Cloudera offers enterprise and express versions of its Cloudera Distribution including Apache Hadoop. Clouderas view of the importance of qualified big data talent shines through the elements of its certification. It includes the following: Cloudera Certified Hadoop Developer (CCDH) This certification is for Developers who are responsible for coding, maintaining, and optimizing Apache Hadoop projects. The CCHD exam features questions that deliver a consistent exam experienceCloudera Certified Developer for Apache Hadoop (CCDH)Individuals who gain Cloudera Developer Certification for Apache Hadoop (CCDH) have exhibited their technical knowledge, skill, and ability to write, maintain, and optimize Apache Hadoop development projects. This certification establishes you as a trusted and invaluable resource for those looking for an Apache Hadoop expert. Cloudera Certification undeniably proves your ability to solve problems using Hadoop. The Motivation for HadoopProblems with traditional large-scale systemsIntroducing HadoopHadoopable problemsHadoop: Basic Concepts and HDFSThe Hadoop project and Hadoop componentsThe Hadoop Distributed File SystemIntroduction to MapReduceMapReduce overviewExample: WordCountMappersReducersHadoop Clusters and the Hadoop EcosystemHadoop cluster overviewHadoop jobs and tasksOther Hadoop ecosystem componentsWriting a MapReduce Program in JavaBasic MapReduce API ConceptsWriting MapReduce Drivers, Mappers, and Reducers in JavaSpeeding up Hadoop development by using eclipseDifferences between the old and new MapReduce APIsWriting a MapReduce Program Using StreamingWriting Mappers and Reducers with the streaming APIUnit Testing MapReduce ProgramsUnit testingThe JUnit and MRUnit testing frameworksWriting unit tests with MRUnitRunning unit testsDelving Deeper into the Hadoop APIUsing the ToolRunner classSetting up and tearing down Mappers and ReducersDecreasing the amount of intermediate data with combinersAccessing HDFS programmaticallyUsing the distributed cacheUsing the Hadoop APIs Library of Mappers, Reducers, and PartitionersPractical Development Tips and TechniquesStrategies for debugging MapReduce codeTesting MapReduce code locally by using LocalJobRunnerWriting and viewing log filesRetrieving job information with countersReusing objectsCreating map-only MapReduce jobsPartitioners and ReducersHow partitioners and Reducers work togetherDetermining the optimal number of Reducers for a jobWriting customer partitionersData Input and OutputCreating custom writable and WritableComparable implementationsSaving binary data using sequenceFile and Avro data filesIssues to consider when using file compressionImplementing custom InputFormats and OutputFormatsCommon MapReduce AlgorithmsSorting and searching large data setsIndexing dataComputing term frequency Inverse Document FrequencyCalculating word co-occurrencePerforming Secondary SortJoining Data Sets in MapReduce JobsWriting a Map-Side JoinWriting a Reduce-Side JoinIntegrating Hadoop into the Enterprise WorkflowIntegrating Hadoop into an existing enterpriseLoading data from an RDBMS into HDFS by using SqoopManaging real-time data using FlumeAccessing HDFS from legacy systems with FuseDFS and HttpFSAn Introduction to Hive, Imapala, and PigThe motivation for Hive, Impala, and PigHive overviewImpala overviewPig overviewChoosing Between Hive, Impala, and PigAn Introduction to OozieIntroduction to OozieCreating Oozie workflows
