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1Z0-475 exam Dumps Source : Oracle IT Architecture SOA 2013 Essentials
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Oracle Oracle IT Architecture SOA
what will information expertise techniques gape relish inner a mountainous company in 2025? Oracle these days made 10 predictions about that future, and this text will dig into only 1 of these: that “eighty% of every commercial enterprise (and mission-important) workloads will stream to the cloud with the aid of 2025.”
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Making any such prediction begs the question: So why haven’t these important, enterprise workloads already moved to cloud computing? Let’s dawn with a glimpse into what’s at stake for these agencies with extraordinarily high-price, excessive-efficiency applications.
“I lately spoke to a communications enterprise that runs a solitary company procedure on Oracle Cloud application that’s tied to $9 billion a 12 months in income,” says Peter Heller, a former functions architect who’s now an analyst in Oracle’s advertising arm.
Peter Heller is Oracle senior director for enterprise structure advertising and marketing.Oracle
The salary circulate is what concerns most, he says, “and it has to preserve working below every scenario,” and that contains tech complications—whether an outage, a security breach, or spike famous. “And it has to bring a under one-2nd response time for a very complicated account activation technique,” Heller says. The commerce “gained’t consequence any resolution that threatens that efficiency.”
That’s now not to avow these IT teams aren’t looking to cleave costs—they at every times are. truly, he says, IT groups “think a responsibility to at every times provide three issues: effectiveness, effectivity, and possibility mitigation,” Heller says. “Does it accomplish what it’s presupposed to? Is it a reasonable can suffuse to purchase and keep? Does it evade risk and consequence certain enterprise continuity? each determination they make, every impute of an answer, should soundless consist of every those things.”
With infrequent exceptions, companies won’t set these mission-essential workloads on first-technology clouds. “They simply weren’t developed for it,” Heller says. “They had been designed so ‘tenants’ of their cloud might partake commodity servers and storage to maximise their gains. And that’s much for disposable gape at various environments and low-chance workloads.” but for proximate true-time processing that underpins hundreds of thousands or billions of bucks of income? no longer so much, he says.
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If cloud suppliers stayed with this cloud architecture—shared, commodity hardware—that “80% by artery of 2025” cloud prediction would not ever arrive to pass, Heller says.
but issues are altering. 2nd-technology cloud architectures signify a fundamental rethinking of the frequent public cloud with the purpose to carry the compute energy, reliability, and protection to Run the most worrying commerce applications. Plus, they “go artery past the elementary efficiencies of early cloud infrastructures,” says Heller.
A more ambitious tackle Cloud ‘effectivity’
To Run a portfolio of enterprise functions in a first-generation cloud (or an on-premises statistics center), “you possess teams of highly professional individuals who control large pools or, ‘estates,’ of IT components,” including compute programs, networks, storage, and databases, Heller says. “They patch software, office upgrades, and maneuver safety dangers and every system of operational hiccups.”
for instance, a regular database property may consist of hundreds of builders with each and every having varied database subscriptions that they exercise for app construction and trying out across dozens of tasks, and then a whole bunch of production databases. “That’s a huge estate, but not the greatest via an extended shot,” Heller says.
A tenant of a first-generation cloud issuer has to interrogate some difficult questions about managing its property: Who applies patches and improvements? Who mitigates and responds to protection breaches? Who resizes the database and storage when limits are reached? “what number of individuals does your cloud company bespeak you hire to accomplish something about their service?” Heller asks.
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Oracle’s retort for its premier cloud database carrier is that it will deal with every these for its cloud tenants. In March 2018, Oracle released its self-using, independent database in Oracle Cloud. The autonomous database is designed to provision, tune, patch, replace, and restful itself without human intervention—all administrative tasks currently carried out in first-generation clouds with the aid of a tenant’s own extremely knowledgeable in-residence IT corpse of workers.
freed from those jobs, these personnel can subsist became loose on more creative projects, reminiscent of getting more value from an organization’s facts and assisting builders create new, records-fueled apps for shoppers and personnel.
Oracle’s large wager is that this revolutionary, self-riding effectivity and newfound liberty for skillful IT team of workers will entice businesses to finally circulation their most-demanding and demanding workloads to the cloud.
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The performance and company Continuity that precise groups Demand
reducing the continuing cost of possession is essential, says Heller, however when it comes to mission-crucial enterprise techniques, cost takes a back seat to predictable performance and enterprise continuity. companies possess constructed these performance degrees into their on-premises operations, and that they won’t flood to the cloud without them.
