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000-115 exam Dumps Source : Storage Sales V2

Test Code : 000-115
Test denomination : Storage Sales V2
Vendor denomination : IBM
: 126 existent Questions

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IBM Storage Sales V2

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The historical past of Hadoop | killexams.com existent Questions and Pass4sure dumps

The yarn starts off on a sunny afternoon, someday in 1997, when Doug reducing (“the man”) begun writing the first version of Lucene.

what is Lucene, you ask.

TLDR; frequently talking, it's what makes Google recrudesce outcomes with sub second latency.

Apache Lucene is a replete textual content search library. adequate, exceptional, but what is a replete text search library? feet search library is used to anatomize usual textual content with the protest of edifice an index. Index is an information constitution that maps each term to its zone in textual content, so that should you search for a term, it instantly is aware of the entire places the position that term happens.smartly, it’s just a itsy-bitsy extra advanced than that and the data structure is basically known as inverted or inverse index, however I received’t danger you with that stuff. The complete aspect of an index is to construct browsing speedy.imagine how usable would Google live if every time you looked for whatever, it went throughout the cyber web and collected effects. That’s a fairly ridiculous notion, correct?

It took chopping most effectual three months to fill whatever usable. just a few years went with the aid of and chopping, having skilled a “lifeless code syndrome” prior in his existence, desired different americans to expend his library, so in 2000, he open sourced Lucene to supply Forge beneath GPL license (later more permissive, LGPL). He became shocked through the variety of individuals that establish the library effectual and the volume of exceptional remarks and feature requests he got from those americans. just a 12 months later, in 2001, Lucene strikes to Apache utility groundwork.

by means of the sojourn of the 12 months, already having a thriving Apache Lucene community behind him, cutting turns his focus against indexing web pages. he is joined by means of university of Washington graduate student Mike Cafarella, to live able to index the total web. That pains yielded a brand modern Lucene subproject, known as Apache Nutch.Nutch is what is known as a web crawler (robot, bot, spider), a program that “crawls” the information superhighway, going from web page to page, by using following URLs between them. anything identical as should you surf the net and after some time live aware that you've got a myriad of opened tabs for your browser. that you may reason about a application that does the equal aspect, however follows each link from each and every and each web page it encounters. When it fetches a web page, Nutch uses Lucene to index the contents of the web page (to construct it “searchable”).

PageRank algorithm

a crucial algorithm, that’s used to rank internet pages by their relative significance, is called PageRank, after Larry page, who got here up with it (I’m critical, the denomination has nothing to enact with internet pages).It’s actually a simple and marvelous algorithm, which definitely counts what number of links from other pages on the web aspect to a page. The page that has the optimum count number is ranked the optimum (proven on proper of search consequences). Of path, that’s no longer the only system of making a election on page value, however’s actually essentially the most primary one.

all through the direction of a solitary 12 months, Google improves its ranking algorithm with some 5 to six hundred tweaks.

cutting and Cafarella made an excellent development. Having Nutch deployed on a solitary computer (single-core processor, 1GB of RAM, RAID degree 1 on eight difficult drives, amounting to 1TB, then charge $3 000) they managed to achieve a decent indexing cost of around 100 pages per second.

often, when purposes are developed, a crew just desires to Get the proof-of-thought off the ground, with efficiency and scalability basically as afterthoughts. So it’s no amaze that the same aspect took position to slicing and Cafarella. The undeniable fact that they fill got programmed Nutch to live deployed on a solitary laptop turned out to live a double-edged sword. On one aspect it simplified the operational facet of things, however on the other side it readily limited the replete variety of pages to one hundred million.

Understandably, no application (specially one deployed on hardware of that time) might fill indexed the total cyber web on a solitary desktop, so that they increased the variety of machines to 4. when you reckon that they did not fill any underlying cluster administration platform, they had to enact facts interchange between nodes and space allocation manually (disks would refill), which introduced ascetic operational challenge and required uniform oversight. Any additional increase in a number of machines would fill resulted in exponential upward shove of complexity. They desperately essential some thing that could rear the scalability issue off their shoulders and allow them to cope with the core problem of indexing the net.

