Live Online Airbyte Course for Data Engineering

Live Course Module: Airbyte Course for Data Engineering

Total Duration: 24 Hours (6 Weeks)

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Week 1: Introduction to Airbyte and Modern ELT (4 Hours)

Goal: Understand ELT architecture, Airbyte fundamentals, and initial setup.

1. Introduction to Modern Data Integration (45 mins)

   • ETL vs ELT in modern data engineering

   • Overview of open-source ELT tools

   • The role of Airbyte in the modern data stack

2. What is Airbyte? (45 mins)

   • Airbyte architecture and core components

   • Features: Connectors, transformations, and scheduling

   • Open-source vs Cloud version overview

3. Installing and Setting Up Airbyte (1.5 hours)

   • Installing Airbyte (Docker-based setup)

   • Navigating the Airbyte UI

   • Key configuration files and settings

4. First Hands-on Pipeline (1 hour)

   • Connecting a source (PostgreSQL / MySQL)

   • Selecting a destination (BigQuery / Snowflake)

   • Running the first sync and validating data

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Week 2: Connectors, Sync Modes & Pipeline Configuration (4 Hours)

Goal: Master connector setup, sync strategies, and data flow understanding.

1. Understanding Airbyte Connectors (1 hour)

   • Source vs Destination connectors

   • Popular built-in connectors overview

   • Connector management and community connectors

2. Sync Modes in Airbyte (1 hour)

   • Full Refresh vs Incremental Sync

   • Append, Deduped History, and CDC modes

   • Choosing the right mode for data efficiency

3. Configuring Pipelines (1 hour)

   • Connection settings: frequency, normalization, and transformations

   • Handling large datasets and incremental loads

   • Scheduling and monitoring syncs

4. Hands-on Exercise (1 hour)

   • Building and scheduling a multi-source pipeline

   • Monitoring logs and understanding sync results

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Week 3: Data Transformations & dbt Integration (4 Hours)

Goal: Learn to implement in-warehouse transformations and dbt integration.

1. Data Normalization in Airbyte (1 hour)

   • JSON normalization explained

   • Managing schema and data types

   • Enabling normalization in Airbyte UI

2. Integrating Airbyte with dbt (1.5 hours)

   • Introduction to dbt basics

   • Configuring dbt transformations within Airbyte

   • Running transformation jobs post-sync

3. Hands-on Lab (1.5 hours)

   • End-to-end ELT workflow: PostgreSQL → BigQuery → dbt

   • Validating transformed data in the warehouse

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Week 4: Custom Connectors, API Usage & Advanced Features (4 Hours)

Goal: Learn to build, customize, and extend Airbyte for advanced use cases.

1. Custom Connector Development (1.5 hours)

   • Introduction to the Airbyte Connector Development Kit (CDK)

   • Building a custom Python connector

   • Testing and publishing custom connectors

2. Airbyte API (1 hour)

   • Overview of Airbyte REST API endpoints

   • Programmatic connector management and pipeline automation

   • API-based sync triggering demo

3. Advanced Features (1.5 hours)

   • Data deduplication and logging

   • Webhooks, notifications, and event triggers

   • Integration with Airflow / Prefect

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Week 5: Security, Optimization & Enterprise-Grade Deployment (4 Hours)

Goal: Implement secure, scalable, and optimized data pipelines.

1. Security and Access Control (1 hour)

   • Authentication and encryption overview

   • Role-based access management

   • Data privacy and compliance (GDPR, SOC 2)

2. Pipeline Optimization (1 hour)

   • Optimizing large syncs and parallel connections

   • Resource management and caching strategies

   • Handling API rate limits and retry mechanisms

3. Enterprise Deployment (1 hour)

   • Airbyte Cloud vs Self-Hosted

   • Scaling Airbyte with Kubernetes

   • Backup and restore procedures

4. Best Practices & Monitoring (1 hour)

   • Monitoring Airbyte with Prometheus & Grafana

   • Alerting and error tracking strategies

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Week 6: Capstone Project & Certification (4 Hours)

Goal: Build and deploy a complete Airbyte data engineering project from scratch.

1. Capstone Project Introduction (30 mins)

   • Problem statement and dataset overview

   • Objectives and expected outcomes

2. Capstone Hands-On Build (2.5 hours)
   End-to-End Project Example:

   • Source: PostgreSQL + API Source (e.g., GitHub / Shopify)

   • Destination: Snowflake / BigQuery

   • Transformation: dbt integration

   • Automation: Airbyte API or Airflow orchestration

   • Monitoring & Logging

3. Project Presentation & Discussion (30 mins)

   • Sharing learnings and design decisions

   • Reviewing pipeline performance and optimizations

4. Final Review & Certification (30 mins)

   • Course recap: beginner → advanced concepts

   • Assessment quiz and feedback

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🧩 Optional Add-ons

• Advanced Workshop: Airbyte Cloud & multi-tenant setup

• Data Observability Integration: Airbyte with Monte Carlo or Soda

• CI/CD Pipeline Integration: Automate with GitHub Actions

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🧰 Tools & Technologies

• Airbyte (Open Source / Cloud)

• Docker & Docker Compose

• dbt Core / dbt Cloud

• Python (for custom connectors)

• Airflow / Prefect (for orchestration)

• Cloud Data Warehouse: BigQuery / Snowflake / Redshift / Databricks

• Monitoring Tools: Prometheus, Grafana

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🎯 Learning Outcomes

By the end of this course, participants will:
✅ Understand ELT principles and Airbyte’s architecture
✅ Build, schedule, and monitor automated ELT pipelines
✅ Integrate dbt for in-warehouse transformations
✅ Develop custom Airbyte connectors using Python
✅ Automate pipelines using the Airbyte API
✅ Deploy and scale Airbyte for enterprise environments