Oracle’s self sustaining Database is built on Oracle Exadata, a powerful, fault-tolerant hardware and utility gadget engineered specially to deliver ascetic performance for Oracle Database workloads. The on-premises and cloud-based types of Exadata are established with banks, telecoms, and other tremendous associations.
Heller believes that because the IT organizations in these companies are assured in this structure on premises, they’ll migrate to it in the cloud. “It suits their existing gadget of list,” says Heller, “which they'd painstakingly constructed to bring what the enterprise needs.”
And while 2nd-generation cloud architectures are designed to bring formidable commerce applications within the cloud, that’s now not ample. to satisfy actual-world provider stage agreements, they’re too designed to lend this efficiency and protection to cloud indigenous functions and integrate with ultimate on-premises functions.
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So, getting returned to that prediction: The intent Oracle believes that 80% of commercial enterprise (and mission-important) workloads will arrive to the cloud by artery of 2025, is that 2nd-generation, enterprise-grade cloud architectures, plus the extreme automation of platforms reminiscent of Oracle autonomous Database, will multiply the cost discount rates of the cloud, whereas letting enterprise IT groups meet their commitments for effectiveness, efficiency, and company continuity.
Plus, it'll supply them a path to eternal innovation that they could’t obtain using their personal on-premises data focus and application. however that’s a narrative for a different day.
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global services Oriented structure (SOA) Middleware Market size, status and Forecast 2019-2025; the latest file specializes in latest market repute, estimated forecast, expand alternatives, key market and suitable gamers.
The targets of this document is to latest the services Oriented architecture (SOA) Middleware progress in united states, Europe, China, Japan, Southeast Asia, India, and faultfinding & South america. This file helps to subsist awake key player of the capabilities Oriented architecture (SOA) Middleware market and analyse their edifice diagram and strategies, this record additionally define, relate and forecast the features Oriented architecture (SOA) Middleware market by means of product classification, applications and by using regions.
entry extra details about this report at: www.themarketreports.com/report/gl…tus-and-forecast
functions Oriented structure (SOA) Middleware Market is categorised on the groundwork of following product forms:
(every product classification is lined in the terms of boom expense, market dimension, regional market size analysis, market measurement forecast,.)
capabilities Oriented architecture (SOA) Middleware Market is studied for here applications:
(every utility is coated within the terms of market share, market measurement, regional market dimension analysis, market measurement forecast,.)
Key companies profiled in this record are Fujitsu Interstage, Hewlett Packard, IBM, Informatica, iWay application, Layer 7, Managed methods, Microsoft, Nastel technologies, Oracle, Perficient, progress utility, Rally application, crimson Hat, SAP, SOA utility, application AG, Tibco utility and more. These agencies are profiled within the terms of company basic particulars, company overview, product capabilities, ancient salary and recent tendencies.
access a replica of this top class research document at: www.themarketreports.com/document/1376715
through the a considerable number of theme matters covered in this document; it provides key insight of the market relish Market size and partake by means of regions, tendencies, driver, alternatives, profits, market partake of groups, their items, mergers & acquisitions, market dimension via kinds & purposes for u.s., Europe, China, Japan, Southeast Asia, India, and vital & South america. With this stalwart base, this file additionally provides the estimated forecast to 2025 by means of product varieties, functions and by means of areas.
Inquire additional about this document at: www.themarketreports.com/file/as…ur-query/1376715
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This post originally appeared on Public spend Forum.
On spend Matters, we track a broad range of procurement technology trends including a range of enablers (e.g., blazing lickety-split in-memory databases, service oriented architecture (SOA) models and software-as-a-service (SaaS) deliver models) to the actual functional tools themselves. These include e-sourcing suites to enable organizations to execute on strategic sourcing initiatives, spend analysis tools to identify savings and aggregation opportunities (and measure results), and shrink management solutions to insure that contracts are inked and then managed based on the perquisite terms and conditions (which are then tracked in as automated a manner as practicable through systems integration to ERP, eProcurement and other transactional tools that serve as systems of record).
