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CCD-333 - Cloudera Certified Developer for Apache Hadoop - Braindumps Information

Vendor : Cloudera
Exam Code : CCD-333
Exam Name : Cloudera Certified Developer for Apache Hadoop
Questions and Answers : 60 Q & A
Updated On : July 13, 2018
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CCD-333 Cloudera Certified Developer for Apache Hadoop

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Cloudera Certified Developer for

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Cloudera : pronounces New Apache Spark practising course for huge statistics builders | killexams.com real questions with brain dumps

PALO ALTO, CA--(Marketwired - Jul sixteen, 2014) -   Cloudera, the leader in enterprise analytic facts administration powered by means of Apache Hadoop™, these days announced the first fingers-on Apache Spark practicing route that will enrich builders' adventure with this groundbreaking new processing engine. The three-day direction, known as Cloudera Developer practising for Apache Spark, will put together builders and software engineers to build finished, unified applications that combine batch, streaming, and interactive analytics on all of their statistics. With Cloudera Developer training for Apache Spark, information authorities can take competencies of this next-generation framework's merits for velocity, ease of use, and advanced analytics to allow sooner company choices and improved user effects. 

Spark is an open supply records analytics framework firstly developed in the AMPLab at the tuition of California, Berkeley that enhances Hadoop as part of an business data hub. widely embraced via the open source neighborhood, huge statistics carriers, and records-intensive enterprises for its circulation processing capabilities and its support for complicated, iterative algorithms, Spark presents performance beneficial properties that allow purposes to run on the records in a Hadoop cluster at accelerates to 100 times sooner than traditional MapReduce classes. Cloudera changed into also the primary business to offer commercial support for Spark as a part of a Cloudera enterprise subscription and recently introduced a collaboration with Databricks, IBM, Intel, and MapR to develop aid for Spark as the regular information processing engine for the Hadoop ecosystem.

through instructor-led discussions and interactive, arms-on workouts, contributors will dive deep into the technical purposes of Spark to take into account the way it relates to the rest of the Hadoop ecosystem and write subtle parallel purposes. developers will learn precise-world top of the line practices drawn from Cloudera's work with Spark on one of the crucial biggest clusters in development and creation:

  • the usage of the Spark shell for interactive statistics evaluation
  • The features of Spark's Resilient distributed Datasets
  • How Spark runs on a cluster
  • Parallel programming with Spark
  • Writing Spark functions
  • Processing streaming statistics with Spark
  • "Spark offers clear advantages for realizing refined analytics and is directly becoming the future of data processing on Hadoop," referred to Sarah Sproehnle, vice chairman, education services, Cloudera. "With Spark, customers can understand instant company advantages. for example, Spark Streaming allows corporations to procedure live records as it arrives within the business information hub, in preference to having to wait to batch-manner it later. The indisputable fact that the identical codebase may also be used for streaming statistics and information-at-rest drastically reduces building time for huge statistics applications, rushing up time-to-insight by way of several orders of magnitude and reducing the want for expensive specialized methods. here's just one case the place the benefits of Spark have a right away affect on an organization's bottom line."

    Cloudera offers a wide selection of lessons to prepare developers to work with all aspects of large facts. Cloudera Developer practising for Apache Spark offers builders an opportunity to adventure the dramatic facts processing improvements Spark provides and build their capabilities with one of the crucial principal tools in an enterprise information hub. To study more about this new course providing:

  • Attend the webinar: Introduction to Spark Developer practicing join Diana Carroll, author of the new Cloudera direction, for an introductory webinar at 10am PT (1pm ET) on Wednesday, July 23, 2014. To register, please discuss with: http://college.Cloudera.com/webinar
  • discover greater particulars on the new direction and register for a class date: http://Cloudera.com/content/Cloudera/en/practicing/lessons/spark-practicing.html
  • View the full path overview and curriculum: http://Cloudera.com/content/dam/Cloudera/elements/PDF/Datasheet/Cloudera_Spark_Training.pdf
  • explore the developer getting to know direction in opposition t Hadoop expertise: http://Cloudera.com/content material/Cloudera/en/working towards/roles/builders.html
  • read about Cloudera certified knowledgeable credentials:  http://Cloudera.com/content material/Cloudera/en/training/certification.html
  • be taught more about Cloudera tuition's working towards and certification classes: http://Cloudera.com/content material/Cloudera/en/practising.html
  • What builders are announcing about Cloudera Developer practicing for Apache Spark:"The presentation structure of all Cloudera practising lessons is always very clear and progressive. via constructing on previous concepts with each new path, Cloudera tuition makes Spark Developer practising an important step in a developer's studying path. The labs have been extremely vital to regular huge records challenges and went past the normal introductory exercises I even have seen in other places. The school room dialogue led by way of the Cloudera teacher turned into beneficial, as every scholar came with distinctive use instances and stages of skills. The direction with no trouble reinforces the value of learning Spark Streaming and the Lambda structure for combining batch and streaming workloads inside a single atmosphere. After seeing how different participants answered to the presentation format of Cloudera's Spark Developer practicing route, I've in fact changed the manner i am going to latest the fundamental ideas in my e-book, Spark In action."-- Chris Fregly, creator of Spark in motion