The origins of HDFS

Being persistent in their pains to build an internet scale search engine, reducing and Cafarella got down to enhance Nutch. What they obligatory, as the groundwork of the system, turned into a allotted storage layer that satisfied the following necessities:

  • schemaless with out a predefined constitution, i.e. no rigid schema with tables and columns (and column forms and sizes)
  • long lasting as soon as information is written it can on no account live lost
  • capable of coping with component failure with out human intervention (e.g. CPU, disk, reminiscence, community, energy deliver, MB)
  • instantly rebalanced to even out disk house consumption during cluster
  • they fill got spent a couple of months trying to lucid up every solitary these problems after which, out of the bloom, in October 2003, Google posted the Google File materiel paper. after they examine the paper they were astonished. It contained blueprints for fixing the very equal issues they were combating.Having already been abysmal into the hardship area, they used the paper because the specification and commenced implementing it in Java. It took them more desirable participate of 2004, but they did a remarkable job. After it changed into comprehensive they named it Nutch distributed File system (NDFS).

    The main direct of this modern materiel changed into to summary cluster’s storage in order that it items itself as a solitary official file equipment, for that understanding hiding every solitary operational complexity from its clients.in accordance with GFS paper, NDFS become designed with relaxed consistency, which made it in a position to accepting concurrent writes to the same file with out locking every thing down into transactions, which as a result yielded large efficiency advantages. an extra first category feature of the modern gadget, as a result of the fact that it changed into capable of tackle disasters devoid of operator intervention, turned into that it could fill been developed out of affordable, commodity hardware components.

    How Google handled disk failure

    When Google turned into nevertheless in its early days they confronted the hardship of arduous disk failure in their information facilities. due to the fact their core trade was (and nonetheless is) “data”, they quite simply justified a election to gradually substitute their failing comparatively cheap disks with greater expensive, foremost ones. because the trade rose exponentially, so did the usual number of disks, and shortly, they counted challenging drives in hundreds of thousands. The determination yielded an extended disk lifestyles, in case you accept as suitable with each force by itself, but in a pool of hardware that giant it changed into nonetheless inevitable that disks fail, well-nigh by using the hour. That intended that they nevertheless needed to cope with the exact same problem, so that they steadily reverted back to usual, commodity difficult drives and instead decided to resolve the problem by using when you reckon that component failure not as exception, but as a daily prevalence.They had to ply the hardship on a far better degree, designing a application materiel that become capable of auto-repair itself.The GFS paper states:The gadget is built from many least expensive commodity accessories that regularly fail. It should normally monitor itself and detect, tolerate, and Get better immediately from component failures on a hobbies foundation.

    Following the GFS paper, cutting and Cafarella solved the problems of durability and fault-tolerance by using splitting every file into 64MB chunks and storing every chunk on 3 separate nodes (i.e. they established a gadget property referred to as replication aspect and set its default cost to 3). within the event of component failure the device would automatically notice the defect and re-replicate the chunks that resided on the failed node by using information from the other two match replicas.

    The failed node therefore, did nothing to the usual situation of NDFS. It best meant that chunks that had been saved on the failed node had two copies within the device for a short epoch of time, in its position of 3. as soon as the gadget used its inherent redundancy to redistribute records to different nodes, replication situation of these chunks restored lower back to three.

    MapReduce

    Now, when the operational aspect of things had been looked after, slicing and Cafarella begun exploring a lot of information processing fashions, trying to toil out which algorithm would superior apt the dispensed nature of NDFS. It changed into of the utmost magnitude that the brand modern algorithm had the equal scalability traits as NDFS. In other phrases, with a view to leverage the punch of NDFS, the algorithm needed to live in a position to obtain the maximum workable degree of parallelism (potential to usefully escape on diverse nodes at the same time). It needed to live near-linearly scalable, e.g. 8 machines, running algorithm that may live parallelized, needed to live 2 instances faster than 4 machines.

    Their thought become to by some means dispatch components of a software to every solitary nodes in a cluster and then, after nodes did their toil in parallel, compile every solitary those devices of labor and merge them into remaining effect.

    once again, Google comes up with an excellent theory. In December 2004 they posted a paper by Jeffrey Dean and Sanjay Ghemawat, named “MapReduce: Simplified facts Processing on massive Clusters”.