Based on looking at these (and related) technology components and innovations in the actual solution capabilities themselves, they contemplate a number of trends converging in 2014, which they deem will consequence it the most distinguished year for the adoption of procurement and accounts payable technologies in the public sector on a national basis (including federal, state, local and higher education) that we’ve seen in history. But what areas are the most captivating (and important) that public sector procurement professionals should pay closest attention to? We’ll preview five trends below and insert five more in the coming weeks:
eProcurement and e-invoicing (P2P) technology competition, pricing and commerce models heat up. Between Ariba, SciQuest, Perfect, Oracle and many others competing for public sector contracts (as well as fresh entrants), and the fact that these technologies are now mature and can deliver on their promises of reducing unauthorized buying, directing spend to preferred suppliers, and reinforcing the ultimate requisitioning mile of strategic sourcing programs, eProcurement will subsist bigger in 2014 than it’s ever been in North America.
To read about the other four trends, click here.
One of the oldest debates in corporate IT is whether to adopt a solitary vendor application suite or deploy best-of-breed packages. CIOs and commerce users possess argued over the years about which approach delivers the example equipoise of user features, time to value, cost management and commerce benefits. It seems relish there's never been a black-and-white retort -- until now.
Never before has the case for best of breed applications been more compelling. Cloud computing has made it financially and operationally viable for a company to pick the world’s best application for every need, every user, and every commerce case. Companies no longer possess to sacrifice functionality for the sake of integration. You can possess your cake and eat it too.
After more than a decade of software industry consolidation and the dominance of “all in one” software suites, it is becoming increasingly difficult to warrant a suite purchase. commerce users were previously forced to accept inferior products in the interest of “seamless integration and simplicity”…that never materialized. Instead, every too often the attempts to install monolithic software suites from the likes of SAP and Oracle ended in projects that ran well over budget, took months or years longer than planned and left commerce users with legacy options, if the projects ever went live at all!
The cloud, with its open, resilient SOA-based application architecture means that integration is far easier and cheaper to manage today. This dramatically weakens the primary – and in some cases only – intuition that customers were buying applications from the suite vendors. Cloud software is built with integration and information exchange in mind. As cloud computing has matured, so has the "API economy." SaaS vendors can now extend their application’s usefulness through releasing APIs to developers, who in turn then offer rich, complementary functionality back to joint customers.
Progressive cloud vendors specializing in monetary services, telephony and communication services and talent management, among others, are handling the integration toil through pre-built connections to other leading applications and developing ecosystems of application partners.
It wasn’t long ago when IT departments were stuck with the reality of implementing modules from one vendor that worked together pretty well, yet didn't satisfy the needs of every department. Choosing an ERP system with stalwart financials meant the likelihood of weakness in other areas such as HR and CRM. Manufacturing companies often elect SAP, yet their finance department probably pined for Oracle.
However, if the company embarked on a best-of-breed path to satisfy diverse needs across commerce units, there were too significant added costs and headaches with highly orchestrated customizations and integration projects that could cost millions of dollars and years to realize. These integrations would then often possess to subsist redone around fresh releases of the respective applications, which meant IT departments were often forced to delay and/or skip fresh versions to maintain the related systems functional.
In the cloud, the Internet becomes the middleware. A company can pick and elect applications from many different vendors, integrate them through APIs or third-party cloud integration services and possess the suite they always wanted in weeks, not years.
As one example, The much Books Foundation, a nonprofit educational organization, integrated Intacct financials with SugarCRM, Magento e-commerce, and warehouse and shipping systems. An ROI analysis by Nucleus Research reports that the company is achieving an annual profit of $133,000 from cost savings and productivity improvements. Most importantly, the systems toil together, the commerce imperatives of the organization are satisfied and the costs weren’t prohibitive for the company along the way.
With subscription-based pricing, costs of a cloud-based commerce product combination are too lower and more aligned with actual demand. Costly consultants, hardware purchases, and expensive service agreements are no longer needed. Individual departments are pleased and IT is not spending excessive time and money maintaining connections and managing updates.
A commerce can then become brisk and responsive again, with their IT systems as a competitive advantage. When fresh functionality is needed, IT departments can badge a shrink with another vendor and obtain the fresh software up and running in hours or days. It's now the vendor’s job to note how they can integrate their product with a company's other systems; integration is no longer the shackles that threaten a CIO’s livelihood.
As the cloud wave continues to roll through the entire software landscape, more and more large public SaaS companies will emerge, such as Salesforce.comConcur Technologies, Inc., and Cornerstone OnDemand Best of breed SaaS companies are thriving, and are now forcing the legacy vendors relish Oracle, SAP and Microsoft to fend off attacks from every sides.
