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C2030-136 exam Dumps Source : Foundations of IBM Big Data & Analytics Architecture V1
Test Code : C2030-136
Test Name : Foundations of IBM Big Data & Analytics Architecture V1
Vendor Name : IBM
: 58 Real Questions
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the vast majority of organizations — 77% of respondents — don’t have a cybersecurity incident response plan utilized throughout the business, in accordance with a study conducted by using the Ponemon Institute and paid for by means of IBM.
one of the vital basic causes for this is the neatly-documented safety skills scarcity. “It’s a major, major difficulty for protection commonly however’s chiefly acute in incident response since it’s a more recent discipline,” noted Ted Julian, vp of product administration and co-founder of IBM Resilient.
IBM purchased Resilient, an incident response business, in 2016. The 2019 Cyber Resilient organization is the dealer’s fourth annual benchmark examine on cyber resilience — how an commercial enterprise aligns its prevention, detection, and response capabilities to control and mitigate threats towards its information and IT infrastructure. For the report, Ponemon surveyed more than three,600 safety and IT professionals globally.ability hole
Survey respondents pointed out they lack the headcount to retain and test their incident response plans and are facing between 10 and 20 open seats on cybersecurity groups. only 30% of respondents mentioned that staffing for protection is adequate to achieve a excessive degree of cyber resilience. And seventy five% of respondents expense their issue in hiring and conserving professional protection personnel as moderately excessive to high.
“network protection has been around 20 years, and malware, virus analysis, issues of that nature are smartly-based ability sets,” Julian pointed out. “but incident response, in terms of firms having dedicated individuals to this function, is comparatively modern and a stunning tall order. You need to find out about networks, safety, a little about malware. You deserve to have enterprise capabilities to have capabilities with the knowledge business have an effect on of assaults. The ability shortage is primarily acute when it comes to incident responders.”
adding to the competencies gap, forty eight% pointed out their company deploys too many separate safety tools, in the end expanding operational complexity and decreasing visibility into overall protection posture.Automation is key
here is the primary 12 months the examine checked out automation and if it’s positive for cyber resilience. When asked to expense the cost of automation and cyber resilience to their safety posture on a scale of one (being the lowest price) to 10 (highest price), 62% rated the price of cyber resilience as very high, and seventy six% referred to they discover automation very positive.
still, simplest 23% observed they have been significant clients of automation applied sciences such as identification management and authentication, incident response platforms, and security information and experience administration (SIEM) equipment. And 77% reported their corporations most effective use automation reasonably, insignificantly, or no longer at all.
Julian says the simple cast off for organizations should still be to position a cyber response plan in region — stat. “You’re not constructing on a sound foundation until you’ve executed that,” he spoke of. And it doesn’t ought to be super advanced.”Make a Plan
“It can be so simple as dusting off the plan you do have and getting a go-practical team to replace it as fundamental,” Julian explained. “It’s about getting the correct individuals across the table.”
It’s critical to have non-IT-safety americans on this team as a result of an assault will have an effect on dissimilar business businesses, he pointed out. “make sure you encompass colleagues in IT more extensively but also HR, as a result of, let’s face it, insider threats are a large deal. advertising should still be worried because in case you a crisis communication plan in area you may should execute that. And there may still be some govt sponsorship as a result of on the conclusion of the day if it’s a very unhealthy breach there goes to be some govt referred to as upon to remark.”
This response plan will help “cut the fog of struggle,” Julian introduced. “When an incident occurs, if in case you have documented your strategies and your response time, and in case you’ve practiced, you've got greater of a combating probability.”
Chart courtesy of IBM
informationan extra response to IBM's Acquisition of pink Hat
Now that the grime has settled around the explosive announcement that IBM could be buying open supply utility provider and longtime Java group process (JCP) leader red Hat, i needed to share the response to the deal of one of the keenest (and most fearless) observers of the Java universe.
