Big Data and Hadoop Training for Data Processing Workflows

This Big Data and Hadoop Training course examines how large, varied datasets are collected, stored, processed, and prepared for analysis. It introduces Hadoop-based data workflows alongside Talend for data ingestion, helping learners understand the movement of information from source systems into usable data pipelines. The subject matter focuses on core Big Data concepts, distributed processing principles, and practical data integration tasks.
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Curriculum

Big Data, Hadoop Training, ZeblearnIndia, Talend, data ingestion Course Syllabus Structure

The Big Data And Hadoop Training curriculum blends theory with practical labs on HDFS, MapReduce and Spark streaming. Each module includes a step‑by‑step Talend job that loads terabytes of data into Hive tables.

• What is Big Data
• Characteristics of Big Data (5Vs)
• Traditional data vs Big Data
• Big Data use cases
• Big Data ecosystem overview
• Business benefits

• What is Hadoop
• Hadoop architecture
• Hadoop components
• Hadoop distributions
• Hadoop use cases
• Hadoop advantages and limitations

• Hadoop cluster components
• Master and slave nodes
• Hadoop daemons overview
• Single-node vs multi-node cluster
• Cluster setup basics
• High-level data flow

• HDFS architecture
• NameNode and DataNode roles
• Block size and replication
• HDFS read and write process
• HDFS commands
• Fault tolerance in HDFS

• YARN architecture
• ResourceManager and NodeManager
• ApplicationMaster
• Resource allocation
• Scheduling concepts
• YARN benefits

• MapReduce programming model
• Mapper and Reducer concepts
• Data flow in MapReduce
• Writing MapReduce programs
• Job execution lifecycle
• MapReduce limitations

• Hive overview
• Pig overview
• HBase basics
• Sqoop overview
• Flume overview
• Oozie overview

• Hive architecture
• Hive data types
• HiveQL basics
• Managed vs external tables
• Partitioning and bucketing
• Performance optimization

• Pig architecture
• Pig Latin language basics
• Data loading and storing
• Pig operators
• UDF concepts
• Pig performance considerations

• HBase architecture
• HBase data model
• Tables, rows, and column families
• HBase read and write operations
• HBase shell basics
• HBase use cases

• Sqoop for RDBMS integration
• Flume for log data ingestion
• Kafka basics
• Real-time vs batch ingestion
• Data ingestion strategies
• Data pipeline design

• Oozie architecture
• Oozie workflows
• Coordinators and bundles
• Scheduling Hadoop jobs
• Error handling
• Workflow monitoring

• Hadoop security overview
• Kerberos basics
• HDFS permissions
• YARN security
• Data encryption basics
• Security best practices

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Awards & Recognition
Global SAP Training Spotlight Award
India's Leadership Excellence Award 2026
Inspiring Woman Tech Leader of the Year

Global SAP Training Spotlight Award

Brandman India · 2026

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Enroll now to receive exclusive access to a private Slack community and monthly webinars on emerging big data trends. Members also get a complimentary e‑book on optimizing Spark jobs.

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Big Data and Hadoop Training for Data Processing Workflows — Flexible Batches For You

StatusDateTypeScheduleTime
SOLD OUT 30 Aug 2026 Weekend SAT - SUN (08 Weeks) 18:00 - 20:00
FILLING FAST 4 Sep 2026 Weekday MON - FRI (08 Weeks) 08:00 - 10:00
AVAILABLE 9 Sep 2026 Weekend SAT - SUN (08 Weeks) 10:00 - 12:00
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FAQ

Frequently Asked Questions

Q: What is Big Data and why does it matter? A: Big Data refers to datasets whose volume, velocity, or variety exceed traditional processing tools, requiring specialized frameworks for insight extraction. Q: How does Hadoop Training enable handling of Big Data? A: Hadoop Training teaches the distributed storage and parallel processing techniques that let organizations scale analytics across thousands of nodes. Q: What role does Talend play in Big Data ingestion? A: Talend provides visual pipelines that automate data ingestion from heterogeneous sources into Hadoop ecosystems.

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