    About ClouderaCloudera is revolutionizing business facts administration with the aid of providing the primary unified Platform for big facts, an business facts hub built on Apache Hadoop™. Cloudera offers businesses one location to save, technique and analyze all their statistics, empowering them to prolong the cost of latest investments while enabling fundamental new tips on how to derive value from their information. handiest Cloudera offers every thing mandatory on a adventure to an enterprise records hub, together with application for business critical facts challenges similar to storage, access, management, analysis, protection and search. because the leading educator of Hadoop gurus, Cloudera has knowledgeable over 22,000 people global. Over 1,000 partners and a seasoned knowledgeable features team help deliver improved time to price. eventually, best Cloudera offers proactive and predictive guide to run an enterprise records hub with confidence. leading companies in every industry plus excellent public sector groups globally run Cloudera in production. www.Cloudera.com

    join with ClouderaRead our blogs: http://www.Cloudera.com/weblog/ and http://imaginative and prescient.Cloudera.com/comply with Cloudera on Twitter: http://twitter.com/ClouderaFollow Cloudera tuition on Twitter: http://twitter.com/ClouderaUVisit us on facebook: http://www.fb.com/Cloudera

    Cloudera, Cloudera's Platform for huge information, Cloudera commercial enterprise facts Hub version, Cloudera commercial enterprise Flex version, Cloudera business fundamental edition and CDH are logos or registered logos of Cloudera Inc. within the u.s., and in jurisdictions all through the area. All different enterprise and product names may be exchange names or logos of their respective homeowners.


    Contexti Appointed to convey Cloudera practising for Apache Hadoop in Australia and Asia | killexams.com real questions with brain dumps

    SYDNEY--(company WIRE)--Contexti, a premier big facts Analytics consulting, techniques integration and practising business serving the Asia Pacific markets has these days been appointed as a certified Cloudera working towards companion for Australia and Asia, presenting Cloudera training and certification for Apache Hadoop.

    "We mandatory a local associate to assist meet the transforming into demand from Australia and Asia Pacific" said Omer Trajman, vp, know-how solutions at Cloudera. "With Contexti's large facts center of attention, and event working with Hadoop and related technologies, they are an incredible option as a provider of approved Cloudera working towards in the region".

    Contexti will present public and personal working towards, providing Cloudera's courses for Apache Hadoop, Apache HBase, Apache Hive and Pig.

    the general public training courses run from 2 to 4 days and are led by Contexti's licensed practitioner trainers. The classes cowl conception, hand-on lab work and prepare members for their certification checks. The courses are designed for builders with some programming experience, ideally Java. Contexti also offers tailor-made inner most classes for business purchasers.

    "Cloudera's practising and Certification programs are world classification and establish graduates as acknowledged Apache Hadoop gurus" talked about Sidney Minassian, Founder & CEO of Contexti. "Given Hadoop is now an important extension to the up to date business records warehouse and a key platform for massive statistics Analytics, our Cloudera working towards classes will help organizations up-skill their people to satisfy the growing demand for Hadoop skill"

    About Cloudera

    Cloudera, the leader in Apache Hadoop-primarily based utility and functions, allows for facts driven organisations to conveniently derive company value from all their structured and unstructured records. Cloudera's Distribution including Apache Hadoop (CDH), obtainable to down load for free at http://www.Cloudera.com/downloads, is probably the most finished, validated, strong and generally deployed distribution of Hadoop in business and non-industrial environments. For the fastest route to reliably the usage of this absolutely open source expertise in construction for massive records analytics and answering up to now un-addressable large questions, businesses can subscribe to Cloudera business, created from Cloudera supervisor application and Cloudera aid. Cloudera also offers practising and certification on Apache technologies, in addition to consulting capabilities. as the precise contributor to the Apache open source neighborhood and with tens of lots of nodes below management across consumers in monetary functions, govt, telecommunications, media, web, advertising, retail, power, bioinformatics, pharma/healthcare, institution analysis, oil and fuel and gaming, Cloudera's depth of journey and dedication to sharing knowledge are unrivaled. www.Cloudera.com

    About Contexti

    Contexti is a premier massive information Analytics consulting, systems integration and training business serving the Asia Pacific markets. We concentrate on Hadoop, NoSQL and connected technologies and superior statistics science recommendations. Our solutions clear up commercial enterprise information processing challenges and convey leap forward consumer, profits and possibility insights. For more counsel consult with Contexti.com

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    Cloudera’s business leading practicing classes Now available On Demand | killexams.com real questions with brain dumps

    October 10, 2016 13:03 ET | source: Cloudera Inc.