Gartner analyst Anne Thomas went toe-to-toe with the Java EE neighborhood remaining 12 months over the destiny of business Java, which is now developed and maintained by the Eclipse foundation as Jakarta EE. She took heavy hearth from that neighborhood over a research word she co-authored with contributing analyst Aashish Gupta ("Market e-book for software platforms").
In that research be aware the authors asserted, among other things, that Java EE has now not stored pace with architectural developments and digital company initiatives, that Java builders are demonstrating a clear preference for lightweight frameworks over Java EE, and that Java EE is not an appropriate framework for building cloud-native functions. They additionally counseled those responsible for modernizing an commercial enterprise's software infrastructure to "strengthen a strategy to cope with the obsolescence of Java EE and different three-tier utility frameworks."
I asked Thomas in regards to the implications of this blockbuster acquisition in an e mail, and he or she stated that combining the IBM and pink Hat teams eliminates some conflicts between the two companies' commercial enterprise Java actions, however also warned of an almost inevitable decline within the complete quantity of substances allocated to helping enterprise Java.
"IBM and purple Hat are both greatest contributors to the Java platform, aside from Oracle," she said, "so as an alternative of two businesses with doubtlessly conflicting agendas, there should be one. This ensures improved synergy, but reduces competitors. and i assume that IBM will in the reduction of the overall number of people committed to assisting the Java group."
The synergy is certainly there. IBM has long been a leading user and contributor to Linux, which undergirds purple Hat's properly products, including the crimson Hat business Linux (RHEL) OS. And IBM and purple Hat are active contributors of the Java neighborhood. each businesses have claimed that the main roles they play in the JCP usually are not plagued by the merger.
Thomas additionally put the acquisition in the context of one of the vital arguments she and Gupta made in that now notorious analysis word.
"average Java EE is a relic of the past," she mentioned. "New construction now happens at the Eclipse basis. And both IBM and red Hat were lively in that neighborhood. besides the fact that children the immediate work there is concentrated on the present Java EE liberate, are expecting future efforts to be concentrated on the microprofile and cloud-native frameworks. once again, combining the IBM and crimson Hat teams should eliminate battle, however also cut back complete resources allotted to helping business Java."
IBM is acquiring red Hat in a $34 billion inventory deal. huge Blue referred to it may be the business's most huge acquisition and the most tremendous tech acquisition of 2018. The deal represents "a landmark second for each businesses and is a tremendous step forward in IBM's ongoing focus on high-price company, the transformation of their portfolio, and their management in the emerging era of AI and cloud."
John has been overlaying the excessive-tech beat from Silicon Valley and the San Francisco Bay enviornment for virtually two decades. He serves as Editor-at-colossal for software building traits (www.ADTMag.com) and contributes continually to Redmond journal, The know-how Horizons in education Journal, and Campus know-how. he is the author of greater than a dozen books, including The everything ebook to Social Media; The every little thing computer publication; Blobitecture: Waveform architecture and Digital Design; John Chambers and the Cisco manner; and Diablo: The respectable approach e-book.
at first glance, IBM’s surprise announcement that it plans to buy red Hat for $34 billion appears like a victory for open source, which has been an incredible driver of innovation in the IT market for many years. however, fresh strikes by renowned business open supply organizations to preclude licenses suggests that open supply’s future can be cloudier than many know.
IBM‘s blockbuster announcement on Sunday that it is going to buy pink Hat in what can be the third-biggest technology acquisition in heritage became cast as a conquest for open supply utility.
“Open source is the default option for contemporary IT solutions,” stated pink Hat CEO and President Jim Whitehurst. “becoming a member of forces with IBM will supply us with a greater stage of scale, substances and capabilities to accelerate the have an impact on of open source as the basis for digital transformation and convey pink Hat to an even wider audience.”
