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000-115 Storage Sales V2

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000-115 exam Dumps Source : Storage Sales V2

Test Code : 000-115
Test cognomen : Storage Sales V2
Vendor cognomen : IBM
: 126 real Questions

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IBM Storage Sales V2

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The historical past of Hadoop | killexams.com real 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 revert outcomes with sub second latency.

Apache Lucene is a complete textual content search library. adequate, exceptional, but what is a complete text search library? feet search library is used to anatomize middling textual content with the intent of building an index. Index is an information constitution that maps each term to its region in textual content, so that should you search for a term, it instantly is aware of the entire places the status that term happens.smartly, it’s just a dinky extra advanced than that and the data structure is basically known as inverted or inverse index, however I received’t pains you with that stuff. The complete aspect of an index is to create 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 effective three months to believe 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 use 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 organize the library effective 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 suspension 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 application yielded a brand unique 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 contemplate 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 create 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 cognomen has nothing to achieve 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 import number is ranked the optimum (proven on proper of search consequences). Of path, that’s no longer the only mode of making a preference on page value, however’s actually essentially the most primary one.

all through the direction of a single 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 single computer (single-core processor, 1GB of RAM, RAID degree 1 on eight difficult drives, amounting to 1TB, then expense $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 find the proof-of-thought off the ground, with efficiency and scalability basically as afterthoughts. So it’s no dumbfound that the very aspect took status to slicing and Cafarella. The undeniable fact that they believe got programmed Nutch to live deployed on a single 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 complete variety of pages to one hundred million.

Understandably, no application (specially one deployed on hardware of that time) might believe indexed the total cyber web on a single desktop, so that they increased the variety of machines to 4. when you account that they did not believe any underlying cluster administration platform, they had to achieve facts interchange between nodes and space allocation manually (disks would refill), which introduced ascetic operational challenge and required uniform oversight. Any additional multiply in a number of machines would believe resulted in exponential upward thrust of complexity. They desperately essential some thing that could boost 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 application 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 believe got spent a yoke of months trying to limpid up utter these problems after which, out of the bloom, in October 2003, Google posted the Google File outfit paper. after they examine the paper they were astonished. It contained blueprints for fixing the very equal issues they were combating.Having already been profound into the rigor area, they used the paper because the specification and commenced implementing it in Java. It took them more desirable portion 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 unique outfit changed into to summary cluster’s storage in order that it items itself as a single official file equipment, for that understanding hiding utter 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 very file with out locking every thing down into transactions, which as a result yielded great efficiency advantages. an extra first category feature of the unique gadget, as a result of the fact that it changed into capable of tackle disasters devoid of operator intervention, turned into that it could believe 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 rigor of difficult disk failure in their information facilities. due to the fact their core traffic was (and nonetheless is) “data”, they quite simply justified a preference to gradually substitute their failing comparatively cheap disks with greater expensive, foremost ones. because the traffic rose exponentially, so did the middling 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 proper with each obligate 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 very problem, so that they steadily reverted back to usual, commodity difficult drives and instead decided to resolve the problem by using when you account that component failure not as exception, but as a daily prevalence.They had to exploit the rigor on a far better degree, designing a application outfit 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 find 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 several nodes (i.e. they established a gadget property referred to as replication aspect and set its default cost to 3). within the event of ingredient 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 accustomed 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 term of time, in its status 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 work out which algorithm would superior suitable the dispensed nature of NDFS. It changed into of the utmost magnitude that the brand unique algorithm had the equal scalability traits as NDFS. In other phrases, with a view to leverage the verve of NDFS, the algorithm needed to live in a position to obtain the maximum possible degree of parallelism (potential to usefully elope on diverse nodes at the very 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 utter nodes in a cluster and then, after nodes did their work in parallel, compile utter 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 work at Google brought us MapReduce, LevelDB (its proponent in the Node ecosystem, Rod Vagg, developed LevelDOWN and LevelUP, that collectively kindly 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 utter 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 course to parallelize the computation2. Distribution — a course to dole the data3. Fault-tolerance — how to tackle program failure