    PALO ALTO, Calif., Oct. 10, 2016 (GLOBE NEWSWIRE) -- Cloudera, the world issuer of the quickest, simplest, and most at ease statistics administration and analytics platform constructed on Apache Hadoop and the latest open supply applied sciences, these days introduced Cloudera OnDemand, a catalog of on-line, self-paced practising courses masking Cloudera's entire portfolio of industry-commonplace Apache Hadoop and Apache Spark practicing classes, enabling people to study concerning the newest huge data applied sciences in a searchable environment anytime, anywhere. Cloudera OnDemand allows for clients to study Hadoop at their personal tempo and devoid of the deserve to commute or take dissimilar days off to take part in reside practising routine.

    Cloudera OnDemand is the business’s eLearning platform that combines recorded lectures from Cloudera senior instructors with arms-on workout routines achieved in a cloud-based mostly lab environment. no matter if you’re an individual trying to extend your fingers-on capabilities of Hadoop, or a company in need of coaching teams of builders, administrators, analysts, and potential statistics scientists, Cloudera is making access to materials and tools seamless.

    “With Cloudera OnDemand, now we have made our working towards lessons more attainable to our valued clientele, as well as companies brooding about deploying a huge records solution,” observed Mark Morrissey, senior director, training courses at Cloudera. “With our cloud-based mostly lab ambiance, students don't need to be concerned about arduous hardware necessities, they comfortably want access to the net to delivery learning immediately. here's going to dramatically alternate the necessities of huge records practising and allow many extra developers to flourish in a huge data environment.”

    In a December 2015 article, CIO magazine covered the lack of technical expertise enterprise challenge. "Two-thirds of agencies that possess even probably the most advanced analytics capabilities can't appoint adequate people who can generate insights from corporate facts, according to new analysis from A.T. Kearney, which surveyed 430 senior executives. furthermore, companies will want 33 % extra large information ability over the subsequent 5 years [per] Khalid Khan, A.T. Kearney accomplice and co-author of the research.”

    Open supply systems like Cloudera business have develop into the de-facto commonplace for driving company transformation throughout the price chain, from compliance and value reduction to customer intimacy and aggressive knowledge. however, international corporations still face a potential hole - an absence of informed data specialists. Cloudera’s dedication to fixing this advantage gap is evidenced via its effective focus on training, a at all times increasing curriculum in keeping with needs and hobby, and their work with governments and universities all over using energetic involvement in open source software communities.

    For agencies with teams of users, Cloudera OnDemand gifts an excellent atmosphere for collaborative studying. within Cloudera's practising platform, students can have interaction with individuals from around the globe permitting for more specified dialogue, archived solutions to skills questions, and the possibility to learn about different use circumstances. consumers that wish to keep their conversations interior, have the alternative to create deepest groups.  

    Cloudera's OnDemand providing can also be purchased as a library where individuals get hold of access to Cloudera’s total suite of coaching content material for twelve months, or by particular person direction title purchasable in six-month subscriptions. All new lessons developed all the way through the subscription period are included with library subscriptions, as well as any updates to lessons.

    among the many courses being made attainable on demand are Developer training For Spark and Hadoop, Cloudera Administrator training, and records Analyst working towards. For extra suggestions on Cloudera OnDemand discuss with: http://www.Cloudera.com/training/ondemand-practising.html.

    About Cloudera

    Cloudera provides the modern facts management and analytics platform developed on Apache Hadoop and the newest open supply technologies. the realm’s leading corporations have faith Cloudera to support clear up their most challenging company problems with Cloudera commercial enterprise, the quickest, least difficult and most secure facts platform accessible for the contemporary world. Our consumers effectively seize, store, manner and analyze massive quantities of information, empowering them to make use of superior analytics to power enterprise decisions instantly, flexibly and at reduce charge than has been feasible before. To be sure our valued clientele are successful, we present complete support, working towards and skilled functions.  gain knowledge of more at Cloudera.com.

    join with Cloudera

    study our blogs: weblog.Cloudera.com and imaginative and prescient.Cloudera.com

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    Cloudera, Cloudera's Platform for large data, Cloudera business records Hub version, Cloudera enterprise Flex version, Cloudera commercial enterprise primary edition, Cloudera Navigator Optimizer and CDH are logos or registered logos of Cloudera Inc. within the united states, and in jurisdictions all through the area. All different business and product names may well be trademarks of their respective house owners.