The acquisition offers big Blue extra ammo to battle the cloud wars, according Ginni Rometty, who's CEO, president, and chairman of IBM. “The acquisition of pink Hat is a video game-changer. It alterations every little thing about the cloud market,” Rometty referred to. “IBM will turn into the realm’s number one hybrid cloud provider…”
The deal highlights the significance of open supply in the tech sector, in line with Jim Zemlin, executive director of The Linux basis. “IBM has been an incredible open supply advocate for over two many years: promoting and defending Linux and open source at crucial features in its background,” he says.
It’s complicated to overstate the affect that open supply has had on the IT industry during the last two decades. due to the fact Linux emerged as a legitimate enterprise alternative for server operating programs close the flip of the millennia, momentum in the back of the open supply development mannequin has been very nearly unstoppable as it changed the direction of IT history.
Microsoft even joined the Open Invention community (OIN) recently and offered its whole patent portfolio to OIN participants, which is reasonably the alternate of route for a corporation whose CEO known as open supply a “melanoma” on innovation in 2001.Open Sourcing big facts
The trajectory of open source initiatives has been even more amazing within the large statistics ecosystem. A handful of influential Apache application foundation projects like Hadoop, Spark, Cassandra, Kafka, and Flink have all pushed the envelope in distributed computing one way or the other. (The fact that very nearly all of these frameworks anticipate a Linux operating equipment on the bottom of the stack additionally shows how the open source group builds off outdated successes.)
but open supply initiatives sponsored through ASF are only the delivery of open supply. There are influential non-Apache projects within the large information world, like TensorFlow, Presto, and Kubernetes, that use the Apache 2 license, while different initiatives, equivalent to Scikit-be trained, Numpy, and Pandas use a BSD license. MySQL, KNIME, and others use GPL, Citus makes use of the AGPL, while Postgres uses the PostgreSQL license.
in spite of the particular licenses used, open supply utility in widely wide-spread has been a boon to large data. in accordance with Brian Gracely, the director of product strategy at purple Hat, there was a shift in the area the place innovation happens: from proprietary products into the open supply realm.
“Our point of view is…that most of the innovation we’re seeing going on these days is happening within the open supply community, it’s going on in open supply,” Gracely informed Datanami past this month. “We continue to peer further and further VC money move into agencies around open supply. We’re seeing extra of the colossal corporations contributing to it. We’re seeing pink Hat most likely, however we’re additionally seeing Microsoft, Google, [and other] higher agencies contributing to that house.”
whereas corporations may equate “open source” with “innovation,” they’re also hunting for some support in pulling open source initiatives collectively, including the complicated work of stitching distinctive frameworks together to work as a cohesive entire. That’s partly what’s powering the commercial open supply company mannequin, the place organizations purchase subscriptions to business models of products from a red Hat, a Cloudera, or an Elastic.
“That’s what we’re all attempting to do: Make it as simple as viable, to take all the friction away,” Gracely spoke of. “I have an idea, I have a company issue, let me simply start engaged on it. The expertise will be there somewhere. It could be delivered by way of your IT neighborhood in the community or it can be delivered out of a public cloud carrier in a single means, shape, or form.”Cloud to the Rescue?
There’s little doubt that many have struggled to get open source frameworks like Hadoop, Spark, and Hive to feature easily and bring the huge information goods as marketed. Many agencies have spent years and hundreds of thousands of dollars to construct functions atop open source Hadoop distributions that may additionally have dozens of open source sub-accessories working beneath. Some have come out of this with creation clusters, whereas many others are nevertheless in construction mode.
actually, the issue in getting price out all this open supply innovation has driven many groups to the cloud, the place Amazon, Google, and Microsoft provided hosted versions of lots of the equal open source items which are pre-configured and pre-integrated. The hosted data warehousing company Snowflake, which recently brought in $450 million in undertaking funding, is having fun with somewhat of success at the moment on the expense of failed Hadoop implementations.