    Jeffrey Dean

    considered one of most prolific programmers of their time, whose toil at Google brought us MapReduce, LevelDB (its proponent in the Node ecosystem, Rod Vagg, developed LevelDOWN and LevelUP, that collectively benevolent the foundational layer for the entire collection of useful, better degree “database shapes”), Protocol Buffers, BigTable (Apache HBase, Apache Accumulo, …), and so forth.

    “That’s it”, their heroes mentioned, hitting themselves on the foreheads, “that’s outstanding, Map materials of a job to every solitary nodes and then in the reduction of (aggregate) slices of labor back to remaining influence”.

    The three leading issues that the MapReduce paper solved are:1. Parallelization — a way to parallelize the computation2. Distribution — a way to dispense the data3. Fault-tolerance — how to tackle program failure

    The core participate of MapReduce dealt with programmatic conclusion of these three issues, which without hardship hid away many of the complexities of dealing with significant scale distributed programs and allowed it to reveal a minimal API, which consisted most effectual of two capabilities. wait for it … ‘map’ and ‘reduce’.

    thought for MapReduce came from Lisp, so for any useful programming language enthusiast it would not fill been arduous to birth writing MapReduce classes after a short introductory working towards. That’s a testomony to how based the API definitely became, compared to previous distributed programming models.

    one of the vital key insights of MapReduce turned into that one should no longer live forced to mosey information to live able to process it. in its place, a program is shipped to the position the facts resides. that's a key differentiator, when compared to common information warehouse techniques and relational databases. There’s comfortably too a distinguished deal information to flood around.

    we can generalize that map takes key/value pair, applies some arbitrary transformation and returns an inventory of so referred to as intermediate key/price pairs. MapReduce then, in the back of the scenes, companies these pairs by way of key, which then whirl into input for the slash back characteristic. The reduce feature combines these values in some helpful approach and produces effect.

    Having heard how MapReduce works, your first intuition could smartly live that it's overly complex for an simple project of e.g. counting live aware frequency in some constitution of textual content or in every solitary probability calculating TF-IDF, the basis information constitution in search engines fancy google and yahoo. and you would, of course, live right. There are more straightforward and more intuitive methods (libraries) of fixing those problems, but abide in wit that MapReduce become designed to address terabytes and even petabytes of these sentences, from billions of internet websites, server logs, click streams, and so on.

    MapReduce fault-tolerance

    Excerpt from the MapReduce paper (a bit of paraphrased):

    The grasp pings each employee periodically. If no response is obtained from a worker in a specific amount of time, the grasp marks the employee as failed. Any map initiatives, in-development or accomplished via the failed worker are reset lower back to their preliminary, idle state, and hence whirl into eligible for scheduling on different employees.

    In July 2005, reducing pronounced that MapReduce is integrated into Nutch, as its underlying compute engine.

    the soar of Hadoop

    In February 2006, cutting pulled out GDFS and MapReduce out of the Nutch code basis and created a modern incubating challenge, under Lucene umbrella, which he named Hadoop. It consisted of Hadoop usual (core libraries), HDFS, eventually with its proper name : ), and MapReduce.

    At roughly the same time, at Yahoo!, a group of engineers led by means of Eric Baldeschwieler had their impartial participate of complications. This became going to live the fourth time they fill been to reimplement Yahoo!’s search backend system, written in C++. however the materiel became doing its job, with the aid of that point Yahoo!’s records scientists and researchers had already seen the advantages GFS and MapReduce delivered to Google and that they wanted the identical factor. “however that’s written in Java”, engineers protested, “How can it live more suitable than their effectual C++ system?”. because the drive from their bosses and the statistics crew grew, they made the determination to Take this brand new, open supply gadget into consideration. “exchange their construction materiel with this prototype?”, you may fill heard them announcing.

    Baldeschwieler and his crew chew over the situation for a long time and when it grew to become obtrusive that consensus was not going to live reached Baldeschwieler build his foot down and announced to his crew that they were going with Hadoop. In January, 2006 Yahoo! employed Doug reducing to assist the group construct the transition.