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    Cloudera CCD-333 Exam (Cloudera Certified Developer for Apache Hadoop) Detailed Information

    Cloudera Certified Administrator for Apache Hadoop (CCAH)
    Training Certification
    | Hadoop Admin CCAH
    A Cloudera Certified Administrator for Apache Hadoop (CCAH) certification proves that you have demonstrated your technical knowledge, skills, and ability to configure, deploy, maintain, and secure an Apache Hadoop cluster.
    Cloudera Certified Administrator for Apache Hadoop (CCA-500)
    Number of Questions: 60 questions
    Time Limit: 90 minutes
    Passing Score: 70%
    Language: English, Japanese
    Price: USD $295
    REGISTER FOR CCA-500
    Exam Sections and Blueprint
    1. HDFS (17%)
    Describe the function of HDFS daemons
    Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing
    Identify current features of computing systems that motivate a system like Apache Hadoop
    Classify major goals of HDFS Design
    Given a scenario, identify appropriate use case for HDFS Federation
    Identify components and daemon of an HDFS HA-Quorum cluster
    Analyze the role of HDFS security (Kerberos)
    Determine the best data serialization choice for a given scenario
    Describe file read and write paths
    Identify the commands to manipulate files in the Hadoop File System Shell
    2. YARN (17%)
    Understand how to deploy core ecosystem components, including Spark, Impala, and Hive
    Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons
    Understand basic design strategy for YARN and Hadoop
    Determine how YARN handles resource allocations
    Identify the workflow of job running on YARN
    Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN
    3. Hadoop Cluster Planning (16%)
    Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster
    Analyze the choices in selecting an OS
    Understand kernel tuning and disk swapping
    Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
    Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA
    Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
    Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
    Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
    4. Hadoop Cluster Installation and Administration (25%)
    Given a scenario, identify how the cluster will handle disk and machine failures
    Analyze a logging configuration and logging configuration file format
    Understand the basics of Hadoop metrics and cluster health monitoring
    Identify the function and purpose of available tools for cluster monitoring
    Be able to install all the ecoystme components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Cloudera Manager, Sqoop, Hive, and Pig
    Identify the function and purpose of available tools for managing the Apache Hadoop file system
    5. Resource Management (10%)
    Understand the overall design goals of each of Hadoop schedulers
    Given a scenario, determine how the FIFO Scheduler allocates cluster resources
    Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN
    Given a scenario, determine how the Capacity Scheduler allocates cluster resources
    6. Monitoring and Logging (15%)
    Understand the functions and features of Hadoop’s metric collection abilities
    Analyze the NameNode and JobTracker Web UIs
    Understand how to monitor cluster daemons
    Identify and monitor CPU usage on master nodes
    Describe how to monitor swap and memory allocation on all nodes
    Identify how to view and manage Hadoop’s log files
    Interpret a log file
    Become a certified big data professional
    Demonstrate your expertise with the most sought-after technical skills. Big data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the big data talent gap.
    Training
    | Certification
    Certification
    Cloudera Certified Professional program (CCP)
    The industry's most demanding performance-based certifications, CCP evaluates and recognizes a candidate's mastery of the technical skills most sought after by employers.
    CCP Data Engineer
    CCP Data Engineers possesses the skills to develop reliable, autonomous, scalable data pipelines that result in optimized data sets for a variety of workloads.
    Learn More
    CCP Data Scientist
    Named one of the top five big data certifications, CCP Data Scientists have demonstrated the skills of an elite group of specialists working with big data. Candidates must prove their abilities under real-world conditions, designing and developing a production-ready data science solution that is peer-evaluated for its accuracy, scalability, and robustness.
    Learn More
    Cloudera Certified Associate (CCA)
    CCA exams test foundational skills and sets forth the groundwork for a candidate to achieve mastery under the CCP program
    CCA Spark and Hadoop Developer
    A CCA Spark and Hadoop Developer has proven his or her core developer skills to write and maintain Apache Spark and Apache Hadoop projects.
    Learn More
    Cloudera Certified Administrator for Apache Hadoop (CCAH)
    Individuals who earn CCAH have demonstrated the core systems administrator skills sought by companies and organizations deploying Apache Hadoop.
    How do I Register and Schedule my Cloudera exam?
    Follow the link on each exam page to the registration form. Once you complete your registration on university.cloudera.com, you will receive an email with instructions asking you to create an account at examslocal.com using the same email address you used to register with Cloudera. Once you create an account and log in on examslocal.com, navigate to "Schedule an Exam", and then enter "Cloudera" in the "Search Here" field. Select the exam you want to schedule and follow the instructions to schedule your exam.
    Where do I take Cloudera certification exams?
    Anywhere. All you need is a computer, a webcam, Chrome or Chromium browser, and an internet connection. For a full set of requirements, visit https://www.examslocal.com/ScheduleExam/Home/CompatibilityCheck
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    It is the sole responsibility of the test taker to maintain connectivity throughout the exam session. If connectivity is lost, for any reason, it is the responsibility of the test taker to reconnect and finish the exam within the scheduled time slot. No refunds or retakes will be given. Unfinished or abandoned exam sessions will be scored as a fail.
    Can I take the exam at a test center?
    Cloudera no longer offers exams in test centers or approves the delivery of our exams in test centers.
    Steps to schedule your exam
    Create an account at www.examslocal.com. You MUST use the exact same email you used to register on university.cloudera.com.
    Select the exam you purchased from the drop-down list (type Cloudera to find our exams).
    Choose a date and time you would like to take your exam. You must schedule a minimum of 24 hours in advance.
    Select a time slot for your exam
    Pass the compatibility tool and install the screen sharing Chrome Extension
    How do I reschedule an Exam Reservation?
    If you need to reschedule your exam, please sign in at https://www.examslocal.com, click on "My Exams", click on your scheduled exam and use the reschedule option. Email Innovative Exams at examsupport@examslocal.com, or call +1-888-504-9178, +1-312-612-1049 for additional support.
    What is your exam cancellation policy?
    If you wish to reschedule your exam, you must contact Innovative Exams at least 24 hours prior to your scheduled appointment. Rescheduling less than 24 hours prior to your appointment results in a forfeiture of your exam fees. All exams are non-refundable and non-transferable. All exam purchases are valid for one year from date of purchase.
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    To retrieve a forgotten password, please visit: https://www.examslocal.com/Account/LostPassword
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    You are marked as a no-show for the exam and you forfeit any fees you paid for the exam.
    What do I need on the day of my exam?
    One form of government issued photo identification (i.e. driver's license, passport). Any international passport or government issued form of identification must contain Western (English) characters. You will be required to provide a means of photo identification before the exam can be launched. If acceptable proof of identification is not provided to the proctor prior to the exam, you will be refused entry to the exam. You must also consent to having your photo taken. The ID will be used for identity verification only and will not be stored. The proctor cannot release the exam to you until identification has been successfully verified and you have agreed to the terms and conditions of the exam. No refund or rescheduling is provided when an exam cannot be started due to failure to provide proper identification.
    You must login to take the exam on a computer that meets the minimum requirements provided within the compatibility check: https://www.examslocal.com/ScheduleExam/Home/CompatibilityCheck
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    Benefits
