|Exam Name||:||Oracle IT Architecture SOA 2013 Essentials|
|Questions and Answers||:||79 Q & A|
|Updated On||:||February 15, 2019|
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what will information expertise techniques look like inner a big 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 all 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 beginning 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 single 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 business “gained’t make any resolution that threatens that efficiency.”
That’s now not to assert these IT teams aren’t looking to cut costs—they at all times are. truly, he says, IT groups “think a responsibility to at all times provide three issues: effectiveness, effectivity, and possibility mitigation,” Heller says. “Does it do what it’s presupposed to? Is it a reasonable can charge to purchase and keep? Does it evade risk and make sure enterprise continuity? each determination they make, every attribute of an answer, should still consist of all those things.”
With infrequent exceptions, companies won’t put 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 share commodity servers and storage to maximise their gains. And that’s great for disposable look at various environments and low-chance workloads.” but for close 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 way of 2025” cloud prediction would not ever come 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 business applications. Plus, they “go way 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 have 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, function upgrades, and manipulate safety dangers and all method 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 use 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 ask some hard 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 indicate you hire to do something about their service?” Heller asks.
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Oracle’s answer for its premier cloud database carrier is that it will deal with all 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 comfortable 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 body of workers.
freed from those jobs, these personnel can be 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 freedom for proficient 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 have constructed these performance degrees into their on-premises operations, and that they won’t flow 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 severe 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 business applications within the cloud, that’s now not ample. to satisfy actual-world provider stage agreements, they’re also designed to lend this efficiency and protection to cloud native 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 come to the cloud by way 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 continual innovation that they could’t get using their personal on-premises data center 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, increase alternatives, key market and suitable gamers.
The targets of this document is to latest the services Oriented architecture (SOA) Middleware development in united states, Europe, China, Japan, Southeast Asia, India, and critical & South america. This file helps to be aware key player of the capabilities Oriented architecture (SOA) Middleware market and analyse their building plan and strategies, this record additionally define, describe 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, development 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 subject matters covered in this document; it provides key insight of the market like Market size and share by means of regions, tendencies, driver, alternatives, profits, market share 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 strong 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 fast 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 contract management solutions to insure that contracts are inked and then managed based on the right terms and conditions (which are then tracked in as automated a manner as possible 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, we see a number of trends converging in 2014, which we think will make it the most important 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 interesting (and important) that public sector procurement professionals should pay closest attention to? We’ll preview five trends below and introduce five more in the coming weeks:
To read about the other four trends, click here.
One of the oldest debates in corporate IT is whether to adopt a single vendor application suite or deploy best-of-breed packages. CIOs and business users have argued over the years about which approach delivers the ideal balance of user features, time to value, cost management and business benefits. It seems like there's never been a black-and-white answer -- 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 business case. Companies no longer have to sacrifice functionality for the sake of integration. You can have 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 justify a suite purchase. Business users were previously forced to accept inferior products in the interest of “seamless integration and simplicity”…that never materialized. Instead, all 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 business users with legacy options, if the projects ever went live at all!
The cloud, with its open, flexible 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 – reason 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 financial services, telephony and communication services and talent management, among others, are handling the integration work 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 strong financials meant the likelihood of weakness in other areas such as HR and CRM. Manufacturing companies often choose 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 business units, there were also 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 have to be redone around new releases of the respective applications, which meant IT departments were often forced to delay and/or skip new versions to keep the related systems functional.
In the cloud, the Internet becomes the middleware. A company can pick and choose applications from many different vendors, integrate them through APIs or third-party cloud integration services and have the suite they always wanted in weeks, not years.
As one example, The Great 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 benefit of $133,000 from cost savings and productivity improvements. Most importantly, the systems work together, the business 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 business product combination are also lower and more aligned with actual demand. Costly consultants, hardware purchases, and expensive service agreements are no longer needed. Individual departments are happy and IT is not spending excessive time and money maintaining connections and managing updates.
A business can then become nimble and responsive again, with their IT systems as a competitive advantage. When new functionality is needed, IT departments can sign a contract with another vendor and get the new software up and running in hours or days. It's now the vendor’s job to show 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 like Oracle, SAP and Microsoft to fend off attacks from all sides.
Of course, best of breed in the cloud isn't perfect itself. Many large companies have legacy systems that they must maintain for a variety of reasons and integrating those inflexible systems with cloud systems requires some work and expense. Thankfully, connecting an open cloud system with an on-premise legacy system is still easier than the cumbersome process of linking legacy to legacy apps, but it is something to plan for none-the-less.
The cloud market itself is also evolving, and we will likely see increased M&A activity as the legacy software suite vendors attempt to buy their way into cloud computing by acquiring some of the best of breed leaders. We are also seeing the leading SaaS providers themselves looking to build and buy their way 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 last year and is already moving beyond their initial segment of core HR into offering financial applications for the very large enterprises.
