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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 : December 13, 2017
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CCD-333 Questions and Answers

CCD-333


QUESTION: 54

Which two of the following are valid statements? (Choose two)


  1. HDFSis optimized for storing a large number of files smaller than the HDFS block size.

  2. HDFS has the Characteristic of supporting a "write once, read many" data access model.

  3. HDFS is a distributed file system that replaces ext3 or ext4 on Linux nodes in a Hadoop cluster. D. HDFS is a distributed file system that runs on top of native OS filesystems and is well suited to storage of very large data sets.


Answer: B, D


QUESTION: 55

You need to create a GUI application to help your company's sales people add and edit customer information. Would HDFS be appropriate for this customer information file?


  1. Yes, because HDFS isoptimized forrandom access writes.

  2. Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data.

  3. No, becauseHDFS can only be accessed by MapReduce applications.

  4. No, because HDFS is optimized for write-once, streaming access for relatively large files.


Answer: A


QUESTION: 56

Which of the following describes how a client reads a file from HDFS?


  1. Theclient queries the NameNode for the block location(s).TheNameNode returns the block location(s) to the client.The clientreadsthe datadirectly off the DataNode(s).

  2. The client queriesall DataNodes in parallel.The DataNode that contains the requested data responds directly to the client. The clientreads thedata directly off theDataNode.

  3. The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodesrespondto the NameNode, and the NameNode redirects the client to the DataNode thatholds the requested data block(s). The client then readsthe datadirectly off the DataNode.


  4. The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode that holds the requested data block. Data istransferred from the DataNode to the NameNode, and then from the NameNode to the client.


Answer: C


QUESTION: 57

You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses TextInputForma and splits each value (a line of text from an input file) into individual characters. For each one of these characters, you will emit the character as a key and as IntWritable as the value. Since this will produce proportionally more intermediate data than input data, which resources could you expect to be likely bottlenecks?


  1. Processor and RAM

  2. Processor and disk I/O

  3. Disk I/O and network I/O

  4. Processor and network I/O


Answer: B


QUESTION: 58

Which of the following statements best describes how a large (100 GB) file is stored in HDFS?


  1. The file is divided into variable size blocks, which are stored on multiple data nodes. Each block is replicated three timesby default.

  2. The file is replicated three times by default. Each ropy of the file is stored on a separate datanodes.

  3. The master copy of the file is stored on a single datanode. The replica copies are divided into fixed-size blocks, which are stored on multiple datanodes.

  4. The file is divided into fixed-size blocks, which are stored on multiple datanodes.Eachblock is replicated three times by default. Multiple blocks from the same file mightreside on the same datanode.

  5. The tile is divided into fixed-sizeblocks, which are stored on multiple datanodes.Eachblock is replicated three times by default.HDES guarantees that different blocks from the same file are never on the same datanode.


Answer: B


QUESTION: 59

Your cluster has 10 DataNodes, each with a single 1 TB hard drive. You utilize all your disk capacity for HDFS, reserving none for MapReduce. You implement default replication settings. What is the storage capacity of your Hadoop cluster (assuming no compression)?


  1. about 3TB

  2. about 5 TB

  3. about 10 TB

  4. about 11 TB


Answer: A


QUESTION: 60

You use the hadoop fs –put command to write a 300 MB file using an HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this file?


  1. They would see no content until the whole file is written and closed.

  2. They would see the content of the file through the last completed block.

  3. They would see the current state of the file, up to the last bit written by the command.

  4. They would see Hadoop throw an concurrentFileAccessException when they try to access this file.


Answer: A


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.
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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.
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Cloudera no longer offers exams in test centers or approves the delivery of our exams in test centers.
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Select the exam you purchased from the drop-down list (type Cloudera to find our exams).
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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)
CCP logo for business cards, résumés, and online profiles
Current
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 in order to maintain current CCP status and privileges.
Companies
Performance-Based
Cloudera’s hands-on exams require candidates to prove their skills on a live cluster, with real data, at scale. This means the CCP professional you hire or manage have skills where it matters.
Verified
The CCP program provides a way to find, validate, and build a team of qualified technical professionals
Current
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.
Audience and Prerequisites
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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