Live Online Stitch Course for Data Engineering

Live Course Module: Stitch Course for Data Engineering

Total Duration: 24 Hours (6 Weeks)

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

Goal: Understand the fundamentals of ELT, the role of Stitch, and set up the environment.

1. Introduction to Data Integration & ELT (45 mins)

   • ETL vs ELT in modern data pipelines

   • The place of Stitch in the modern data stack

   • Use cases for Data Engineers

2. Overview of Stitch (45 mins)

   • What is Stitch?

   • Architecture, workflow, and ecosystem

   • Key concepts: Sources, Destinations, and Pipelines

3. Setting Up Your Stitch Environment (1 hour)

   • Account setup and interface walkthrough

   • Understanding Stitch dashboard and workspace

   • Stitch pricing and usage overview

4. Your First Data Pipeline (1.5 hours)

   • Creating a data source (e.g., PostgreSQL, Google Analytics, Shopify)

   • Selecting a destination (e.g., Snowflake, BigQuery, Redshift)

   • Configuring and running your first ELT job

   • Monitoring data sync and verifying results

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Week 2: Working with Sources, Destinations & Data Flow (4 Hours)

Goal: Learn to manage sources, destinations, and sync processes.

1. Stitch Sources Deep Dive (1 hour)

   • Supported sources: Databases, SaaS apps, and Webhooks

   • Configuring replication methods (Full Table, Incremental, Log-based)

   • Data extraction and replication logic

2. Destinations Deep Dive (1 hour)

   • Supported destinations (Snowflake, BigQuery, Redshift, Databricks)

   • Destination configuration best practices

   • Managing data retention and warehouse permissions

3. Data Flow & Schema Management (1 hour)

   • Understanding Stitch’s data flow process

   • Schema mapping and naming conventions

   • Handling schema changes and evolution

4. Monitoring, Alerts, and Troubleshooting (1 hour)

   • Sync logs and error messages

   • Setting up alerts and notifications

   • Troubleshooting failed syncs and data mismatches

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Week 3: Stitch Advanced Features & Transformations (4 Hours)

Goal: Learn advanced Stitch capabilities and integration with transformation tools.

1. Advanced Stitch Configuration (1 hour)

   • Scheduling syncs

   • Configuring replication frequency and priorities

   • Managing multiple sources and destinations

2. Transformations Overview (1 hour)

   • What transformations are and why they matter

   • Stitch’s approach: ELT-first strategy

   • Integration with external transformation tools

3. dbt Integration with Stitch (1.5 hours)

   • Connecting dbt with Stitch for data modeling

   • Creating dbt models and running transformations post-load

   • Scheduling transformations in Stitch

4. Hands-on Lab (30 mins)

   • Build a Stitch + dbt transformation pipeline

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Week 4: Security, Governance & Optimization (4 Hours)

Goal: Learn about data security, governance, and performance tuning.

1. Security in Stitch (1 hour)

   • Authentication & encryption standards

   • Role-based access control (RBAC)

   • Compliance (GDPR, SOC 2, HIPAA overview)

2. Audit Trails & Metadata Management (1 hour)

   • Monitoring data lineage

   • Using Stitch logs for auditing

   • Integration with monitoring tools (e.g., Datadog, CloudWatch)

3. Pipeline Performance Optimization (1.5 hours)

   • Managing large datasets and batch sizes

   • Optimizing warehouse loading performance

   • Sync frequency and incremental strategies

4. Cost Optimization (30 mins)

   • Understanding Stitch pricing model

   • Reducing cost with efficient sync schedules

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Week 5: Automation, APIs, and Integration (4 Hours)

Goal: Automate Stitch workflows and extend with APIs and orchestration tools.

1. Automation and Scheduling (1 hour)

   • Automating Stitch syncs

   • Time-based and event-driven automation

   • Using webhooks and triggers

2. Stitch REST API (1.5 hours)

   • Overview of Stitch API endpoints

   • Managing connectors via API

   • Monitoring pipelines and fetching metadata programmatically

3. Integration with Orchestration Tools (1 hour)

   • Airflow, Prefect, or Dagster integration

   • Building a data pipeline orchestration flow

4. Practical Lab (30 mins)

   • API-based pipeline automation exercise

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Week 6: Enterprise-Grade Deployment & Capstone Project (4 Hours)

Goal: Apply all concepts in a real-world enterprise data engineering scenario.

1. Enterprise Deployment Patterns (1 hour)

   • Multi-region and multi-environment setups

   • CI/CD for Stitch pipelines (with GitHub Actions)

   • Governance and scalability strategies

2. Capstone Project (2 hours)
   End-to-End Data Pipeline Project:

   • Source: PostgreSQL or Salesforce

   • Destination: Snowflake or BigQuery

   • Transformation: dbt or external SQL engine

   • Automation: Stitch API or Airflow integration

   • Monitoring & Documentation

3. Final Review and Certification (1 hour)

   • Recap of beginner → advanced concepts

   • Best practices for production deployment

   • Assessment and Q&A session

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🧩 Optional Add-ons

• Advanced Stitch API Lab: Build custom automation scripts for connectors.

• Data Observability Workshop: Integrate Stitch with tools like Monte Carlo or Soda.

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🧰 Tools & Technologies Used

• Stitch Platform (Web UI & REST API)

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

• dbt (Core or Cloud)

• Airflow / Prefect (for orchestration)

• GitHub / GitLab (for version control & CI/CD)

• Visualization Tools: Looker / Tableau / Metabase

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🎯 Learning Outcomes

By the end of this 6-week course, learners will:
✅ Understand ELT workflows and Stitch’s role in modern data pipelines
✅ Build and monitor Stitch connectors and destinations
✅ Integrate Stitch with dbt for post-load transformations
✅ Secure, optimize, and automate Stitch pipelines using APIs
✅ Deploy enterprise-grade, production-ready data pipelines