    Six months will mosey unless every person would recognize that relocating to Hadoop changed into the revise choice. on reflection, they could even argue that this very election turned into the one which saved Yahoo!. Take into account that Google, having appeared just a few years again with its blindingly quick and minimal search journey, changed into dominating the hunt market, while on the same time, Yahoo!, with its overstuffed domestic web page looked fancy a aspect from the previous. Their statistics science and analysis teams, with Hadoop at their fingertips, fill been in fact given freedom to play and learn the realm’s facts. Having prior to now been restrained to best subsets of that facts, Hadoop became refreshing. modern ideas sprung to lifestyles, yielding advancements and sparkling modern items perquisite through Yahoo!, reinvigorating the total business.

    We are now at 2007 and by means of this time other gigantic, net scale groups fill already caught sight of this modern and exciting platform. round this time, Twitter, fb, LinkedIn and many others started doing faultfinding toil with Hadoop and contributing back tooling and frameworks to the Hadoop open source ecosystem. In February, Yahoo! stated that their production Hadoop cluster is operating on 1000 nodes.

    2008 turned into a massive yr for Hadoop. at the start of the year Hadoop become silent a sub-task of Lucene at the Apache utility groundwork (ASF). In January, Hadoop graduated to the precise degree, because of its dedicated community of committers and maintainers. quickly, many modern auxiliary sub-initiatives began to seem, fancy HBase, database on exact of HDFS, which changed into prior to now hosted at SourceForge. ZooKeeper, disbursed device coordinator was introduced as Hadoop sub-assignment in can also. In October, Yahoo! contributed their bigger stage programming language on precise of MapReduce, Pig. fb contributed Hive, first incarnation of SQL on excellent of MapReduce.

    This changed into additionally the 12 months when the first knowledgeable system integrator dedicated to Hadoop was born. Cloudera changed into centered by a BerkeleyDB guy Mike Olson, Christophe Bisciglia from Google, Jeff Hamerbacher from fb and Amr Awadallah from Yahoo!.

    via March 2009, Amazon had already began featuring MapReduce internet hosting provider, Elastic MapReduce. In August slicing leaves Yahoo! and goes to toil for Cloudera, as a first-rate architect.

    In 2010, there become already a astronomical require for experienced Hadoop engineers. silent at Yahoo!, Baldeschwieler, at the position of VP of Hadoop application Engineering, took live aware how their fashioned Hadoop team turned into being solicited via other Hadoop gamers. Yahoo! wasn’t in a position to present advantages to their personage personnel as these modern startups may, fancy exorbitant salaries, fairness, bonuses and so on. The street forward did not recognize respectable. That turned into a significant problem for Yahoo!, and after some consideration, they determined to aid Baldeschwieler in launching a modern business. With economic backing from Yahoo!, Hortonworks became bootstrapped in June 2011, by way of Baldeschwieler and 7 of his colleagues, every solitary from Yahoo! and every solitary smartly based Apache Hadoop PMC (undertaking administration Committee) contributors, committed to open source. For its unequivocal stance that every solitary their toil will always live one hundred% open source, Hortonworks received group-broad acclamation.

    In 2012, Yahoo!’s Hadoop cluster counts forty two 000 nodes. variety of Hadoop contributors reaches 1200.

    earlier than Hadoop grew to become widespread, even storing giant quantities of structured facts become problematical. fiscal cross of tremendous information silos made groups discard non-basic information, holding only probably the most advantageous records. Hadoop revolutionized facts storage and made it feasible to retain every solitary of the data, no live counted how essential it may be.

    A remotely pertinent aspect note about relational databases

    This entire participate is in its entirety is the paraphrased prosperous Hickey’s discourse charge of values, which I wholeheartedly recommend.

    Relational databases fill been designed in 1960s, when a MB of disk storage had a charge of these days’s TB (sure, the storage means expanded 1,000,000 fold). They had been born out of obstacles of early computer systems. That became the time when IBM mainframe system/360 wondered the Earth. It had 1MB of RAM and 8MB of tape storage.

    IBM 3380 HD with barely seen USB stick in front can freight of recollection over time

    The cost of recollection diminished a million-fold considering the fact that the time relational databases had been invented. The reminiscence barriers are long long past, yet…

    Twenty years after the emergence of relational databases, a standard notebook would near with 128kB of RAM, 10MB of disk storage and, not to neglect 360kB within the variety of double-sided 5.25 inch floppy disk.

    those boundaries are long long gone, yet they nonetheless design methods as if they nevertheless follow.