The glaring momentum of cloud providers turned into also evident in the contemporary information that Cloudera and Hortonworks are becoming a member of forces to create a single business with about $720 million in annual salary and a pair of,500 clients. The groups realized that, in its place of fighting each other for share of the marketplace for on-premise Hadoop distributions, their greatest competitor is the general public cloud.
the brand new Cloudera is hatching a plan to counteract the momentum of AWS, Google Cloud, and Microsoft Azure clouds by means of constructing a hybrid platform that runs atop Kubernetes containerization infrastructure. That capability to span from on-premise to the cloud – which the public cloud suppliers cannot do – appears to be one of the crucial main drivers of IBM’s hobby in purple Hat, too.Freeloading within the Cloud
There’s little question that open supply is accountable for a ton of innovation within the technology field. It’s additionally unleashed market forces which are nonetheless taking part in out these days. a few of these forces are having positive affects, but there are additionally some unexpected consequences that are beginning to emerge.
for example, each Redis Labs and MongoDB have recently scaled again their open supply licenses in response to what they perceive as freeloaders in the cloud.
In August, Redis Labs moved its Redis Modules from an AGPL license to a brand new license that combines Apache v2.0 with Commons Clause, which restricts the sale of coated application. The web have an effect on is that Redis Modules, including RediSearch, Redis Graph, ReJSON, ReBloom and Redis-ML aren't any longer open source software, even while the core Redis database continues with a BSD license.
In an August 22 blog submit, Redis Labs cofounder and CTO Yiftach Shoolman put the blame square on the cloud.
“Cloud suppliers had been taking knowledge the open supply group for years by way of selling (for hundreds of hundreds of thousands of dollars) cloud functions in line with open supply code they didn’t develop (e.g. Docker, Spark, Hadoop, Redis, Elasticsearch and others),” Shoolman writes. “This discourages the group from investing in constructing open supply code, because any capabilities advantage goes to cloud suppliers instead of the code developer or their sponsor.”
below the brand new license, Redis Labs shoppers can nonetheless construct and promote items atop the modules – they simply can’t promote the unmodified modules. “We agree with this licensing supports the open and free use of modules, whereas nevertheless keeping their rights over commercializing their assets,” Shoolman writes.
NoSQL database MongoDB is making an identical exchange to its open source license for its community edition, for equivalent motives. On October sixteen, the company introduced that it might use a brand new Server side Public License (SSPL) for MongoDB neighborhood Server. the brand new license would preserve all the same freedoms that the open supply group had with MongoDB beneath the AGPL license, but require “that any firm making an attempt to make the most MongoDB as a provider have to open supply the software that it uses to present such provider.”
“The market is more and more ingesting application as a service, developing an amazing opportunity to foster a brand new wave of amazing open supply server-aspect software,” MongoDB CTO and co-founder Eliot Horowitz pointed out in a statement. “unluckily, as soon as an open source assignment turns into exciting, it is too convenient for cloud providers who have not developed the utility to seize the entire price whereas contributing little back to the community.”
The alternate in sentiment around open source caught the attention of David Flower, the CEO of VoltDB, which develops an in-reminiscence disbursed relational database available as open supply with an AGPL license.
“just a few years lower back, proper open supply facts platforms had been extraordinarily conventional, as they enabled businesses to cost-with ease get a address on ‘large records,'” Flower tells Datanami via electronic mail. “however, these days’s digital transformation options have brought that style to a halt, as systems equivalent to Redis and MongoDB are reversing open source techniques and charging unhappy valued clientele to retain pace with the scale of information increase.”
It’s doubtful that the massive facts group will go returned to establishing proprietary software at this element. although, it’s clear that current business open supply thoughts aren't panning out as purveyors expected, so that it will doubtless result in extra restrictive licenses sooner or later. With the cloud poised to proceed gobbling up extra workloads, agencies may wish to pay extra attention to licenses in the back of the know-how that’s unlocking all the statistics innovation.
Is Hadoop officially useless?
Anatomy of a Hadoop challenge Failure
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We’ve entered a new phase in IT — in the world, really — where the combination of big data, artificial intelligence, and high performance computing is pushing the bounds of what’s possible in business and science, in work and play. What new creations this virtuous circle will yield has yet to be seen, but if what we’ve seen up to this point is any indication, it’s bound to impress.