    Individuals
    Performance-Based
    Employers want to hire candidates with proven skills. The CCP program lets you demonstrate your skills in a rigorous hands-on environment.
    Skills not Products
    Cloudera’s ecosystem is defined by choice and so are our exams. CCP exams test your skills and give you the freedom to use any tool on the cluster. You are given a customer problem, a large data set, a cluster, and a time limit. You choose the tools, languages, and approach. (see below for cluster configuration)
    Promote and Verify
    As a CCP, you've proven you possess skills where it matters most. To help you promote your achievement, Cloudera provides the following for all current CCP credential holders:
    A Unique profile link on certification.cloudera.com to promote your skills and achievements to your employer or potential employers which is also integrated to LinkedIn. (Example of a current CCP profile)
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    The big data space is rapidly evolving. CCP exams are constantly updated to reflect the skills and tools relevant for today and beyond. And because change is the only constant in open-source environments, Cloudera requires all CCP credentials holders to stay current with three-year mandatory re-testing.
    CCP Data Engineer Exam (DE575) Details
    Exam Question Format
    You are given five to eight customer problems each with a unique, large data set, a CDH cluster, and four hours. For each problem, you must implement a technical solution with a high degree of precision that meets all the requirements. You may use any tool or combination of tools on the cluster (see list below) -- you get to pick the tool(s) that are right for the job. You must possess enough industry knowledge to analyze the problem and arrive at an optimal approach given the time allowed. You need to know what you should do and then do it on a live cluster under rigorous conditions, including a time limit and while being watched by a proctor.
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    Candidates for CCP Data Engineer should have in-depth experience developing data engineering solutions and a high-level of mastery of the skills below. There are no other prerequisites.
    Register for DE575
    Required Skills
    Data Ingest
    The skills to transfer data between external systems and your cluster. This includes the following:
    Import and export data between an external RDBMS and your cluster, including the ability to import specific subsets, change the delimiter and file format of imported data during ingest, and alter the data access pattern or privileges.
    Ingest real-time and near-real time (NRT) streaming data into HDFS, including the ability to distribute to multiple data sources and convert data on ingest from one format to another.
    Load data into and out of HDFS using the Hadoop File System (FS) commands.
    Transform, Stage, Store
    Convert a set of data values in a given format stored in HDFS into new data values and/or a new data format and write them into HDFS or Hive/HCatalog. This includes the following skills:
    Convert data from one file format to another
    Write your data with compression
    Convert data from one set of values to another (e.g., Lat/Long to Postal Address using an external library)
    Change the data format of values in a data set
    Purge bad records from a data set, e.g., null values
    Deduplication and merge data
    Denormalize data from multiple disparate data sets
    Evolve an Avro or Parquet schema
    Partition an existing data set according to one or more partition keys
    Tune data for optimal query performance
    Data Analysis
    Filter, sort, join, aggregate, and/or transform one or more data sets in a given format stored in HDFS to produce a specified result. All of these tasks may include reading from Parquet, Avro, JSON, delimited text, and natural language text. The queries will include complex data types (e.g., array, map, struct), the implementation of external libraries, partitioned data, compressed data, and require the use of metadata from Hive/HCatalog.
    Write a query to aggregate multiple rows of data
    Write a query to calculate aggregate statistics (e.g., average or sum)
    Write a query to filter data
    Write a query that produces ranked or sorted data
    Write a query that joins multiple data sets
    Read and/or create a Hive or an HCatalog table from existing data in HDFS
    Workflow
    The ability to create and execute various jobs and actions that move data towards greater value and use in a system. This includes the following skills:
    Create and execute a linear workflow with actions that include Hadoop jobs, Hive jobs, Pig jobs, custom actions, etc.
    Create and execute a branching workflow with actions that include Hadoop jobs, Hive jobs, Pig jobs, custom action, etc.
    Orchestrate a workflow to execute regularly at predefined times, including workflows that have data dependencies
    CCP Data Scientist (Cloudera Certified Professional Program)
    CCP Data Scientists have demonstrated their skills in working with big data at an elite level. Candidates must prove their abilities on a live cluster with real data sets.
    Prove your expertise at the highest level
    Required Exams
    DS700 – Descriptive and Inferential Statistics on Big Data
    DS701 – Advanced Analytical Techniques on Big Data
    DS702 - Machine Learning at Scale
    CCA Spark and Hadoop Developer Exam (CCA175) Details
    Number of Questions: 10–12 performance-based (hands-on) tasks on CDH5 cluster. See below for full cluster configuration
    Time Limit: 120 minutes
    Passing Score: 70%
    Language: English, Japanese (forthcoming)
    Price: USD $295
    Exam Question Format
    Each CCA question requires you to solve a particular scenario. In some cases, a tool such as Impala or Hive may be used. In other cases, coding is required. In order to speed up development time of Spark questions, a template is often provided that contains a skeleton of the solution, asking the candidate to fill in the missing lines with functional code. This template is written in either Scala or Python.
    You are not required to use the template and may solve the scenario using a language you prefer. Be aware, however, that coding every problem from scratch may take more time than is allocated for the exam.
    Evaluation, Score Reporting, and Certificate
    Your exam is graded immediately upon submission and you are e-mailed a score report the same day as your exam. Your score report displays the problem number for each problem you attempted and a grade on that problem. If you fail a problem, the score report includes the criteria you failed (e.g., “Records contain incorrect data” or “Incorrect file format”). We do not report more information in order to protect the exam content. Read more about reviewing exam content on the FAQ.
    If you pass the exam, you receive a second e-mail within a few days of your exam with your digital certificate as a PDF, your license number, a Linkedin profile update, and a link to download your CCA logos for use in your personal business collateral and social media profiles
    Audience and Prerequisites
    There are no prerequisites required to take any Cloudera certification exam. The CCA Spark and Hadoop Developer exam (CCA175) follows the same objectives as Cloudera Developer Training for Spark and Hadoop and the training course is an excellent preparation for the exam.
    Register for CCA175
    Required Skills
    Data Ingest
    The skills to transfer data between external systems and your cluster. This includes the following:
    Import data from a MySQL database into HDFS using Sqoop
    Export data to a MySQL database from HDFS using Sqoop
    Change the delimiter and file format of data during import using Sqoop
    Ingest real-time and near-real time (NRT) streaming data into HDFS using Flume
    Load data into and out of HDFS using the Hadoop File System (FS) commands
    Transform, Stage, Store
    Convert a set of data values in a given format stored in HDFS into new data values and/or a new data format and write them into HDFS. This includes writing Spark applications in both Scala and Python (see note above on exam question format for more information on using either Scala or Python):
    Load data from HDFS and store results back to HDFS using Spark
    Join disparate datasets together using Spark
    Calculate aggregate statistics (e.g., average or sum) using Spark
    Filter data into a smaller dataset using Spark
    Write a query that produces ranked or sorted data using Spark
    Data Analysis
    Use Data Definition Language (DDL) to create tables in the Hive metastore for use by Hive and Impala.
    Read and/or create a table in the Hive metastore in a given schema
    Extract an Avro schema from a set of datafiles using avro-tools
    Create a table in the Hive metastore using the Avro file format and an external schema file
    Improve query performance by creating partitioned tables in the Hive metastore
    Evolve an Avro schema by changing JSON files

