Hadoop & Big Data Course in Hyderabad
Understand big data foundations with Hadoop, Hive, HBase and Spark
The Hadoop and Big Data course gives you a structured foundation in distributed data storage and processing. You start with big data concepts, Linux and SQL, then study the Hadoop architecture: HDFS for storage, YARN for resource management and MapReduce for batch processing. You work with Apache Hive for SQL on large data, including partitioning, bucketing, file formats such as Parquet and ORC and performance tuning, Apache HBase for wide-column storage, Apache Spark for faster processing, and data ingestion with tools such as Apache NiFi and Kafka. The course explains that Apache Sqoop was retired by the Apache project in 2021 and shows modern alternatives for relational transfers, such as Spark JDBC and Kafka Connect. You also learn the Hadoop ecosystem's role next to cloud data lakes and lakehouses, so you understand legacy clusters you may meet at work and how they migrate to Amazon EMR, Azure and Google Cloud services. The course suits graduates and professionals who want a big data foundation; basic Linux and SQL help. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Big Data Concepts & Ecosystem
- •Big data characteristics and use cases
- •Distributed storage and processing; CAP theorem overview
- •Ecosystem map: Hadoop, Spark, Hive, HBase, Kafka and cloud equivalents
- •Lab: Classify sample use cases by batch or streaming needs
- •Outcome: Explain why big data tools exist
Linux, Java & SQL Foundations
- •Linux commands, permissions and shell scripting for cluster work
- •SQL queries and joins
- •Java and Python basics for MapReduce and Spark
- •Lab: Prepare a Linux VM and practise commands
- •Outcome: Work comfortably on a cluster node
Hadoop Architecture & HDFS
- •Hadoop components and cluster roles
- •HDFS architecture, block size, replication and rack awareness
- •HDFS commands, permissions, snapshots and high availability
- •Lab: Set up a single-node or pseudo-distributed cluster and load files
- •Outcome: Store data in HDFS and explain how it stays safe
YARN & MapReduce
- •YARN architecture and scheduling
- •MapReduce programming model: map, shuffle and reduce
- •Combiners, partitioners and counters
- •Lab: Run word-count and a log-analysis MapReduce job
- •Outcome: Understand the model underlying distributed batch processing
Apache Hive
- •Hive architecture and the metastore
- •Managed versus external tables; partitioning and bucketing
- •File formats: Text, ORC and Parquet; joins and window functions
- •Lab: Create partitioned tables and query a large dataset
- •Outcome: Analyse large datasets with SQL
Hive Optimisation & Advanced Features
- •Execution engines: Tez and Spark; vectorisation and statistics
- •Map joins, skew handling and cost-based optimisation
- •User-defined functions and ACID tables
- •Lab: Tune a slow Hive query and compare timings
- •Outcome: Make Hive queries faster
Apache HBase & NoSQL on Hadoop
- •HBase data model and architecture
- •Row key design and region splits
- •HBase shell, bulk loading and Hive integration
- •Lab: Design a table for time-series data
- •Outcome: Choose and model low-latency wide-column storage
Apache Spark on Hadoop
- •Spark versus MapReduce
- •DataFrames and Spark SQL with Hive tables
- •Running Spark on YARN; basic tuning
- •Lab: Rewrite a MapReduce job in PySpark
- •Outcome: Process data faster with Spark
Data Ingestion: NiFi, Kafka & Alternatives to Sqoop
- •Apache NiFi flows, processors and provenance
- •Kafka as an ingestion buffer; Kafka Connect and Spark JDBC for relational transfers
- •Why Apache Sqoop was retired and how legacy jobs are replaced
- •Lab: Ingest files and database rows into HDFS
- •Outcome: Bring data into a cluster with supported tools
Workflow Scheduling & Data Quality
- •Scheduling concepts; Oozie in legacy clusters and Apache Airflow as a modern option
- •Data validation, schema evolution and file format choices
- •Small-file problems and compaction
- •Lab: Schedule a daily ingest and Hive load
- •Outcome: Run recurring pipelines reliably
Security, Administration & Monitoring
- •Cluster security concepts: Kerberos, Apache Ranger and encryption at rest
- •Capacity planning, queues and monitoring
- •Distributions and operations: Cloudera, EMR and Dataproc overview
- •Lab: Review a cluster dashboard and diagnose a failed job
- •Outcome: Understand how clusters are secured and operated
From Hadoop to Cloud Lakehouses & Capstone
- •Why many organisations move from on-premises Hadoop to cloud data lakes
- •Mapping HDFS, Hive and YARN to S3, Glue, EMR, Azure and Google Cloud equivalents
- •Capstone: ingest, store, query and analyse a large dataset with Hive and Spark
- •Interview practice for big data roles
- •Outcome: A portfolio-ready big data project and a cloud migration view
Tools Covered
Projects Included
- HDFS Data Lake Setup: Load and organise log data in HDFS with a documented directory layout; deliverable is a lake structure and loading scripts.
- Log Analytics with MapReduce and Hive: Analyse web logs using MapReduce, then reproduce results in Hive; deliverable is a comparison of approaches.
- Partitioned Hive Warehouse: Build partitioned Parquet tables for sales data and tune queries; deliverable is a warehouse and timing report.
- Time-Series on HBase: Design an HBase table for sensor readings and query by time range; deliverable is a schema and query examples.
- Capstone Big Data Pipeline: Ingest with NiFi, store in HDFS, transform with Spark and query with Hive; deliverable is a GitHub repository and architecture diagram.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Hadoop & Big Data course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Hadoop & Big Data.
Certificate of Completion
Hadoop & Big Data
This certifies that
[Your Name]
has successfully completed the
Hadoop & Big Data
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Hadoop & Big Data course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best Hadoop & Big Data Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Big Data Developer, Hadoop Developer, Data Engineer (entry level), Hive Developer; support is guidance with the job search, not a guarantee of a job.