When viewed as independent fields, you can see real gains made in big data management, AI, and HPC. It’s fair to say that each discipline is on its own upward trajectory. Each has its own challenges and its own roadmap for overcoming those challenges to bring success to their practitioners.
But when you view those three fields as a cohesive whole, one gets the sense that something much bigger is under foot. If technology is subject to evolutionary forces and moves forward in an evolutionary manner, it seems like we’re about to enter a period of punctuated equilibrium, driven by the collective power of those three engineers.
Together Tabor Communications Inc. CEO Tom Tabor, the three managing editors of TCI publications, including Alex Woodie of Datanami, Doug Black of EnterpriseAI, and Tiffany Trader of HPCwire, explored the virtuous cycle of big data, AI, and HPC this week during a panel discussion at Advanced Scale Forum, TCI’s annual event.
Here is an edited version of the editor’s discussion with Tabor:
Since the dawn of time, data management has posed a challenge to mankind. From oral histories and cave paintings to handwritten scrolls and Guttenberg’s printing press, we’ve always maxed out their data storage capabilities and run into I/O bottlenecks.
The creation of the World Wide Web 30 years supercharged the data management opportunity, as well as the challenge. By interconnecting the lives of 4 billion people through PCs, smart phones, and social media, we’ve witnessed a classic “network effect” unfold, fueling an exponential growth of data that’s expected to top 40 zettabytes by 2022.
Every minute of every day, Snapshat users share half a million photos, half a million Tweets are sent, half a million Facebook comments are lodged, and 4.1 million YouTube users are watching a video. Every minute, there are 16M text messages, 156 million emails — about two-thirds of which are spam (the rest, presumably, involve cat videos).
The problem, of course, is managing all this data, about 70% to 80% of which is unstructured, is hard. Before they can do fancy analytics, machine learning, or AI upon it, the data must be brought under control, protected, governed, cleansed, cataloged, and tracked.
This is the big data problem, and it’s why only a handful of companies have managed to find terminal velocity with their big data efforts. The difficult of managing big data is what Ali Ghodsi, the CEO of Databricks and one of the early developers of Apache Spark, called “AI’s 1% problem.”
There are a number of ways people are working to solve the data management problem. Things like data fabrics, data catalogs, federated databases, and tools for building repeatable data pipelines, will all be part of the solution. But the fact remains that there’s no silver bullet for data management. One must get in there, get dirty, and find what works to solve one’s own data challenges.
But once you wrangle the data, the potential benefits of AI are enormous.
In many ways, the hype over big data has shifted to artificial intelligence, which has become the driving force behind many organization’s data collection and data monetization efforts.
IBM’s CEO Ginni Rometty recently commented that the enterprise opportunity around AI is revolving around several dimensions as AI evolves from its first chapter, characterized by departmental level, PoC “random acts of digital” to chapter two: enterprise-wide AI. It will involve scaling AI, embedding it everywhere in the business, in a hybrid fashion that spans on-prem and cloud infrastructures, including infusing it into mission-critical applications.
A big challenge around AI, however, involves how to take AI from the prototype phase into production. IBM’s advice boils down to “think big, start small, and move fast.” As one department finds success with AI, confidence will build, and pretty soon, more departments will gravitate to the technology, and even the C suite will get behind it.
Another piece of advice for scaling AI comes from Ed Abbo, who is president and CIO at C3, which has implemented some of the largest AI applications outside of Silicon Valley. There are three main ingredients, Abbo said, including
“Unless you’ve got all three elements,” Abbo says, “you’re still in the prototype world.”
Despite the progress we’ve made through the “big data boom” and the current “hype rocket” that’s taking off around AI, the dirty little secret in the industry is that less than 2% of the world’s data is being utilized for analysis. If we’re going to push the needle past 2%, it’s going to take a lot of horsepower. Whether it’s scale-up capability supercomputing for the DOE or some variant of scale out – in the datacenter, in the cloud, at the edge, in their phones, their space shuttles, boats and cars — organizations will require massive amounts of compute.