    Cloudera CCD-333

    CCD-333 exam :: Article by ArticleForgeCCD-333 examination Questions - pass in First attempt issuu enterprise emblem
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  • large records: using ArcGIS with Apache Hadoop. Erik Hoel and Mike Park

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  • 1 large records: the usage of ArcGIS with Apache Hadoop Erik Hoel and Mike Park

    2 outline Overview of Hadoop adding GIS capabilities to Hadoop Integrating Hadoop with ArcGIS

    three Apache Hadoop what's Hadoop? Hadoop is a scalable open supply framework for the disbursed processing of extraordinarily massive information units on clusters of commodity hardware - Maintained by using the Apache software groundwork - Assumes that hardware failures are typical Hadoop is primarily used for: - allotted storage - dispensed computation

    four Apache Hadoop what is Hadoop? historically, construction of Hadoop begun in 2005 as an open source implementation of a MapReduce framework - inspired through Google s MapReduce framework, as posted in a 2004 paper via Jeffrey Dean and Sanjay Ghemawat (Google Lab) - Doug cutting (Yahoo!) did the initial implementation Hadoop incorporates a allotted file system (HDFS), a scheduler and useful resource manager, and a MapReduce engine - MapReduce is a programming mannequin for processing huge records units in parallel on a disbursed cluster - Map() a method that performs filtering and sorting - cut back() a manner that performs a summary operation