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

    thought for MapReduce came from Lisp, so for any useful programming language zealot it would not believe been difficult 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 dash information to live able to process it. in its place, a program is shipped to the status the facts resides. that's a key differentiator, when compared to common information warehouse techniques and relational databases. There’s comfortably too a considerable deal information to flow 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 course of key, which then rotate into input for the cleave 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 knotty for an simple project of e.g. counting live aware frequency in some build of textual content or in utter probability calculating TF-IDF, the groundwork information constitution in search engines enjoy google and yahoo. and you would, of course, live right. There are more straightforward and more intuitive methods (libraries) of fixing those problems, but suffer in intelligence 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 rotate into eligible for scheduling on different employees.

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

    the rise of Hadoop

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

    At roughly the very time, at Yahoo!, a group of engineers led by means of Eric Baldeschwieler had their objective partake of complications. This became going to live the fourth time they believe been to reimplement Yahoo!’s search backend system, written in C++. however the outfit 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 effective C++ system?”. because the drive from their bosses and the statistics crew grew, they made the determination to pick this brand new, open supply gadget into consideration. “exchange their construction outfit with this prototype?”, you may believe 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 status 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 create the transition.

    Six months will dash unless every person would recognize that relocating to Hadoop changed into the rectify choice. on reflection, they could even quarrel that this very preference turned into the one which saved Yahoo!. pick 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 very time, Yahoo!, with its overstuffed domestic web page looked enjoy a aspect from the previous. Their statistics science and analysis teams, with Hadoop at their fingertips, believe been in fact given freedom to play and determine the realm’s facts. Having prior to now been restrained to best subsets of that facts, Hadoop became refreshing. unique ideas sprung to lifestyles, yielding advancements and sparkling unique items birthright through Yahoo!, reinvigorating the total business.

    We are now at 2007 and by means of this time other gigantic, net scale groups believe already caught sight of this unique and exciting platform. round this time, Twitter, fb, LinkedIn and many others started doing censorious work 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 still 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 unique auxiliary sub-initiatives began to seem, enjoy 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 work for Cloudera, as a first-rate architect.

    In 2010, there become already a ample claim for experienced Hadoop engineers. still 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 luminary personnel as these unique startups may, enjoy exorbitant salaries, fairness, bonuses and so on. The street forward did not inspect respectable. That turned into a significant problem for Yahoo!, and after some consideration, they determined to aid Baldeschwieler in launching a unique business. With economic backing from Yahoo!, Hortonworks became bootstrapped in June 2011, by course of Baldeschwieler and 7 of his colleagues, utter from Yahoo! and utter smartly based Apache Hadoop PMC (undertaking administration Committee) contributors, committed to open source. For its unequivocal stance that utter their work 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 utter of the data, no live counted how essential it may be.

    A remotely material aspect note about relational databases

    This entire portion is in its entirety is the paraphrased prosperous Hickey’s speak expense of values, which I wholeheartedly recommend.

    Relational databases believe been designed in 1960s, when a MB of disk storage had a expense 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 imbue of remembrance over time

    The cost of remembrance 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 benchmark notebook would promote 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 unique value over the obsolete one, as a result keeping only probably the most concurrent statistics. expertise, trends, predictions are utter derived from background, by gazing how a limpid variable has changed over time. account about this for a minute. imagine what the region 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 speak “cost of values” brings these elements domestic superbly. He calls it PLOP, region oriented programming. the majority of their systems, each databases and programming languages are nevertheless concentrated on location, i.e. remembrance handle, disk sector; however they now believe well-nigh limitless provide of reminiscence. because values are represented by course of reference, i.e. by course of their vicinity in reminiscence/database, so as to entry any expense in a shared atmosphere they believe to “stop the realm” unless they efficaciously retrieve it. What achieve 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 furthermore live changed earlier than they find to it.