Satisfying this need has resulted in a growing ecosystem of speedy interconnects, new storage technologies, and specialized processors (think GPUs, TPUs, FPGAs, and whatever else is coming down the pike).
Machine learning’s potential has been unlocked by the rise of big data and the availability of compute. This is the virtuous circle — the connection between big data, AI, and HPC — and it’s really at its infancy. The “virtuousness” of the cycle has been explained well by Andrew Ng of Landing AI, where better data creates new and better products, which in turn attracts more users, which yields even more insights. It’s a cycle that is undergirded by fast compute for training the models and for deploying them in the field for inferencing.
With so much hype circling about AI, big data, and emerging “edge to core” computing paradigms, it’s important to remind ourselves not to get too far over their skis. Nobody wants to repeat the “AI winter” mistakes of the past, or break out jars of Hadoop to smear about like some cure-all, as Intersect360 Research’s Addison Snell pointed out during his ASF panel.
With that in mind, it’s also worth reminding ourselves of Amara’s Law, which speaks about the difficulty of forecasting the effects of technology. The law states that they tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
The virtuous cycle of big data, AI, and HPC, use of which is mostly confined to the upper reaches of enterprise and the FAANG companies, has already yielded amazing gems (NLP, image and facial recognition, etc.) that have impacted the business world and the public domain. And when taking the long view, the odds look quite favorable that this cycle — which really is the essence and the epitome of human achievement as it pertains to information, software, data, and hardware — will evolve and proliferate to drive the state of the art and expand the realm of the possible for decades to come.
Global Hadoop Big Data Analytics Market: Snapshot
The expansion of the World Wide Web started from the later phase of the previous century and has shown an almost exponential rate of growth in this one. The genesis of search engines and indexing in early 2000s helped users of the Internet gain relevant data from the ever-increasing piles of information across the online highways. Technology has come a very long way from human-based search engines and results to automated crawlers that identify and index new and modified websites. As the Internet grew to contain millions and billions of pages, it became increasingly difficult and eventually impossible to simply index and manage the data.The inception of Hadoop in 2006 was a result of the research and development to find a solution for the accurate and efficient calculation of data amid the growing piles of it. Yahoo released Hadoop as an open-source project and today, most of the technologies and their frameworks are operated and maintained by the Apache Software Foundation, a non-profit organization. The necessity of Hadoop in Big Data analysis comes due to the inherent complexities and challenges involved in it, as well as the advantages that Hadoop can bring to the table. One of the key advantages of Hadoop is its capacity to process and store massive volumes of data at high speeds, making it easier to tackle the growing Big Data concern. Hadoop also provides high computing power, a decent tolerance for faults, and is highly flexible.
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Global Hadoop Big Data Analytics Market: Inclusive Insight
Hadoop is referred to an open source structure that allows big data analytics and storage and employs simple programming plans across dispersed clusters of computer. Big data is a huge volume of data and information and can be structured as well as unstructured. As the volume of information is extremely large, it cannot processed and stored by the conventional systems. Furthermore, it also supports institutions and organizations for understanding the trend and help in the decision making for the corporate level in real-time. With the advent of this solution, data can be stored, evaluated, and extracted in order to meet the desired objective. It aids in modernizing the models of business and maximize the revenue of the vendors.
The accumulation of information is done at an extensively high pace keeping in mind the velocity, volume, and variety owing to the daily operation of the respective industries. In a situation as such, organizations are likely to face several technical difficulties in the process of collecting, storing, analyzing, and reporting via shared and interlinked dashboards. Management of such information needs high-end data management equipment and suitably channelized flow of information. The companies require state-of-the-art for bog data resolves in order to collect, store, evaluate, picture, and predict the future from the available evidence attained from the massive volumes of information.
