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Big Data Course in Ludhiana | TechCADD Computer Education

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Professional Big Data & Data Engineering – Hadoop, Spark, Hive and Pipelines

Big Data Course in Ludhiana - TechCADD

  • Training Overview

  • Why TechCADD?

  • Student Reviews

Big Data engineering enables scalable processing, storage, and analysis across distributed systems; this course focuses on Hadoop, Hive, Spark, and PySpark with ingestion, orchestration, testing, and cloud-ready deployments to build production-style pipelines.


Why Join the Big Data Course?

  • Industry-Relevant Stack: Master Hadoop, Spark, and SQL-on-Hadoop used across data platforms.
  • Pipeline-Ready: Build ingestion-to-consumption flows with orchestration and validation.
  • Team Practices: Apply Git, testing, documentation, and deployment checklists.

What Will Be Learned

  • Big Data Foundations: Distributed concepts, Linux basics, file formats (CSV, JSON, Parquet), and Git.
  • Hadoop Core: HDFS, YARN, MapReduce patterns, and cluster basics.
  • SQL on Hadoop: HiveQL, partitions/bucketing, ORC/Parquet, and performance practices.
  • Spark Core: RDDs, transformations/actions, and jobs with Spark shell/basics.
  • Spark SQL & DataFrames: DataFrame API, Spark SQL, joins, aggregations, and UDFs.
  • PySpark Development: Project structure, I/O, configs, and reusable job patterns.
  • Streaming & Kafka Basics: Kafka fundamentals and Spark Structured Streaming overview.
  • Ingestion & Orchestration: Sqoop/Flume basics, Airflow DAGs, schedulers, and monitoring.
  • Data Quality & Testing: Unit tests for PySpark, data validation with frameworks, and CI runs.
  • Cloud & Deployments: EMR/Dataproc overview, S3/GCS storage, Docker basics for jobs.

Why Choose This Training?

  • Beginner-Friendly: Clear path from fundamentals to orchestrated, tested pipelines.
  • Hands-On First: Labs, notebooks, orchestration DAGs, and code reviews.
  • Expert Guidance: Emphasis on performance, reliability, and readability.
  • Career Support: Portfolio curation, resume tips, and interview preparation.

Career Paths After Training

  • Big Data Engineer (Trainee/Junior)
  • Data Engineer
  • Spark/PySpark Developer
  • ETL Developer
  • Hadoop Administrator (Junior)

Who Can Join?

  • Students and beginners targeting data engineering roles.
  • Software engineers transitioning to distributed data systems.
  • Analysts automating data pipelines and large-scale processing.
  • Python developers expanding into Spark and orchestration.

Training Mode & Format

  • Mode: Online / Offline (based on availability)
  • Format: Instructor-led sessions, labs, DAGs, testing, and deployment exercises

What This Big Data Course Covers

Emphasis on schema design, partitioning, storage formats, and Spark job patterns to build scalable, cost-aware pipelines for batch and near-real-time use cases.

Hands-on modules culminate in capstones such as an ingestion-to-lake pipeline with Hive SQL marts, a Spark SQL analytics job suite, or a PySpark streaming demo with basic Kafka.

Mentors provide feedback on data modeling, job performance, DAG reliability, test coverage, and deployment hygiene aligned with hiring standards.

Course Modules

  • Module 1: Big Data Foundations

    Distributed concepts, Linux basics, CSV/JSON/Parquet, Git workflows, and environment setup.

  • Module 2: Hadoop Core – HDFS & YARN

    HDFS architecture, replication/blocks, YARN resource management, and MapReduce basics.

  • Module 3: Hive & SQL on Hadoop

    HiveQL, external/managed tables, partitions, bucketing, ORC/Parquet, and tuning basics.

  • Module 4: Spark Core

    RDDs, transformations/actions, jobs, and Spark application basics for batch workloads.

  • Module 5: Spark SQL & DataFrames

    DataFrames, Spark SQL, joins, aggregations, UDFs/UDAFs, and performance considerations.

  • Module 6: PySpark Development

    Project structure, configs, file I/O, reusable job patterns, and parameterized pipelines.

  • Module 7: Kafka & Streaming Fundamentals

    Kafka basics, topics/partitions, producers/consumers, and Spark Structured Streaming overview.

  • Module 8: Ingestion & Orchestration

    Sqoop/Flume basics, Airflow DAGs, scheduling, retries/alerts, and pipeline monitoring.