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

    possibly you could possibly allege that you just do, definitely, maintain a inevitable 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 ample edge of suggestions about legacy is either discarded, kept in expensive, really honorable systems or obligate outfitted birthright into a relational database.

    however, we, because it individuals, being nearer to that infrastructure, took keeping of their wants. supply exploit systems and computing device logs don’t discard tips. can they commit a brand unique supply file to source exploit over the outdated one? achieve 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 status Hadoop was missing probably the most, turned into knitting. despite the fact ample clusters of looms, powered by course of MapReduce believe been happily weaving away, it grew to become increasingly glaring that greater solemn wool working outfit 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 utter 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 preference 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 springy platform atop HDFS was quintessential.

    On Fri, 03 Aug 2012 07:51:39 GMT the terminal preference 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 work 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 intent 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 ingredient 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 theme matter in Hadoop circles is currently leading memory. There are plans to achieve anything identical with leading remembrance as what HDFS did to tough drives. several courses of memory, slower and faster challenging disks, solid situation drives and main reminiscence (RAM) should still utter live governed via YARN. software frameworks may still live in a position to utilize various kinds of reminiscence for distinctive functions, as they view 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 honorable facts processing platforms out of date. Having a unified framework and programming mannequin in a single platform greatly reduced the preliminary infrastructure investment, making Spark that a entire lot accessible.

    Up before, identical ample statistics use 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 entire article, i'm compelled to prove 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 real 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 ingenious records transport software for relocating records throughout networks at optimum pace. they believe 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 traffic developed what it calls a Multipurpose Transaction Protocol (MTP), which is developed on the UDP protocol with advances in actual time packet dash exploit and mistake recuperation. The MTP is a substitute for the generic TCP protocol. in accordance with information day trip, MTP/IP allows it to create use 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 create use of than Aspera and scales without needing a lot of programming support. This velocity offers colossal traffic 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 imbue them 1% in income.

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

    IBM Cyber safety OfferingsImage from IBM Briefing

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

    IBM Blockchain for StorageImage from IBM Briefing

    in response to IBM, the unique retort improves on- and off-chain records resiliency and efficiency with enterprise-confirmed NVMe-based mostly IBM FlashSystem 9100 or LinuxONE Rockhopper II infrastructure. It furthermore reduces inspect at various, construction and deployment time for each on/off-chain solutions, enhancing time to unique 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 unique retort 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 unique 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 believe been introduced in November 2018.

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    Chromebooks aren't flawless 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 still 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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    Multiple times a week, they find emails, comments and messages asking “what’s the best Chromebook for this expense or that price.” Often times, that’s a difficult query to retort because so many factors promote into play such as use-case, location and personal preferences.

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    ASUSTOR Inc. today released two unique NAS models, the AS3102T v2 and AS3204T v2, which improve 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 use of network aggregation settings to double the network performance of the NAS. Increasing the amount of Ethernet ports allows difficult drives to create better use of their rated speed for those who necessity it, such as those in the AV industry. Their unique models furthermore champion the Intel AES-NI instruction set, which increases encryption and decryption performance using the CPU. great amounts of data can now live encrypted far more quickly and helps multiply data security and efficiency for storage, backup, and remote access.

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    The utter unique AS3102T v2 and AS3204T v2 will ship with the latest version of ADM, making backups and storage easier than ever. choose from more than 200 apps on App Central. ASUSTOR NAS devices are furthermore 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 furthermore improved remote connection features in the up and coming ADM 3.2. Ezconnect.to, ASUSTOR’s unique remote web login service does not necessity configuration of the router, instead only requiring an ASUSTOR Cloud ID. Just kind in the designated URL with your Cloud ID and login to bask in the convenience of accessing your NAS from anywhere.

    “The AS3102T v2 and AS3204T v2 integrate business-quality hardware into an affordable NAS to wait on multiply performance and reliability. Since some higher capacity drives contained only four screw holes, they believe redesigned the difficult 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 necessity towering performance and towering 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 single 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 single 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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