What will you learn in Big Data Hadoop Certification Training Course
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
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/Reducerinterfaces, Writable types, job configuration and countersHands-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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Specification: Big Data Hadoop Certification Training Course
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FAQs
- 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.
- 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.
- 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.
- 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.
- 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.