    5 Apache Hadoop what is Hadoop? a number of frameworks were constructed extending Hadoop that are also part of Apache - Cassandra - a scalable multi-master database without a single aspects of failure - HBase - a scalable, disbursed database that supports structured facts storage for enormous tables - Hive - a knowledge warehouse infrastructure that offers records summarization and advert hoc querying - Pig - a high-stage records-move language and execution framework for parallel computation - ZooKeeper - a excessive-performance coordination carrier for dispensed functions

    6 MapReduce excessive level overview cut up map() mix Shuffle Partition variety map() reduce() part 1 information map() in the reduction of() map() half 2 hdfs://path/input hdfs://course/output

    7 Apache Hadoop MapReduce The word count number illustration Map 1. every line is break up into words 2. every be aware is written to the map with the observe because the key and a price of 1 Partition/kind/Shuffle 1. The output of the mapper is sorted and grouped in line with the important thing 2. each key and its associated values are given to a reducer cut back 1. For each key (word) given, sum up the values (counts) 2. Emit the word and its count number red purple blue red eco-friendly green blue red eco-friendly green blue blue blue Map Map Map pink 1 pink 1 blue 1 pink 1 eco-friendly 1 green 1 blue 1 purple 1 eco-friendly 1 green 1 blue 1 blue 1 blue 1 Partition Shuffle style eco-friendly 1 green 1 eco-friendly 1 crimson 1 purple 1 purple 1 pink 1 blue 1 blue 1 blue 1 blue 1 blue 1 in the reduction of in the reduction of eco-friendly three red four blue 5

    eight Apache Hadoop Hadoop Clusters natural Hadoop Clusters The Dredd Cluster

    9 including GIS capabilities to Hadoop

    10 Hadoop Cluster.jar

    11 adding GIS Capabilities to Hadoop established approach deserve to reduce big volumes of information into manageable datasets that will also be processed within the ArcGIS Platform - Clipping - Filtering - Grouping

    12 including GIS Capabilities to Hadoop Spatial statistics in Hadoop Spatial information in Hadoop can show up in a number of distinct codecs Comma Delimited ONTARIO, , RANCHO CUCAMONGA, , REDLANDS, , RIALTO, , running with SPRINGS, , the place described in diverse fields Tab Delimited ONTARIO aspect( , ) RANCHO CUCAMONGA element( , ) REDLANDS factor( , ) RIALTO point( , ) running SPRINGS with the point( , ) location described in regularly occurring textual content (WKT) JSON { attr : identify = ONTARIO , geometry : x :34.05, y : { attr : identify = RANCHO , geometry : x :34.12, y : { attr : name = REDLANDS , geometry : x :34.05, y : { attr : identify = RIALTO , geometry : x :34.11, y : { attr : identify = working , geometry : x :34.20, y : with Esri s JSON defining the location

    13 GIS equipment for Hadoop Esri on GitHub GIS tools for Hadoop tools samples Spatial Framework for Hadoop hive equipment and samples the usage of the open source resources that resolve specific complications Hive person-defined services for spatial processing JSON helper utilities spatial-sdk-hive.jar json spatial-sdk-json.jar Geoprocessing equipment for Hadoop HadoopTools.pyt Geometry API Java esri-geometry-api.jar Geoprocessing equipment that replica to/from Hadoop Convert to/from JSON Invoke Hadoop Jobs Java geometry library for spatial information processing

    14 GIS tools for Hadoop Java geometry API Topological operations - Buffer - Union - Convex Hull - incorporates -... In-memory indexing Accelerated geometries for relationship exams - Intersects, carries, nevertheless being maintained on Github https://github.com/esri/geometry-api-java

    15 GIS tools for Hadoop Java geometry API OperatorContains opcontains = OperatorContains.native(); for (Geometry geometry : somegeometrylist) opcontains.accelerategeometry(geometry, sref, GeometryAccelerationDegree.enumMedium); for (factor factor : somepointlist) boolean contains = opcontains.execute(geometry, point, sref, null); OperatorContains.deaccelerateGeometry(geometry);

    sixteen GIS equipment for Hadoop Hive spatial capabilities Apache Hive supports analysis of huge datasets in HDFS the usage of a SQL-like language (HiveQL) while also retaining full assist for MapReduce - maintains additional metadata for information saved in Hadoop - especially, schema definition that maps the normal data to rows and columns - enables SQL-like interplay with records using the Hive question Language (HQL) - pattern of Hive desk create commentary for essential CSV? Hive user-defined functions (UDF) that wrap geometry API operators Modeled on the ST_Geometry OGC compliant geometry type https://github.com/esri/spatial-framework-for-hadoop