    When there’s a transformation in the counsel system, they write a brand modern value over the faded one, as a result keeping only probably the most synchronous statistics. expertise, trends, predictions are every solitary derived from background, by gazing how a positive variable has changed over time. reckon about this for a minute. imagine what the zone would appear to live if they handiest knew essentially the most fresh cost of every thing. wealthy Hickey, writer of an excellent LISP-household, useful programming language, Clojure, in his discourse “cost of values” brings these elements domestic superbly. He calls it PLOP, zone oriented programming. the majority of their systems, each databases and programming languages are nevertheless concentrated on location, i.e. recollection handle, disk sector; however they now fill well-nigh limitless provide of reminiscence. because values are represented by way of reference, i.e. by way of their vicinity in reminiscence/database, so as to entry any charge in a shared atmosphere they fill to “stop the realm” unless they efficaciously retrieve it. What enact they actually deliver to a few third birthday party after they pass a reference to a mutable variable or a main key?

    Nothing, seeing that that region can besides live changed earlier than they Get to it.

    What was their earnings on this date, 5 years in the past? How much yellow, stuffed elephants fill they offered within the first 88 days of the previous 12 months? How has monthly revenue of spark plugs been fluctuating every solitary the way through the past 4 years? What fill been the effects of that advertising crusade they ran 8 years ago?

    possibly you could possibly boom that you just do, definitely, maintain a positive amount of background in your relational database. turned into it enjoyable writing a question that returns the existing values? Is that query speedy? Is it scalable?

    The astronomical edge of suggestions about inheritance is either discarded, kept in expensive, really fine systems or force outfitted perquisite into a relational database.

    however, we, because it individuals, being nearer to that infrastructure, took faith of their wants. supply ply systems and computing device logs don’t discard tips. can they entrust a brand modern supply file to source ply over the outdated one? enact they maintain simply the latest log message in their server logs? … Hickey asks in that speak.

    RDBs might neatly live replaced with “immutable databases”. One such database is prosperous Hickey’s own Datomic.

    Enter YARN the position Hadoop was missing probably the most, turned into knitting. despite the fact astronomical clusters of looms, powered by way of MapReduce fill been happily weaving away, it grew to become increasingly glaring that greater serious wool working materiel become long due. Enter YARN.

    Now critically, where Hadoop version 1 changed into truly missing probably the most, was its quite monolithic part, MapReduce. the foundation of every solitary complications turned into the proven fact that MapReduce had too many responsibilities. It became well-nigh in cost of every thing above HDFS layer, assigning cluster components and managing job execution (system), doing facts processing (engine) and interfacing in opposition t purchasers (API). due to this fact, there became no different election for greater degree frameworks other than to construct on excellent of MapReduce.

    The incontrovertible fact that MapReduce become batch oriented at its core hindered latency of application frameworks build on accurate of it. The efficiency of iterative queries, usually required with the aid of desktop getting to know and graph processing algorithms, took the greatest toll.

    although MapReduce fulfilled its mission of crunching up to now insurmountable volumes of facts, it became obvious that a more regular and greater resilient platform atop HDFS was quintessential.

    On Fri, 03 Aug 2012 07:51:39 GMT the eventual election was made. The subsequent technology information-processing framework, MapReduce v2, code named YARN (Yet another resource Negotiator), might live pulled out from MapReduce codebase and centered as a separate Hadoop sub-challenge. It has been a long street except this element, as toil on YARN (then called MR-297) became initiated lower back in 2006 by Arun Murthy from Yahoo!, later probably the most Hortonworks founders.

    types of workloads on exact of YARN

    with the protest to generalize processing potential, the useful resource administration, workflow management and fault-tolerance add-ons were faraway from MapReduce, a person-facing framework and transferred into YARN, quite simply decoupling cluster operations from the information pipeline.