Of course, best of breed in the cloud isn't perfect itself. Many large companies possess legacy systems that they must maintain for a variety of reasons and integrating those inexorable systems with cloud systems requires some toil and expense. Thankfully, connecting an open cloud system with an on-premise legacy system is soundless easier than the cumbersome process of linking legacy to legacy apps, but it is something to diagram for none-the-less.
The cloud market itself is too evolving, and they will likely contemplate increased M&A activity as the legacy software suite vendors attempt to buy their artery into cloud computing by acquiring some of the best of breed leaders. They are too seeing the leading SaaS providers themselves looking to build and buy their artery into adjacent product areas to expand their offerings, as we’ve seen with Salesforce.com’s entry into marketing and support. Workday enjoyed an extremely successful IPO ultimate year and is already moving beyond their initial segment of core HR into offering monetary applications for the very large enterprises.
Yet these SaaS suite vendors won’t address every of the application needs of an enterprise and will requisite to travel much deeper in their core application areas to subsist competitive with the best of breed alternatives. The notion of an integrated suite solitary will no longer subsist adequate to sell through an inferior product. These vendors will requisite to subsist thoughtful when carving out upright slices and depend more on ecosystem partners to fill in the gaps. Even with these emerging, cloud-based "suite" commerce models, best-of-breed has a prominent role.
For the lion's partake of companies that crave choice, flexibility and liberty to create their own suite of best-in-class technologies from multiple vendors, the cloud is a dream arrive true. And for software entrepreneurs, there's no time relish the present to capture their own sweet slice of the cloud market. May the best cloud innovator, in every practicable vertical, win.
Byron Deeter is a former SaaS CEO and currently a confederate in the Menlo Park, California office of Bessemer Venture Partners. Deeter focuses on investments in the cloud computing and Internet sectors. He lead Bessemer’s investments in numerous cloud companies, including Intacct, Box, DocuSign, Eloqua, Cornerstone OnDemand, Twilio, and SendGrid.
Architectue of Hadoop and MapReduce
The early 1980s, 1990s were dominated by database giants Microsoft, Oracle, and IBM complying Codd’s 12 golden rules of relational database management systems keeping in view of the structure and cardinality of the databases. Oracle invented the proprietary database for analyzing the enterprise database conundrums. Google reinvented the wheel by majorly pushing the software applications into the open source communities. Google released several open source applications and elaborated the MapReduce technology in one of their research papers published along with Chubby and GFS (Google File System). According to the founder of Hadoop, Doug Cutting, Hadoop found its root in Nutch an open source Apache source code. Google released several applications and open source code from 2004 through 2006 describing Google File System and MapReduce. Doug Cutting was hired by Yahoo and moved away from Nutch project. By 2008, Hadoop went mainstream with web-scale architecture addressing several performance bottlenecks with parallel and distributed computing gaining large momentum. Both Apache Hadoop and MapReduce were the bloodlines of Google MapReduce and Google File System. However, it was heavily fueled and constructed with a weighty framework by Doug Cutting and his colleagues at Yahoo. Instead of storing the data on a disk, Hadoop stores the data on a file system with data nodes with designation node describing the location of the data.
Before dawn to exercise Hadoop and MapReduce, two of essential factors for businesses running mountainous data analytics in Hadoop clusters with MapReduce are the value of time and attribute of services. This includes having the perquisite infrastructure in Hadoop cluster ecosystem that can allow capturing, analyzing, and acting on the mountainous data to provide the benefits to the business. The commerce decisions are often made in a fraction of a second. Not acting in time could count the loss of commerce occasion for the corporations. Loss of occasion sometimes can imply the loss of revenues. The data always needs to stream in real-time for edifice strategic analytics culture in the organization. The value of the data can drop to zero when it’s not streamlined in real-time. This delay of data transmission can occur due to the architecture of Hadoop and MapReduce, and network latency. Putting every the valuable information related to mountainous data strategy, Hadoop, and MapReduce architecture in the perquisite time and perquisite attribute can count providing the solace to the corporations that are looking to taming the mountainous data. One of the most distinguished commerce cases is fraud-detection in the airplane crashes or power plants.
