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Cloudera Hadoop Administration Training Course

Cloudera Hadoop Administration Online Training by BIT will help you master Hadoo..

  • 25000 22000
What you will learn
  • Cloudera Manager features that make managing your clusters easier, such as aggregated logging, configuration & resource...
  • Configuring & deploying production-scale clusters that provide key Hadoop-related services, include YARN, HDFS, Impala,...
  • Determining the correct hardware and infrastructure for your cluster
  • Proper cluster configuration and deployment to integrate with the data center
  • Ingesting, storing, and accessing data in HDFS, Kudu, and cloud object stores such as Amazon S3
  • How to load file-based and streaming data into the cluster using Kafka and Flume
  • Configuring automatic resource management to ensure service-level agreements are met for multiple users of a cluster
Hadoop Administration Training Course

Hadoop Administration Professional Online Training Program is a comprehensive Ha..

  • 25000 22000
What you will learn
  • Describe the fundamentals and components of Hadoop
  • Elucidate the features, architecture, security considerations of Hadoop Distributed File System (HDFS)
  • Provide an overview of Hadoop Ecosystem covering different tools for integration, analysis, data storage and retrieval
  • Understand the features, concepts, architecture of MapReduce
  • Plan, install, and configure Hadoop. Practice Hadoop security system and configure Kerberos Security
  • Manage and schedule jobs to be executed in Hadoop system.
  • Install and manage other Hadoop clusters including Pig, Hive, HBase, Sqoop, HDFS
  • Utilize best practices for deploying, managing, and monitoring Hadoop clusters
Big Data Hadoop Training Course

Big Data Hadoop Professional Online Training Program is curated by Hadoop exper..

  • 40000 35000
What you will learn
  • Role of Relational Database Management System (RDBMS) and Grid computing
  • Concepts of MapReduce and HDFS
  • Using Hadoop I/O to write MapReduce programs
  • Develop MapReduce applications to solve the problems
  • Set up Hadoop cluster and administer
  • Hive, a data warehouse software, for querying and managing large datasets residing in distributed storage
  • Use of Sqoop in controlling the import and consistency
  • Spark, Spark SQL, Streaming, Data Frame, RDD, GraphX and MLlib writing Spark applications
  • Hadoop testing applications using MRUnit and other automation tools
  • Configuring ETL tools like Pentaho/Talend to work with MapReduce, Hive, Pig, etc.
Big Data Hadoop Developer Training Course

Become an expert in Hadoop by getting hands-on knowledge on MapReduce, Hadoop Ar..

  • 35000 30000
What you will learn
  • Fundamentals of Hadoop and YARN and write applications using them
  • Setting up pseudo-node and multi-node clusters on Amazon EC2
  • Spark, Spark SQL, Streaming, Data Frame, RDD, GraphX and MLlib writing Spark applications
  • Hadoop administration activities like cluster managing, monitoring, administration and troubleshooting
  • Set up different configurations of Hadoop cluster
  • Maintain and monitor Hadoop cluster by considering the optimal hardware and networking settings
  • Leverage Pig, Hive, Hbase, ZooKeeper, Sqoop, Flume, and other projects from the Apache Hadoop ecosystem
  • Hadoop testing applications using MRUnit and other automation tools
  • Practicing real-life projects using Hadoop and Apache Spark
Big Data Hadoop Analyst Training Course

Big Data Hadoop Analyst online training course helps you master Big Data Analysi..

  • 45000 35000
What you will learn
  • How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
  • Using Apache Hive and Apache Impala to provide SQL access to data
  • Hive and Impala syntax and data formats, including functions and subqueries
  • Create, modify, and delete tables, views, and databases; load data; and store results of queries
  • Create and use partitions and different file formats. Combining two or more datasets using JOIN or UNION, as appropriate
  • What analytic and windowing functions are, and how to use them. Store and query complex or nested data structures
  • Process and analyze semi-structured and unstructured data. Techniques for optimizing Hive and Impala queries
  • Extending the capabilities of Hive and Impala using parameters, custom file formats and SerDes, and external scripts
  • How to determine whether Hive, Impala, an RDBMS, or a mix of these is best for a given task
Hadoop Data Analytics Course

Hadoop Data Analytics online training course explains how to apply data analytic..

  • 45000 40000
What you will learn
  • Explain the fundamentals of Apache Hadoop, Data ETL (extract, transform, load), data processing using Hadoop tools
  • Performing data analysis and processing complex data using Pig
  • Perform data management and text processing using Hive
  • Extending, troubleshooting, and optimizing Pig and Hive performance Analyze data with Impala
  • Comparative study of MapReduce, Pig, Hive, Impala, and Relational Databases
Big Data Hadoop Architect Master Program

BIT's extensive Big Data Hadoop Architect online training is curated by Hadoop e..