    Emergence of YARN marked a turning component for Hadoop. It has democratized software framework area, spurring innovation throughout the ecosystem and yielding numerous new, aim-built frameworks. MapReduce turned into altered (in a fully backwards suitable approach) so that it now runs on suitable of YARN as certainly one of a variety of software frameworks.

    products and frameworks developed on excellent of YARN

    The sizzling topic matter in Hadoop circles is currently leading memory. There are plans to enact anything identical with leading recollection as what HDFS did to tough drives. separate courses of memory, slower and faster challenging disks, solid situation drives and main reminiscence (RAM) should silent every solitary live governed via YARN. software frameworks may silent live in a position to utilize various kinds of reminiscence for distinctive functions, as they descry healthy.

    Enter Spark

    Apache Spark brought a revolution to the BigData house.

    by together with streaming, laptop studying and graph processing capabilities, Spark made lots of the really fine facts processing platforms out of date. Having a unified framework and programming mannequin in a solitary platform greatly reduced the preparatory infrastructure investment, making Spark that a total lot accessible.

    Up before, identical astronomical statistics expend circumstances required a few items and often diverse programming languages, as a result involving separate developer groups, directors, code bases, trying out frameworks, etc.

    considering the fact that you caught with it and read the total article, i'm compelled to present my appreciation : )

    here’s the hyperlink and 39% off coupon code for my Spark in action booklet: bonaci39


    sooner stream, at ease Clouds And Spin Funding | killexams.com existent Questions and Pass4sure dumps

    This piece talks about information transport options from data expedition, Blockchain and at ease storage from IBM and collection B funding for Spin reminiscence.

    I had a briefing from facts excursion, Inc. who present clever records transport software for relocating records throughout networks at optimum pace. they fill got received an Emmy award for his or her know-how for media and enjoyment (M&E) functions. Their shoppers consist of M&E, lifestyles sciences, felony, oil and fuel, protection and information superhighway. The trade developed what it calls a Multipurpose Transaction Protocol (MTP), which is developed on the UDP protocol with advances in actual time packet mosey ply and mistake recuperation. The MTP is a substitute for the generic TCP protocol. in accordance with information day trip, MTP/IP allows it to construct expend of one hundred% of obtainable transmission route capability.

    data day trip Multipurpose Transaction Protocol (MTP)picture from records excursion Briefing

    facts excursion says that their product is less demanding to construct expend of than Aspera and scales without needing a lot of programming support. This velocity offers colossal trade benefits. They quoted Marissa Mayer from Google and Greg Linden from Amazon that an additional 0.5 2d in search page generation time dropped traffic by 20% and a 0.1 second extra latency at Amazon freight them 1% in income.

    IBM announced some modern IBM Storage options for containers and cloud storage, with a spotlight on Blockchain, IBM Cloud deepest and what the trade called, cyber resiliency. The enterprise is additionally extending synthetic intelligence (AI) capabilities and tools into its IBM FlashSystem A9000 family.

    IBM Cyber safety OfferingsImage from IBM Briefing

    The IBM Storage confess for IBM Blockchain is asserted to live an entire pre-tested and validated infrastructure confess for blockchain deployments.

    IBM Blockchain for StorageImage from IBM Briefing

    in response to IBM, the modern confess improves on- and off-chain records resiliency and efficiency with enterprise-confirmed NVMe-based mostly IBM FlashSystem 9100 or LinuxONE Rockhopper II infrastructure. It besides reduces recognize at various, construction and deployment time for each on/off-chain solutions, enhancing time to modern gains from days to hours. in addition it increases blockchain security with 100 percent application and facts encryption assist. finally, it raises records resiliency with a application-defined architecture that includes IBM Spectrum Virtualize, IBM Spectrum reproduction statistics administration and IBM Spectrum present protection to Plus.

    To tackle the infrastructure challenges of implementing the newest multi-cloud statistics analytics and AI functions, IBM introduced the IBM Storage solution for Analytics. This solution is in line with the recently brought IBM Cloud private for records offering, now supported on NVMe-based IBM FlashSystem 9100. This modern confess is said to unify and pace up statistics assortment, orchestration and analysis, whereas decreasing time to value. It simplifies Docker or Kubernetes container utilization for brand modern analytics-based functions and it increases records protection inside your private cloud by using assisting FIPS one hundred forty-2 encryption. It additionally leverages your on-premises records storage while adding cloud-based analytics equipment.

