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Live Advanced Data Engineering Program by TechieOps

Become Job-Ready in Data Engineering

Build production-grade batch and streaming data pipelines using PySpark, Apache Airflow, AWS S3, EMR, Glue, Apache Iceberg, and Snowflake through hands-on live training.

✓ Build real data pipelines
✓ Learn PySpark and Airflow
✓ Work with AWS and Snowflake
✓ Capstone project and interview guidance
Take Admission Now View Full Syllabus

Next Live Batch Admission

Speak with TechieOps and confirm your seat for the upcoming Advanced Data Engineering batch.

  • ✓ 40 hours live online training
  • ✓ 7 structured modules
  • ✓ Batch and streaming pipeline projects
  • ✓ AWS, PySpark, Airflow, Iceberg, Snowflake
  • ✓ Interview preparation guidance
WhatsApp for Fees & Admission Email Course Advisor
Fast response for batch timing, fees, and enrollment process.
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40 Hours Live intensive training
7 Modules Complete DE roadmap
Hands-on Batch and streaming projects
Capstone End-to-end pipeline
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Why Candidates Should Join This Program

This program is designed for learners who want practical, job-ready Data Engineering skills. Candidates learn how real data platforms are planned, built, orchestrated, optimized, and delivered using modern tools like PySpark, Airflow, AWS, Iceberg, and Snowflake.

01

Build Real Data Pipelines

Create end-to-end batch and streaming pipelines using PySpark, AWS services, Airflow orchestration, and Snowflake loading.

02

Learn Modern Data Stack

Work with Spark, S3, EMR, Glue, Apache Iceberg, Airflow, and Snowflake in a structured practical learning path.

03

Career-Focused Training

Prepare for Data Engineer roles with hands-on assignments, capstone project, real-world use cases, and interview guidance.

By the end of the program, candidates will be able to present a portfolio-ready Data Engineering capstone: an end-to-end data pipeline that ingests data from S3, processes it using PySpark, manages data using Iceberg, orchestrates workflows with Airflow, and loads data into Snowflake.

Who Is This Best For?

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Software Engineers

Developers who want to transition into Data Engineering and big data pipeline development.

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Data Analysts

Analysts who want to move beyond SQL reporting and enter Big Data, ETL, and cloud data engineering roles.

✓

Freshers

Learners with Python and SQL basics who want practical Data Engineering project experience.

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Working Professionals

Professionals preparing for Data Engineer interviews, cloud data roles, or production pipeline responsibilities.

What You Will Learn

PySpark Spark Architecture DataFrames Spark Streaming Apache Airflow AWS S3 AWS EMR AWS Glue Glue Data Catalog Apache Iceberg Snowflake Batch Pipelines Streaming Pipelines Pipeline Optimization

Complete Advanced Data Engineering Syllabus

The syllabus is structured into 7 practical modules covering fundamentals, distributed processing, AWS data engineering, orchestration, lakehouse architecture, Snowflake, and a final capstone.

Module 1 Introduction to Data Engineering Data engineering landscape, pipeline architecture, batch vs streaming 2 hrs +
  • Data Engineering landscape and role of a Data Engineer
  • Batch vs streaming systems
  • Data pipeline architecture
  • Overview of Spark, Airflow, AWS, and Snowflake
Module 2 PySpark Fundamentals to Advanced Spark architecture, transformations, optimization, batch and streaming 12 hrs +
  • Spark architecture: Driver, Executors, SparkSession
  • RDD vs DataFrame vs Dataset concepts
  • DataFrame operations, filtering, joins, aggregations, and window functions
  • Handling nested data such as JSON
  • Performance tuning using partitions, caching, and optimized transformations
  • Structured Streaming architecture and streaming sources such as Kafka and files
  • Windowed aggregations, watermarking, late data handling, and output modes
  • Writing streaming data to sinks such as S3 and console
Hands-on: Build a batch ETL pipeline using PySpark and a real-time streaming pipeline using Spark Structured Streaming.
Module 3 AWS for Data Engineering S3, EMR, Glue, Data Catalog, data lake patterns 8 hrs +
  • Amazon S3 data lake concepts
  • File formats: CSV, Parquet, and ORC
  • Partitioning strategies for scalable data storage
  • Running Spark jobs on AWS EMR
  • EMR cluster setup and configuration
  • AWS Glue Crawlers and Data Catalog
  • Glue ETL jobs and schema inference
Hands-on: Build an end-to-end pipeline using S3, EMR, and Glue.
Module 4 Apache Airflow DAGs, operators, sensors, scheduling, retries, orchestration 6 hrs +
  • Airflow architecture
  • DAGs, operators, and sensors
  • Scheduling and dependencies
  • Error handling and retries
  • Best practices for production pipelines
Hands-on: Build Airflow DAGs for ETL workflows and orchestrate Spark jobs.
Module 5 Apache Iceberg Modern lakehouse tables, schema evolution, partitioning, time travel 4 hrs +
  • Modern data lake challenges
  • Introduction to Apache Iceberg
  • Schema evolution
  • Partitioning and time travel
  • ACID transactions in data lakes
Hands-on: Manage datasets using Apache Iceberg.
Module 6 Snowflake Cloud data warehousing, loading, query optimization, S3 integration 4 hrs +
  • Snowflake architecture
  • Warehouses, databases, and schemas
  • Data loading using COPY INTO
  • Query optimization
  • Integration with AWS S3
Hands-on: Load and query data in Snowflake.
Module 7 Capstone Project End-to-end pipeline with S3, PySpark, Iceberg, Airflow, Snowflake 4 hrs +
  • End-to-end pipeline with data ingestion from S3
  • Processing using PySpark for batch and streaming workloads
  • Table format management using Apache Iceberg
  • Workflow orchestration using Airflow
  • Data loading into Snowflake
Capstone: Build and explain a complete production-style Data Engineering pipeline.

Teaching Methodology and Deliverables

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Teaching Methodology

Live coding sessions, real-world use cases, hands-on assignments, industry best practices, and capstone-based learning.

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Course Deliverables

Course slides, sample datasets, hands-on exercises, end-to-end project code, and interview preparation guidance.

Learning Outcomes

  • Design and build production-grade data pipelines
  • Handle both batch and real-time data processing
  • Work with modern cloud data engineering tools
  • Use PySpark for distributed data processing
  • Orchestrate workflows using Apache Airflow
  • Build data lake and lakehouse patterns using AWS and Apache Iceberg
  • Load, query, and optimize data in Snowflake
  • Prepare for Data Engineer roles in top companies

Frequently Asked Questions

FAQ Who is this course for? Best-fit audience Info +
This course is suitable for software engineers, data analysts, freshers with Python and SQL basics, and professionals preparing for Data Engineering roles.
FAQ Do I need prior cloud experience? Prerequisite clarity Info +
Prior cloud experience is helpful but not mandatory. The course explains AWS services like S3, EMR, and Glue from a practical Data Engineering perspective.
FAQ Will I build real projects? Hands-on learning Info +
Yes. The course includes hands-on pipelines using PySpark, AWS, Airflow, Iceberg, and Snowflake, along with a final capstone project.
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Ready to start Advanced Data Engineering? Talk to TechieOps and complete your admission process.
Enroll Now View Syllabus