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Module 02 of 03

Big Data & Pipeline Engineering

Spark, Databricks, Airflow, Kafka & Streaming

Learn distributed data processing with Spark, work with Databricks, orchestrate pipelines using Airflow, and understand streaming and event-driven data systems with Kafka.

FormatRecorded Program
Duration~40 Hours
AccessLifetime Access
PrerequisitesRecommended
Status: Currently not live
Overview

Move From Data Foundations to Data at Scale

Once you understand SQL, Python, ETL and data warehousing, the next challenge is learning how to process larger volumes of data, build reliable pipelines and handle data as it moves continuously through systems. This module takes you into distributed processing, pipeline orchestration and streaming using Apache Spark, Databricks, Airflow and Kafka.

Core Technology Scope
Distributed Systems & Big Data FundamentalsApache Spark Architecture & RDD/DataFramesSpark SQL, Transformations, Caching & OptimizationDatabricks Lakehouse & Delta Lake BasicsApache Airflow Workflow Orchestration & SensorsApache Kafka Streaming & Event-Driven Architecture
Key Advantages

Move From Building Pipelines to Engineering Them at Scale

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Distributed data processing

Understand why traditional single-machine processing fails on massive volumes and how distributed compute clusters operate.

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Apache Spark in depth

Master Spark architecture (driver, executor, memory management) and process large datasets using Spark SQL and DataFrames.

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Databricks modern platform

Work with notebooks, compute clusters, Delta Lake concepts, and automated ETL jobs on Databricks.

✓

Pipeline orchestration with Airflow

Learn how Apache Airflow schedules, coordinates, monitors, and recovers complex multi-stage DAG workflows.

✓

Streaming and event-driven systems

Understand Apache Kafka architecture, producers, consumers, topics, partitions, and real-time streaming mechanics.

Target Audience

Who Is This Program For?

  • Aspiring Data Engineers who have built foundational SQL and Python skills and want to move to large-scale data systems
  • Data Engineers looking to strengthen their technical depth in Spark tuning, Airflow DAGs, and Kafka
  • Software Developers moving toward data engineering who need to understand distributed systems
  • Learners with equivalent foundational knowledge ready for intermediate Big Data engineering
Prerequisites

Starting Requirements

A basic understanding of SQL, Python, ETL/data pipelines, and data warehousing concepts is recommended. You do not need to complete Module 1 if you have equivalent experience.

Level: Intermediate
Curriculum Breakdown

What You'll Learn

Structured modules designed to build competencies systematically.

•Big Data fundamentals: Volume, Velocity, Variety, and distributed computing challenges
•Distributed systems architecture vs monolithic single-node processing
•Batch vs streaming processing paradigms and Data Lakes overview
•Apache Spark architecture: Driver, Executors, Cluster Manager, and Worker nodes
•Resilient Distributed Datasets (RDDs), DataFrames, and Datasets
•Spark execution model: DAGs, Stages, Tasks, Transformations (lazy evaluation), and Actions
Outcome: Understand how distributed processing works and how Spark distributes computation across clusters.
Real-World Applications

Practical Learning Across Scale Tools

Build and work with practical applications directly derived from our curricula.

Apache Spark · Parquet · PySpark

Spark Large-Scale Data Transformation

Hands-on processing of high-volume datasets using PySpark/Spark SQL, partition tuning, and broadcast join optimization.

Focus: Wide vs narrow transformations, shuffle reduction, and caching.
Databricks · Delta Lake · PySpark

Databricks Lakehouse Workflow

Configure Databricks notebooks and clusters to build an automated ETL job ingesting raw data into Delta tables.

Focus: Cluster configuration, Delta ACID operations, and scheduled notebook jobs.
Apache Airflow · Python · Docker

Airflow Multi-Stage Orchestrated DAG

Author and test a complete multi-dependency Airflow pipeline with custom operators, failure retries, email alerts, and sensors.

Focus: DAG scheduling, task dependencies, sensor checks, and retry policies.
Apache Kafka · Event Streaming

Kafka Producer-Consumer Stream Simulator

Build an event streaming prototype sending real-time transactional events to Kafka topics and consuming them with consumer groups.

Focus: Partitioning, consumer offset commits, and event-driven data flow.
Product Architecture

The Scale Stage of the Data Engineering Journey

This module is Module 02 of the 3-module Data Engineering Master Program.

MODULE
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Module 1: Data Engineering Foundations

SQL · Python · ETL · Linux · Warehousing

CURRENT MODULEACTIVE

Module 2: Big Data & Pipeline Engineering

Spark · Databricks · Airflow · Kafka · Streaming

MODULE
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Module 3: Modern Data Engineering with dbt & Architecture

dbt · AWS · ELT · Data Quality · Observability · Architecture

Want all three modules in one unified 12-week progression with Capstone?Explore Master Program
Package Breakdown

Everything You Need for the Journey

~40 Hours of in-depth recorded curriculum
Lifetime access to all lectures and resources
Comprehensive slides, architectural diagrams, and cheat sheets
Hands-on exercises covering Spark, Databricks, Airflow, and Kafka
Practice notebooks and sample DAG templates
TailorTech WhatsApp Community support
Instructor Experience

Learn From Industry Experience

Delivered by an industry professional with 12+ years of experience in large-scale distributed systems, Spark tuning, and enterprise pipeline orchestration.

Program Status

Inquire About Big Data & Pipeline Engineering

Register your interest or speak with our advisors to learn about upcoming cohorts and updates.

Status
Currently not live
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Frequently Asked Questions

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