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Big Data Hadoop Certification Training Course

Big Data Hadoop Certification Training Course Course

Edureka’s Big Data Hadoop Certification combines deep dives into HDFS, MapReduce, Hive, and Spark with practical cluster administration, security, and real-world pipeline development.

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Big Data Hadoop Certification Training Course on Edureka — Edureka’s Big Data Hadoop Certification combines deep dives into HDFS, MapReduce, Hive, and Spark with practical cluster administration, security, and real-world pipeline development.

Pros

  • Comprehensive coverage of both batch (MapReduce/Hive) and real-time (Spark) processing engines
  • Strong emphasis on cluster setup, security (Kerberos), and high availability configurations
  • Capstone project integrates all components into a deployable end-to-end pipeline

Cons

  • Requires access to a multi-node Hadoop environment for full hands-on experience
  • Advanced Spark tuning and streaming integrations (Kafka) are touched on but not deeply explored

Big Data Hadoop Certification Training Course Course

Platform: Edureka

Instructor: Unknown

What will you learn in Big Data Hadoop Certification Training Course

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  • Understand Big Data ecosystems and Hadoop core components: HDFS, YARN, MapReduce, and Hadoop 3.x enhancements

  • Ingest and process large datasets using MapReduce programming and high-level abstractions like Hive and Pig

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  • Implement real-time data processing with Apache Spark on YARN, leveraging RDDs, DataFrames, and Spark SQL

  • Manage data workflows and orchestration using Apache Oozie and Apache Sqoop for database imports/exports

Program Overview

Module 1: Introduction to Big Data & Hadoop Ecosystem

⏳ 1 hour

  • Topics: Big Data characteristics (5 V’s), Hadoop history, ecosystem overview (Sqoop, Flume, Oozie)

  • Hands-on: Navigate a pre-configured Hadoop cluster, explore HDFS with basic shell commands

Module 2: HDFS & YARN Fundamentals

⏳ 1.5 hours

  • Topics: HDFS architecture (NameNode/DataNode), replication, block size; YARN ResourceManager and NodeManager

  • Hands-on: Upload/download files, simulate node failure, and write YARN application skeletons

Module 3: MapReduce Programming

⏳ 2 hours

  • Topics: MapReduce job flow, Mapper/Reducer interfaces, Writable types, job configuration and counters

  • Hands-on: Develop and run a WordCount and Inverted Index MapReduce job end-to-end

Module 4: Hive & Pig for Data Warehousing

⏳ 1.5 hours

  • Topics: Hive metastore, SQL-like queries, partitioning, indexing; Pig Latin scripts and UDFs

  • Hands-on: Create Hive tables over HDFS data and execute analytical queries; write Pig scripts for ETL tasks

Module 5: Real-Time Processing with Spark on YARN

⏳ 2 hours

  • Topics: Spark architecture, RDD vs. DataFrame vs. Dataset APIs; Spark SQL and streaming basics

  • Hands-on: Build and run a Spark application for batch analytics and a simple structured streaming job

Module 6: Data Ingestion & Orchestration

⏳ 1 hour

  • Topics: Sqoop imports/exports between RDBMS and HDFS; Flume sources/sinks; Oozie workflow definitions

  • Hands-on: Automate daily data ingestion from MySQL into HDFS and schedule a multi-step Oozie workflow

Module 7: Cluster Administration & Security

⏳ 1.5 hours

  • Topics: Hadoop configuration files, high availability NameNode, Kerberos authentication, Ranger/Knox basics

  • Hands-on: Configure HA NameNode setup and secure HDFS using Kerberos principals

Module 8: Performance Tuning & Monitoring

⏳ 1 hour

  • Topics: Resource tuning (memory, parallelism), job profiling with YARN UI, cluster monitoring with Ambari

  • Hands-on: Tune Spark executor settings and analyze MapReduce job performance metrics

Module 9: Capstone Project – End-to-End Big Data Pipeline

⏳ 2 hours

  • Topics: Integrate ingestion, storage, processing, and analytics into a cohesive workflow

  • Hands-on: Build a complete pipeline: ingest clickstream data via Sqoop/Flume, process with Spark/Hive, and visualize results

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Job Outlook

  • Big Data Engineer: $110,000–$160,000/year — design and maintain large-scale data platforms with Hadoop and Spark

  • Data Architect: $120,000–$170,000/year — architect end-to-end data solutions spanning batch and streaming workloads

  • Hadoop Administrator: $100,000–$140,000/year — deploy, secure, and optimize production Hadoop clusters for enterprise use

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FAQs

Do I need prior IoT or cloud experience to take this course?
No prior IoT or cloud experience required; basic Python and Linux helpful. Introduces IoT fundamentals, ecosystem, and solution architectures. Hands-on exercises with Raspberry Pi, Sense HAT, and Python scripting. Covers Azure IoT Hub device provisioning, telemetry, and routing. Prepares learners for IoT Developer and Edge Solutions Engineer roles.
Will I learn to build end-to-end IoT solutions with Azure?
Connect Raspberry Pi devices and collect sensor data. Stream telemetry to Azure IoT Hub and Azure Storage Explorer. Implement message routing and data visualization dashboards. Apply edge computing with Azure IoT Edge modules. Deploy scalable and secure IoT solutions using cloud services.
Does the course cover edge computing and local analytics?
Deploy containerized IoT Edge modules on Raspberry Pi. Perform local analytics on streaming sensor data. Manage edge workloads with Azure IoT Edge architecture. Combine edge and cloud processing for optimal performance. Integrate with Azure dashboards for monitoring and insights.
Can I integrate voice interfaces like Alexa with IoT devices?
Build and deploy AWS Alexa skills for IoT interaction. Query sensor readings and control actuators via voice commands. Integrate Pi devices with Alexa for smart home or lab projects. Test and debug voice commands in real-time. Combine voice control with cloud and edge computing workflows.
Will I work on a real-world capstone IoT project?
Design end-to-end IoT architecture using Raspberry Pi and Azure. Implement device provisioning, telemetry ingestion, and local analytics. Integrate voice-driven control with Alexa. Apply security and scalability best practices. Present a portfolio-ready, production-like IoT project.

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