Yet these SaaS suite vendors won’t address all of the application needs of an enterprise and will need to go much deeper in their core application areas to be competitive with the best of breed alternatives. The notion of an integrated suite alone will no longer be sufficient to sell through an inferior product. These vendors will need to be thoughtful when carving out vertical slices and rely more on ecosystem partners to fill in the gaps. Even with these emerging, cloud-based "suite" business models, best-of-breed has a prominent role.
For the lion's share of companies that desire choice, flexibility and freedom to create their own suite of best-in-class technologies from multiple vendors, the cloud is a dream come true. And for software entrepreneurs, there's no time like the present to capture their own sweet slice of the cloud market. May the best cloud innovator, in every possible vertical, win.
Byron Deeter is a former SaaS CEO and currently a partner 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.
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 heavy 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 name node describing the location of the data.
Before beginning to practice Hadoop and MapReduce, two of essential factors for businesses running big data analytics in Hadoop clusters with MapReduce are the value of time and quality of services. This includes having the right infrastructure in Hadoop cluster ecosystem that can allow capturing, analyzing, and acting on the big data to provide the benefits to the business. The business decisions are often made in a fraction of a second. Not acting in time could mean the loss of business opportunity for the corporations. Loss of opportunity sometimes can imply the loss of revenues. The data always needs to stream in real-time for building 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 all the valuable information related to big data strategy, Hadoop, and MapReduce architecture in the right time and right quality can mean providing the comfort to the corporations that are looking to taming the big data. One of the most important business 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 business than converting the raw data into unique and valuable insights. This requires master data management and data governance in deep data lakes with Hadoop clusters. Some organizations build the reports that produce millions of lines of the output. It’s uphill task to scroll through all the lines to find the value in the big data. Corporations are looking to increase 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 keep 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 business performance and key performance indicators of the enterprise. One of the root causes for such chaos in a data-driven organization could be overlooking the quality of service (QoS). After deploying the data on Hadoop cluster, one must be 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 mean missing business opportunities this actually can lead to the sluggish performance of Hadoop clusters without valuable insights. This could be because the resources are underutilized in Hadoop clusters, when all the jobs are running on same nodes, without breaking the batch jobs into chunks due to lack of optimization .
For the organization, missing deadlines could imply undependable Hadoop cluster ecosystem that can bring the business to grinding halt with long-running jobs completing late without complying the service level agreements set by the top management of the organization. This all 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 big data landscape is pretty complex. The data analyst needs to observe all the information sources around the organization and gain a 360 degrees overview of the enterprise in real-time seeing all 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 get the data stored through batch jobs. These batch jobs have some latency, sometimes hours and sometimes days. Running a full update to the data lakes can be quite expensive from various databases and also can be time-consuming. Change Data Capture (CDC) can enable extracting the big 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 part 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 be 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, all the data is either a key or value. Every map task takes the key-value pairs as the inputs and after applying particular transformation rules, it produces new key-value pairs. Initially, MapReduce sorts the key for all 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 task 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 task with SQL query, the SQL is not efficient to create control flow 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 difference 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 be 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 need not be high-scalable hardware.
· Hadoop allows spinning up and spinning down the nodes based on the data size volume requirements without having to complex architecture.
· Hadoop can process the unstructured big data unlike RDBMS that require specific schema of the database format.
· Hadoop is open-source ecosystem that does not require expensive licensing costs to get it up and running for enterprise-grade big data applications.
· Hadoop maintains a huge open-source community knowledge hubs and forums to further the advancements.
· Hadoop has disaster recovery planning that is fault-tolerant by quickly distributing the data to other nodes, when some nodes go down.
· Hadoop can perform sophisticated and complex algorithms for large-scale big data.
· Hadoop can be leveraged for text analytics, processing the raw data in the form of unstructured and semi-structured formats for data warehousing.
· Hadoop can be 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 be supported by Apache Pig and Hive. The data processing can be processed with ultra-blazing speeds with Apache Spark leveraging in-memory computing.
· HBase is a great NoSQL component that is part of Hadoop ecosystem.
· There are several data ingestion components that are part of Hadoop such as Apache Storm, Apache Sqoop, and Apache Flume.
· Apache Zookeeper can perform service-level programming.
· Apache Oozie can be leveraged for scheduling.
· Apache Mahout is the machine-learning arm of Hadoop.
· Apache Ambari can be leveraged for system deployment of Hadoop system.
· Hadoop Yarn can be 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 slow performing tasks, MapReduce has to extensively monitor every task that can slow 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 new nodes to improve the performance can result in duplication of the data records running the same 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 Big 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/
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