In today’s world, storing the raw data into data lakes powered by Hadoop is the focus of the commerce than converting the raw data into unique and valuable insights. This requires master data management and data governance in abysmal data lakes with Hadoop clusters. Some organizations build the reports that yield millions of lines of the output. It’s uphill job to scroll through every the lines to find the value in the mountainous data. Corporations are looking to expand the agility to build the data-driven culture in the data analytics world. The dynamics of the data lakes usage is quite unpredictable in the organization. As organizations maintain adding more workloads through batch jobs to Hadoop cluster, the complexity of the data increases significantly. When Hadoop cluster has thousands of nodes running hundreds of jobs, the system turns from order to chaos and the system complexity increases exponentially. This can drastically impact the overall commerce performance and key performance indicators of the enterprise. One of the root causes for such chaos in a data-driven organization could subsist overlooking the attribute of service (QoS). After deploying the data on Hadoop cluster, one must subsist able to measure the performance of Hadoop. QoS can aid in measuring the performance of Hadoop by breaking down the priorities of high priority batch jobs versus low priority batch jobs. Sometimes missing deadlines can count missing commerce opportunities this actually can lead to the sluggish performance of Hadoop clusters without valuable insights. This could subsist because the resources are underutilized in Hadoop clusters, when every the jobs are running on very nodes, without breaking the batch jobs into chunks due to requisite of optimization .
For the organization, missing deadlines could imply undependable Hadoop cluster ecosystem that can bring the commerce to grinding halt with long-running jobs completing late without complying the service flush agreements set by the top management of the organization. This every boils down to overbuilt hardware and underutilized CPU resources.
This requires not just day-to-day data transmission, but up-to-the-second real-time data transfers. Achieving real-time analytics for the corporations has never been easy. The mountainous data landscape is pretty complex. The data analyst needs to celebrate every the information sources around the organization and gain a 360 degrees overview of the enterprise in real-time seeing every the information sources connecting from different data sources. The traditional data warehouses used to capture and store the data from the enterprises as a long-term strategy of data analytics. The data lakes powered by Hadoop can process the unstructured, semi-structured, and sensors data. Both data lakes and data warehouses usually obtain the data stored through batch jobs. These batch jobs possess some latency, sometimes hours and sometimes days. Running a plenary update to the data lakes can subsist quite expensive from various databases and too can subsist time-consuming. Change Data Capture (CDC) can enable extracting the mountainous data with change-only delta mechanism from the transactions for real-time streaming into the data lake.
Concepts of MapReduce Framework
MapReduce is a programming model that resides as section of Apache Hadoop ecosystem. MapReduce renders API framework to handle large-scale workloads with design patterns and several libraries such as Hive, Pig, and Cascading that Run on top of MapReduce as an abstraction layer. Scalding is a programming language that is an extension of Cascading Java-based framework. The fundamental framework of MapReduce is about breaking down the large chunks of datasets on parallel and distributed computing clusters. MapReduce identifies the closest cluster instead of trying to reach farther clusters. JobTracker manages the identification of the location of the data then it performs the orchestration of the tasks map and reduce. Once the tasks are identified, they will subsist executed on TaskTracker.
Map will perform the algorithm to transform the data, and Reduce is leveraged to apply data aggregations of the mapped data. The large clustering and grouping of Map and Reduce activities together will trigger the communication only at the End of the task. This allows MapReduce to execute thousands of tasks executed parallelly for high-scalability and high-performance. For MapReduce, every the data is either a key or value. Every map job takes the key-value pairs as the inputs and after applying particular transformation rules, it produces fresh key-value pairs. Initially, MapReduce sorts the key for every the received inputs. The output map tasks are sorted and sent to reducers by the Shuffle process. Java can implement MapReduce with reduce class, map class, and configuration class for the job with output types for key and value pairs. Once a MapReduce job is built as a cookie-cutter template, it’s easier to apply for large-scale web data generated. In order to equate it to an SQL job with SQL query, the SQL is not efficient to create control flood of splitting the tasks into sub streams and parallel processing these tasks. This is where MapReduce reigns. It can perform the tasks by splitting onto distributed computing clusters.
Features of Hadoop
Hadoop is an open-source ecosystem framework that encompasses HDFS and MapReduce components. The primary dissimilarity between SQL and Hadoop is that, Hadoop is a massively parallel and distributing ecosystem that can split the tasks for processing the data. Hadoop can store the data on any number of data nodes that is fault-tolerant. Parallel processing and distributed computing are the main features of Hadoop framework. MapReduce performs the parallel processing, while HDFS performs the distributed computing.
