Instructor-led Big Data and Hadoop live online classes
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Duration |
Timings |
|---|---|---|
| Feb 23rd | Fri & Sat (5.5 Weeks) Weekend Batch | ⚡Filling Fast Timings – 08:30 PM to 11:30 PM (EST) |
CertAdda’s extensive Big Data Analytics certification is curated by Hadoop experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, and Pig. Throughout this online instructor-led Big Data Hadoop certification training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism, and Finance domains using CertAdda’s Cloud Lab. Enroll now to learn Big Data from instructors with over 10+ years of experience, with hands-on demonstrations.
$424.00 Original price was: $424.00.$399.00Current price is: $399.00.
Date |
Duration |
Timings |
|---|---|---|
| Feb 23rd | Fri & Sat (5.5 Weeks) Weekend Batch | ⚡Filling Fast Timings – 08:30 PM to 11:30 PM (EST) |
Learning Objectives: In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.
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Learning Objectives: In this module, you will learn Hadoop Cluster Architecture, important configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and how to setup Single Node and Multi-Node Hadoop Cluster.
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Learning Objectives: In this module, you will understand Hadoop MapReduce framework comprehensively, the working of MapReduce on data stored in HDFS. You will also learn the advanced MapReduce concepts like Input Splits, Combiner & Partitioner.
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Learning Objectives: In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.
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Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts. You will also be working on healthcare dataset.
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Learning Objectives: This module will help you in understanding Hive concepts, Hive Data types, loading and querying data in Hive, running hive scripts and Hive UDF.
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Learning Objectives: In this module, you will understand advanced Apache Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive. You will also acquire indepth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.
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Learning Objectives: This module will cover advance Apache HBase concepts. We will see demos on HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.
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Learning Objectives: In this module, you will learn what is Apache Spark, SparkContext & Spark Ecosystem. You will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You will be running application on Spark Cluster & comparing the performance of MapReduce and Spark.
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Learning Objectives: In this module, you will understand how multiple Hadoop ecosystem www.edureka.co © 2019 Brain4ce Education Solutions Pvt. Ltd. All rights Reserved. components work together to solve Big Data problems. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.
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For pursuing a career in Data Science, knowledge of Big Data, Apache Hadoop & Hadoop tools are necessary. Hadoop practitioners are among the highest paid IT professionals today with salaries ranging around $97K (source: payscale), and their market demand is growing rapidly.
Organisations are showing interest in Big Data and are adopting Hadoop to store & analyse it. Hence, the demand for jobs in Big Data and Hadoop is also rising rapidly. If you are interested in pursuing a career in this field, now is the right time to get started with online Hadoop Training.
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