    17 GIS tools for Hadoop Hive spatial capabilities Defining a table on CSV records with a spatial element CREATE desk IF now not EXISTS earthquakes ( earthquake_date STRING, latitude DOUBLE, longitude DOUBLE, magnitude DOUBLE) ROW layout DELIMITED FIELDS TERMINATED with the aid of ',' Spatial query the usage of the Hive UDFs select counties.identify, count number(*) cnt FROM counties verify if polygon includes point be part of earthquakes the place ST_Contains(counties.boundaryshape, ST_Point(earthquakes.longitude, earthquakes.latitude)) neighborhood by using counties.identify ORDER with the aid of cnt desc; construct a point from latitude and longitude https://github.com/esri/spatial-framework-for-hadoop

    18 GIS tools for Hadoop Geoprocessing tools Geoprocessing tools that enable ArcGIS to interact with big information stored in Hadoop - replica to HDFS Uploads data to HDFS - copy from HDFS Downloads files from HDFS - features to JSON Converts a characteristic class to a JSON file - JSON to elements Converts a JSON file to a function classification - Execute Workflow Executes Oozie workflows in Hadoop replica to HDFS copy from HDFS Execute Workflow Hadoop equipment facets to JSON JSON to facets https://github.com/esri/geoprocessing-equipment-for-hadoop

    19 Hadoop Cluster replica to HDFS copy from HDFS filter aspects to JSON JSON JSON JSON to facets influence

    20 DEMO point in Polygon Demo Mike Park

    21 combination Hotspots Step 1. Map/cut back to combination aspects into containers typical hotspots and large facts each and every feature is weighted, in part, via the values of its neighbors regional searches in very massive datasets may also be extremely expensive without a spatial index The influence of such analysis would have as many points because the original records mixture Hotspots points are aggregated and summarized into packing containers defined be an everyday integer grid The size of the summarized statistics is not plagued by the measurement of the customary statistics, only the variety of packing containers Hotspots can then be calculated on the summary information count 35 Min Max count number three 2 Min three count 35 Min 2 Max 7 2 Max count number 3 2 Min three Max 7 Step 2. Map/in the reduction of to calculate global values for bin aggregates count 5 5 Min 2 Max Step three. Map/cut back to calculate hotspots using boxes (following couple of slides) 3

    22 DEMO combination Hotspot analysis Mike Park

    23 Integrating Hadoop with ArcGIS

    24 Integrating Hadoop with ArcGIS moving forward Optimizing facts storage - What s incorrect with the latest facts storage - Sorting and sharding Spatial indexing facts supply Geoprocessing - Native implementations of key spatial statistical capabilities

    25 Optimizing statistics Storage Distribution of spatial information throughout nodes in a cluster hdfs:///path/to/dataset part-1.csv dredd0 part-2.csv dredd1 half-three.csv dredd2 processed on dredd1 processed on dredd0 dredd0 dredd1 dredd2

    26 aspect in Polygon in more element the usage of GIS equipment for Hadoop 1. The entire set of polygons is distributed to every node 2. each and every node builds an in-reminiscence spatial index for brief lookups 3. each element assigned to that node is bounced off the index to peer which polygon incorporates the factor 4. The nodes output their partial counts that are then combined right into a single result considerations: each checklist in the dataset needed to be processed, however best a subset of the information make a contribution to the answer The reminiscence requirements for the spatial index can be giant because the variety of polygons raises

    27 Optimizing information Storage Ordering and sharding raw data in Hadoop is not optimized for spatial queries and analysis concepts for optimized statistics storage 1. form the information in linearized house 2. break up the ordered statistics into equal density areas, called shards Shards make certain that the vast majority of elements are co-discovered on the same machine as their neighbors - This reduces community utilization when doing regional searches

    28 Hadoop and GIS Distribution of ordered spatial records across nodes in a cluster hdfs:///direction/to/dataset half-1 dredd0 half-2 dredd1 half-3 dredd2 dredd0 dredd1 dredd2 dredd3 dredd4

    29 Spatial Indexing distributed quadtree The quadtree index of a dataset consists of sub-indexes which are disbursed across the cluster each of those sub-indexes features to a shard with a 1-1 cardinality each sub-index is kept on the equal computing device as the shard that it indexes Shard records Index

    30 factor in Polygon indexed elements Counting elements in polygons the use of a spatially indexed dataset instead of send each polygon to each and every node, we handiest send a subset of the polygons each and every node queries the index for elements which are contained in its polygon subset The polygons from each and every node are then combined to provide the final result

    31 DEMO Filtering Areas of interest with facets Mike Park

    32 Conclusion Miscellaneous clever and insightful statements Overview of Hadoop including GIS capabilities to Hadoop Integrating Hadoop with ArcGIS

    33

    34 Caching I/O read CCD-333s (the place may still this go?) dredd0 dredd1 dredd2 dredd3 dredd

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