On the timeline, Hadoop is the bloodline of Nutch project from Google GFS and MapReduce papers in early 2004s. In 2006, Hadoop-Project was born. Hadoop 0.19 in 2008 reached a terabyte benchmark. Hadoop released 0.19, 0.20 in April 2009. In 2010, Hadoop released two versions Hadoop 0.20.2 and 0.21. Hadoop 1.0 and 0.20.203 were released in 2011. In 2013, Hadoop 1.2.1 and 2.2 releases were distributed as open source.
· RDBMS proved to subsist an expensive ecosystem without parallel processing and distributed computing capabilities.
· Hadoop was released with the features of distributed computing capabilities and parallel processing that can Run on low-cost commodity servers.
· The commodity servers requisite not subsist high-scalable hardware.
· Hadoop allows spinning up and spinning down the nodes based on the data size volume requirements without having to intricate architecture.
· Hadoop can process the unstructured mountainous data unlike RDBMS that require specific schema of the database format.
· Hadoop is open-source ecosystem that does not require expensive licensing costs to obtain it up and running for enterprise-grade mountainous data applications.
· Hadoop maintains a huge open-source community information hubs and forums to further the advancements.
· Hadoop has calamity recovery planning that is fault-tolerant by quickly distributing the data to other nodes, when some nodes travel down.
· Hadoop can perform sophisticated and intricate algorithms for large-scale mountainous data.
· Hadoop can subsist leveraged for text analytics, processing the raw data in the configuration of unstructured and semi-structured formats for data warehousing.
· Hadoop can subsist leveraged for risk analytics, graph analytics, and consumer analytics.
· Hadoop ecosystem comprise several tools at different layers. HDFS supports the distributed computing and MapReduce supports the parallel processing of the data.
· The distributed programming on Hadoop ecosystem can subsist supported by Apache Pig and Hive. The data processing can subsist processed with ultra-blazing speeds with Apache Spark leveraging in-memory computing.
· HBase is a much NoSQL component that is section of Hadoop ecosystem.
· There are several data ingestion components that are section of Hadoop such as Apache Storm, Apache Sqoop, and Apache Flume.
· Apache Zookeeper can perform service-level programming.
· Apache Oozie can subsist leveraged for scheduling.
· Apache Mahout is the machine-learning arm of Hadoop.
· Apache Ambari can subsist leveraged for system deployment of Hadoop system.
· Hadoop Yarn can subsist leveraged for the scheduling of the jobs and managing cluster resources (Achari, 2015).
Pros and cons of the MapReduce framework
o MapReduce can perform parallel processing of the jobs with mainly two daemon processes of JobTracker and TaskTracker.
o MapReduce overcomes the serialization challenges of Java programming language by using the writeable interface for values and comparable interface for the keys.
o MapReduce has efficient architecture with mapper, shuffler, sorting, and reducer components for processing the tasks (Achari, 2015).
o MapReduce leverages speculative execution by monitoring the tasks by moving the tasks to a different node for processing.
o In order to identify the late performing tasks, MapReduce has to extensively monitor every job that can late down the system significantly.
o Speculative execution feature can adversely impact the performance especially when reducer tends to process millions of key value pairs and Sqoop by importing the data to fresh nodes to better the performance can result in duplication of the data records running the very tasks on several nodes.
o MapReduce is not highly scalable for tasks that require repetitive processing with multiple iterations.
Achari, S. (2015). Hadoop Essentials. UK: Packt Publishing — ebooks Account.
Arnold, S. E. (2010, October 29). MapReduce, Chubby and Hadoop. KMWorld, 19, 20. Retrieved from http://www.kmworld.com/Articles/News/News-Analysis/MapReduce-Chubby-and-Hadoop-70841.aspx
Chalkiopoulos, A. (2014). Programming MapReduce with Scalding. UK: Packt Publishing — ebooks Account.
Sakr, S., & Gaber, M. (2014). large Scale and mountainous Data: Processing and Management. Boca Raton, Florida: Auerbach Publications.
Wilkes, S. (2016). Why Time-Value of Data Matters. Retrieved April 24, 2016 , from http://insidebigdata.com/2016/04/08/why-time-value-of-data-matters/