  • 150000 135000
What you will learn
  • Introduction to Hadoop ecosystem
  • Working with HDFS and MapReduce
  • Real-time analytics with Apache Spark
  • ETL in Business Intelligence domain
  • Working on large amounts of data with NoSQL databases
  • Real-time message brokering system. Hadoop analysis and testing

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What you will learn
  • Cloudera Manager features that make managing your clusters easier, such as aggregated logging, configuration & resource...
  • Configuring & deploying production-scale clusters that provide key Hadoop-related services, include YARN, HDFS, Impala,...
  • Determining the correct hardware and infrastructure for your cluster
  • Proper cluster configuration and deployment to integrate with the data center
  • Ingesting, storing, and accessing data in HDFS, Kudu, and cloud object stores such as Amazon S3
  • How to load file-based and streaming data into the cluster using Kafka and Flume
  • Configuring automatic resource management to ensure service-level agreements are met for multiple users of a cluster
What you will learn
  • Describe the fundamentals and components of Hadoop
  • Elucidate the features, architecture, security considerations of Hadoop Distributed File System (HDFS)
  • Provide an overview of Hadoop Ecosystem covering different tools for integration, analysis, data storage and retrieval
  • Understand the features, concepts, architecture of MapReduce
  • Plan, install, and configure Hadoop. Practice Hadoop security system and configure Kerberos Security
  • Manage and schedule jobs to be executed in Hadoop system.
  • Install and manage other Hadoop clusters including Pig, Hive, HBase, Sqoop, HDFS
  • Utilize best practices for deploying, managing, and monitoring Hadoop clusters
What you will learn
  • Role of Relational Database Management System (RDBMS) and Grid computing
  • Concepts of MapReduce and HDFS
  • Using Hadoop I/O to write MapReduce programs
  • Develop MapReduce applications to solve the problems
  • Set up Hadoop cluster and administer
  • Hive, a data warehouse software, for querying and managing large datasets residing in distributed storage
  • Use of Sqoop in controlling the import and consistency
  • Spark, Spark SQL, Streaming, Data Frame, RDD, GraphX and MLlib writing Spark applications
  • Hadoop testing applications using MRUnit and other automation tools
  • Configuring ETL tools like Pentaho/Talend to work with MapReduce, Hive, Pig, etc.
What you will learn
  • Fundamentals of Hadoop and YARN and write applications using them
  • Setting up pseudo-node and multi-node clusters on Amazon EC2
  • Spark, Spark SQL, Streaming, Data Frame, RDD, GraphX and MLlib writing Spark applications
  • Hadoop administration activities like cluster managing, monitoring, administration and troubleshooting
  • Set up different configurations of Hadoop cluster
  • Maintain and monitor Hadoop cluster by considering the optimal hardware and networking settings
  • Leverage Pig, Hive, Hbase, ZooKeeper, Sqoop, Flume, and other projects from the Apache Hadoop ecosystem
  • Hadoop testing applications using MRUnit and other automation tools
  • Practicing real-life projects using Hadoop and Apache Spark
What you will learn
  • How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
  • Using Apache Hive and Apache Impala to provide SQL access to data
  • Hive and Impala syntax and data formats, including functions and subqueries
  • Create, modify, and delete tables, views, and databases; load data; and store results of queries
  • Create and use partitions and different file formats. Combining two or more datasets using JOIN or UNION, as appropriate
  • What analytic and windowing functions are, and how to use them. Store and query complex or nested data structures
  • Process and analyze semi-structured and unstructured data. Techniques for optimizing Hive and Impala queries
  • Extending the capabilities of Hive and Impala using parameters, custom file formats and SerDes, and external scripts
  • How to determine whether Hive, Impala, an RDBMS, or a mix of these is best for a given task
What you will learn
  • Explain the fundamentals of Apache Hadoop, Data ETL (extract, transform, load), data processing using Hadoop tools
  • Performing data analysis and processing complex data using Pig
  • Perform data management and text processing using Hive
  • Extending, troubleshooting, and optimizing Pig and Hive performance Analyze data with Impala
  • Comparative study of MapReduce, Pig, Hive, Impala, and Relational Databases
What you will learn
  • Introduction to Hadoop ecosystem
  • Working with HDFS and MapReduce
  • Real-time analytics with Apache Spark
  • ETL in Business Intelligence domain
  • Working on large amounts of data with NoSQL databases
  • Real-time message brokering system. Hadoop analysis and testing
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