The market intelligence publication delves into the possible growth opportunities for the global Hadoop big data analytics market and the chronological growth of the market throughout the forecast period. It also uniquely provisions required data related to facers such as dynamics influencing the progress in all possible retrospective manner. Several ubiquitous and non-ubiquitous trends have also been mentioned in the study. An outlook of extensive nature keeping in mind the Porter’s five forces analysis has been provided to make the vendor landscape transparent to the reader. The report further reaches out to point out accomplishments related to R&D, acquisitions, mergers, and crucial partnerships and verifications. The companies in limelight have been analyzed on market shares, products, and key strategies.
Global Hadoop Big Data Analytics Market: Focus on Trends
The global market for Hadoop big data analytics is likely to be driven by the mounting demand for the installation of solutions for Hadoop big data analytics for the purpose of evaluating the extensively growing unstructured and structured data in order to attain beneficial insights from it. Furthermore, the rising need for information conversion by Internet of Things and big data by organizations for the purpose of decision making has substantially propelled the growth of the global Hadoop big data in the last couple of years. Convergence of Internet of Things and big data is another facet that is expected to fuel the growth of the market.
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The dearth in safety standards and the lack of appropriate big data supervisory structure is presumed to hinder the industry growth in the near future. The shortage of skilled personnel who are capable of converting machine language into operational solutions for advanced analytics and data warehousing is expected to further act as a restraint in the growth of the market.
Global Hadoop Big Data Analytics Market: Regional Outlook
Europe and North America are the possible regions which are likely to provide lucrative opportunities for growth to the vendors operating in the global hadoop big data analytics market. The developing economies in Asia Pacific are anticipated to emerge as profitable markets to capitalize on.
Global Hadoop Big Data Analytics Market: Companies Mentioned in the Report
A few of the leading companies in the market are Tableau Software Teradata Corporation, IBM Corporation, Amazon Web Services, Microsoft Corporation, and Pentaho Corporation.
About TMR ResearchTMR Research is a premier provider of customized market research and consulting services to business entities keen on succeeding in today’s supercharged economic climate. Armed with an experienced, dedicated, and dynamic team of analysts, they are redefining the way their clients’ conduct business by providing them with authoritative and trusted research studies in tune with the latest methodologies and market trends.
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This release was published on openPR.
ThinkstockShare What Is Endpoint Security Today? Big Data and Mobile Trends Point to the ‘Startpoint’ on Twitter Share What Is Endpoint Security Today? Big Data and Mobile Trends Point to the ‘Startpoint’ on Facebook Share What Is Endpoint Security Today? Big Data and Mobile Trends Point to the ‘Startpoint’ on LinkedIn
Where does “it” end?
We can probably all agree that securing and protecting the devices that communicate with their networks is a fair definition of endpoint security. Similarly, these devices are one of the best places to start when figuring out how to secure your networks because they are a particular pain point for security teams, responsible for 70 percent of breaches and a source of daily headaches.
But within this traditional definition, what, really, is the endpoint of today? How they answer that question has significant security implications.
While the conversation has focused on devices thus far, consider that the “2019 IBM X-Force Threat Intelligence Index Report” found that “human error continues to facilitate breaches,” and the 2018 edition of the report noted, “To err is human … When it comes to data security, the potentially detrimental impact of an inadvertent insider on IT security cannot be overstated.”
If human error and manipulation are sources of so much frustration, are the devices really the problem?Data Production and Consumption Are Going Big and Going Mobile
Let’s go through some quick points before diving deeper. Threat actors are taking advantage of devices with malicious intent, but they know that humans also facilitate breaches. Trends show a rising mobile-first preference, which has attackers moving away from malware. Phishing is increasing and remains the preferred attack method, according to Microsoft. In addition, as reported by Threatpost, with more and more organizations deploying mobile devices in professional settings, employees are using these devices for personal functions.
In fact, people are going mobile-first for their internet usage at such high rates that attackers are now tailoring their tactics for mobile viewing.