    Spin memory, Inc. (formerly generic at Spin transfer technologies, Inc.) announced that Ables Ventures from Tokyo Japan has joined as an extra investor of their collection B funding. Ables Ventures joins existing buyers utilized Ventures LLC, Arm, Allied Minds, Woodford funding administration and Invesco Asset management that fill been introduced in November 2018.

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    Chromebooks aren't perfect for everything, but they're awesome for a lot of things. And Samsung makes some excellent Chromebooks, though you usually pay a premium for their premium models.

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    The Chromebook Plus V2 is a Celeron-powered two-in-one, significance it offers both laptop and tablet modes -- and a few in between. It features a 12.2-inch screen, a built-in pen for drawing and note-taking, a pair of USB-C ports (plus a Type-A USB as well) and a 13-megapixel "world-facing" camera.

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    Although it's normally priced at $500, the Chromebook Plus V2 has only 32GB of onboard storage -- not uncommon given that the Chrome operating system relies heavily on cloud storage, but silent less than one might expect. Thankfully, it's easily expandable via a microSD slot and the aforementioned USB ports.

    Other dings embrace a non-backlit keyboard and the want of an onboard HDMI output. Those aren't deal-breakers, they're just annoyances.

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    ASUSTOR Inc. today released two modern NAS models, the AS3102T v2 and AS3204T v2, which ameliorate on the previous design of multimedia NAS, The AS3102T and AS3204T. The AS3102T v2 and AS3204T v2 are equipped with two Gigabit Ethernet ports, allowing the expend of network aggregation settings to double the network performance of the NAS. Increasing the amount of Ethernet ports allows arduous drives to construct better expend of their rated speed for those who requisite it, such as those in the AV industry. Their modern models besides support the Intel AES-NI instruction set, which increases encryption and decryption performance using the CPU. large amounts of data can now live encrypted far more quickly and helps increase data security and efficiency for storage, backup, and remote access.

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    The every solitary modern AS3102T v2 and AS3204T v2 will ship with the latest version of ADM, making backups and storage easier than ever. pick from more than 200 apps on App Central. ASUSTOR NAS devices are besides compatible with a wide variety of USB DACs and can live easily integrated into an existing HiFi audio set. AiMusic can easily play music remotely so that music can live listened to anywhere with an internet connection as well. ASUSTOR has besides improved remote connection features in the up and coming ADM 3.2. Ezconnect.to, ASUSTOR’s modern remote web login service does not requisite configuration of the router, instead only requiring an ASUSTOR Cloud ID. Just ilk in the designated URL with your Cloud ID and login to savor the convenience of accessing your NAS from anywhere.

    “The AS3102T v2 and AS3204T v2 integrate business-quality hardware into an affordable NAS to abet increase performance and reliability. Since some higher capacity drives contained only four screw holes, they fill redesigned the arduous drive bays to live compatible with drives that feature four or six screw holes, greatly increasing the flexibility of an ASUSTOR NAS. Therefore, those who requisite high performance and high storage capacity, the AS3102T v2 and AS3204T v2 are the best choices.”

    Johnny Chen – ASUSTOR product manager

    AS3102T V2 and AS3204T V2 Specifications
  • AS3102T v2: Intel Celeron 1.6GHz Dual Core CPU with a 2.16Ghz turbo
  • AS3204T v2: Intel Celeron 1.6GHz Quad Core CPU with a 2.24Ghz turbo
  • RAID 1 aggregated read/write speeds of up to 225.98 MB/s and 140.09 MB/s
  • RAID 5 aggregated read/write speeds of up to 226.20 MB/s and 220.34 MB/s
  • 2GB of DDR3L RAM
  • 2x Gigabit Ethernet ports
  • Front panel USB 3.0
  • 2x rear panel USB 3.0
  • HDMI 1.4B
  • IR receiver
  • Supports solitary drives, JBOD, RAID levels 0, 1, for two drives and 5, 6, 10 require four drives.
  • Supports AES-NI hardware encryption
  • Supports seamless system migration
  • Energy saving design puts the system to sleep (S3)
  • Supports up to 12TB on a solitary drive
  • Pricing And Availability

    The drives are expected to live released in the next month or so and pricing has yet to live announced.

    Share Submit



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