6 North America Data Storage Market Facts and Figures
6.1 North America Data Storage by Company
6.2 North America Data Storage Breakdown by Type
6.3 North America Data Storage Breakdown by Application
7 Europe Data Storage Market Facts and Figures
8 China Data Storage Market Facts and Figures
9 Japan Data Storage Market Facts and Figures
10 Southeast Asia Data Storage Market Facts and Figures
11 India Data Storage Market Facts and Figures
12 Company Profiles and Key Figures in Data Storage Business
13 Data Storage Manufacturing Cost Analysis
13.1 Data Storage Key Raw Materials Analysis
13.1.1 Key Raw Materials
13.1.2 Key Raw Materials Price Trend
13.1.3 Key Suppliers of Raw Materials
13.2 Proportion of Manufacturing Cost Structure
13.3 Manufacturing Process Analysis of Data Storage
13.4 Data Storage Industrial Chain Analysis
14 Marketing Channel, Distributors and Customers
14.1 Marketing Channel
14.2 Data Storage Distributors List
14.3 Data Storage Customers
15 Market Dynamics
15.1 Data Storage Market Trends
15.2 Data Storage Drivers
15.3 Data Storage Market Challenges
15.4 Data Storage Market Restraints
Browse complete table of contents at -
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