For example, Threatpost described a recent campaign in which threat actors used malicious tool kits to attack Verizon Wireless customers due to their deep understanding of the provider’s infrastructure, creating spoofs of seemingly legitimate subdomains. According to Lookout security researcher Jeremy Richards, this type of attack looks sloppy and obviously not legitimate when opened on a desktop. However, when opened on a mobile device, “it looks like what you would expect from a Verizon customer support application.”
For good measure, let’s add in some data production numbers. According to Forbes:
Clearly, part of the issue is manageability. You simply cannot keep your network secure without some kind of endpoint security solution because every minute counts once you have been breached. But big data and mobile are two factors that are testing the limits of manageability, giving way to a completely new meaning of identity and access management (IAM) and how they address the problem as a whole.Where Is the Endpoint?
From these trends, statistics and developments, they need to ask some questions that, depending on the answers, could completely change how they think about and manage endpoint security.Unconscious Data
Unlike in the past, when users were more conscious of their data production and consumption (access a stationary terminal, use it and walk away from it), today there is a great deal of unconscious data production and consumption (mobile devices are always on, always broadcasting and always connected to some secondary device such as a health monitor or watch). How does this situation of unconscious data production and consumption alter the meaning of endpoint security?Continuous Data
Previously, users were producing and consuming data in finite blocks, whereas today, there is a seemingly endless stream of continuous data consumption and production, mainly due to their mobile devices. This is one of the reasons they have big data, and the situation will only get worse as they integrate more IoT, wearable and peripheral devices — which, in part, explains why enterprises are adopting unified endpoint management (UEM) systems. Remember, humans are responsible for much of this data. How does this continuous stream of data alter the meaning of endpoint security?Device or User?
Finally, given recent trends — specifically the shift to social engineering attacks — ask yourself: Is the attacker going after the device or the human to gain access to the network? Put another way, is it the device that is vulnerable, or the user? It’s not clear-cut who or what the target is anymore.
I’d assert that both the device and the human are targets because they each have their own unique vulnerabilities, and the intent will determine the method of attack. But that’s probably the easiest answer, and the nuance of the question deserves attention.
It’s the attack’s intent that throws all of this into a conundrum. Because you don’t know what you don’t know, the attacker has the upper hand. This reinforces why beginning with endpoints is a great way to protect your network.
But I’ll take it one step further: If your endpoint is the device, then your “startpoint” is the human. Attackers are always tweaking their tactics to get past the technology to the human. They haven’t even begun to discuss the human/tech interaction, but Verizon’s “Insider Threat Report” described five very possible scenarios that illustrate that interaction:
So what’s the solution? To quote Lewis Carroll, “‘Begin at the beginning,’ the King said gravely, ‘and go on till you come to the end: then stop.'”If Machines Are the End, Then Humans Are Very Much the Start
I don’t expect a couple generations’ worth of people to begin thinking of endpoints any differently; an endpoint will continue to be defined as a device that communicates with a network. But that definition puts into their mind that the device is the terminus point of data production and consumption. It’s a bit of a mental barrier, whether they like it or not.
Big data and mobile trends indicate that devices are not the terminus points; they are. They make the final decision to click the link. They make the final decision to send that information. They make the final decision to produce and consume data and where from.
Therefore, let’s step up their endpoint game by using technology to manage security while spending equal time addressing the problems caused by the “startpoint” of the system, the human. Looking at these issues as a looping continuum of data flow that is mobile, instead of as distinct and discrete issues with terminus points, may better position us to reduce the risk they face.Tags: Big Data | Data Protection | Endpoint | Endpoint Protection | Endpoint Security | Human Error | Identity and Access Management (IAM) | Insider Threats | Mobile Devices | Mobile Security | Mobile Threats | Social Engineering | Unified Endpoint Management (UEM) George Platsis George Platsis works with the private, public and nonprofit sectors to address their strategic, operational and... 3 Posts Follow on Continue reading What’s new
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