  • Final Module: Cloud, Testing & Portfolio

    EMR/Dataproc overview, S3/GCS storage, unit tests/data validation, Docker basics, and capstone deployments.

Who Should Join This Training?

This training is ideal for:

  • Students & Fresh Graduates: Build employable Big Data skills and portfolio pipelines.
  • Job Seekers & Data Aspirants: Practice ingestion, processing, Hive marts, and deployments.
  • Working Professionals: Upgrade to Spark/PySpark and orchestrated, tested pipelines.
  • Python Developers: Move from scripts to scalable data engineering workflows.

  • Pipeline-First Learning

    End-to-end labs from raw ingestion to Hive marts with Spark processing.

  • Practical Requirements

    PC/laptop, terminal basics, and Python; setup assistance and sample datasets included.

  • Interactive Sessions

    Small batches, code reviews, and mentor-led tuning and validation walkthroughs.

  • Placement Support

    Resume help, interview prep, and portfolio reviews for data engineering roles.

Why Choose TechCADD for Big Data Course in Ludhiana? - Top-Rated Institute

TechCADD Computer Education provides practical, pipeline-focused training with testing, documentation, and deployable artifacts to meet hiring expectations.

🔥 Training Highlights!

✅ Hadoop, Hive & Spark

✅ PySpark Jobs & Tuning

✅ Airflow DAGs & Monitoring

✅ Capstones, Tests & Deployments

📊 Why Students Choose Us?

🎯 Practical, Job-Oriented Focus

💻 Structured Labs & Reviews

⭐ Strong Feedback Culture

🏢 Local Career Support

🎯 Quick Facts About This Training:
  • ⏰ 100% Practical Sessions
  • 👥 Small Batch Size
  • 📍 Convenient Location
  • 🧭 Capstone Portfolio
  • 💻 Hadoop, Hive & Spark
  • 📜 Training Certificate
  • 🎯 Placement Guidance
  • 💰 Affordable Fees
  • ⭐ Excellent Reviews
  • 👨‍🎓 5000+ Alumni
  • 🏢 Local Partners
  • 👤 One-to-One Batches

Ready to engineer scalable data pipelines? Join TechCADD’s Big Data Course to master Hadoop, Hive, Spark, and PySpark with orchestration, testing, and cloud-ready deployments, then publish portfolio projects.

📞 Enroll Today! Contact now for batch timings, fee details, and to book a free demo session.

Student Testimonials & Reviews – Big Data Course in Ludhiana

  • S

    Sanjay Patel

    From Hive partitions to PySpark jobs and Airflow DAGs, the course connected concepts into deployable pipelines with reviews and validation checklists.

  • D

    Deepika Sharma Data Engineering Intern

    Hands-on labs with Spark SQL and PySpark plus Airflow orchestration helped secure an internship and ship a small analytics pipeline.

  • T

    Tanvi Agarwal Junior Big Data Developer

    Mentor feedback on partitions, DataFrame joins, and DAG reliability made deployments smoother and performance better in capstones.

  • H

    Harpreet Singh College Student

    Building a Spark SQL analytics job and a simple streaming demo with Kafka was a highlight; learned to test and monitor pipelines properly.

  • P

    Pooja Verma Freelance Data Engineer

    Data validation, logging, and documentation improved client delivery; deployments and DAGs made handover easy.

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What will be covered in the Big Data Course?

Hadoop (HDFS, YARN, MapReduce), Hive/SQL on Hadoop, Spark Core, Spark SQL, PySpark, Kafka basics, ingestion with Sqoop/Flume, Airflow orchestration, data testing/validation, and cloud-ready deployments.

Is prior experience required?

No prior professional experience is required; the course starts from fundamentals and progresses to orchestrated, tested, and deployable pipelines with guided labs.

Are online classes available?

Yes — classroom and live online sessions are available; online includes real-time coding, screen sharing, and mentor feedback.

Do you provide certification and placement help?

Yes. On completion a TechCADD certificate is provided along with resume support, interview prep, and portfolio reviews.

What are the batch options and fees?

Weekday, weekend, and fast-track options are available; contact the counseling team for current schedules and fee details with EMI options.

Will there be capstone pipelines, notebooks, and docs?

Yes, capstones are central to the training with notebooks, validation, documentation, and deployment guidance to present to employers.