Databricks and Lakehouse Architecture Course: Advancing Applied Data Engineering Practices

Introduction

Data engineering is rapidly evolving with the emergence of the Lakehouse architecture, a modern paradigm that unifies the flexibility of data lakes with the reliability and structure of data warehouses. As organizations seek to leverage big data for analytics, machine learning, and real-time decision-making, the Databricks platform has become a cornerstone of modern applied data engineering practices. This course is designed to provide professionals with the knowledge and skills to build scalable, secure, and high-performance data systems using Databricks and the Lakehouse model.

The program begins with a comprehensive foundation in Lakehouse concepts and Databricks capabilities, guiding participants through key components such as Delta Lake, data ingestion pipelines, and collaborative workspaces. Learners will gain hands-on experience working with structured and unstructured data, managing complex transformations, and orchestrating workloads for analytics and AI applications.

A central focus of the course is applied data engineering, where participants will learn how to build enterprise-grade pipelines that support batch, streaming, and machine learning workflows. Through practical exercises, learners will explore how to optimize storage, improve query performance, and integrate Databricks with cloud-native ecosystems.

The course also emphasizes governance, security, and compliance within Lakehouse environments, preparing participants to design systems that meet enterprise data management standards. Topics include role-based access control, auditing, data lineage, and integration with regulatory frameworks.

Finally, the course covers emerging trends in Databricks and Lakehouse adoption, including AI/ML integration, real-time analytics, and multi-cloud strategies. By completing this training, participants will not only master Databricks tools but also develop the strategic mindset to advance data engineering practices and deliver business value through innovative data architectures.

Who Should Attend

• Data engineers building pipelines on Databricks.
• BI and analytics professionals seeking Lakehouse expertise.
• Cloud architects designing scalable data infrastructures.
• Machine learning engineers integrating pipelines with AI models.
• Data scientists requiring advanced data preparation skills.
• IT managers and decision-makers implementing data-driven solutions.
• Enterprise data architects modernizing legacy data systems.
• Consultants advising organizations on data strategy.
• System administrators managing Databricks environments.
• Researchers in data engineering.

Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

• Understand the principles and advantages of Lakehouse architecture.
• Build and optimize scalable data pipelines with Databricks.
• Ingest, transform, and manage structured and unstructured data.
• Leverage Delta Lake for ACID compliance and reliability.
• Implement real-time and batch data processing pipelines.
• Apply advanced performance optimization techniques.
• Integrate Databricks with cloud platforms and enterprise systems.
• Implement governance, security, and compliance in Lakehouse systems.
• Support AI and machine learning workflows with engineered pipelines.
• Apply best practices and emerging trends in Lakehouse adoption.

Comprehensive Course Outline

Module 1: Introduction to Databricks and Lakehouse

• Evolution from data warehouses and lakes to Lakehouse.
• Core components of Databricks platform.
• Delta Lake fundamentals for reliability and performance.
• Key use cases of Lakehouse architecture in enterprises.

Module 2: Data Ingestion and Integration

• Ingesting structured, semi-structured, and unstructured data.
• Integration with cloud storage and enterprise databases.
• Real-time ingestion with streaming technologies.
• Automating ingestion workflows in Databricks.

Module 3: Data Transformation and Processing

• ETL and ELT pipelines in Databricks.
• Working with PySpark and SQL APIs.
• Managing schema evolution and data quality.
• Optimizing transformations for large-scale processing.

Module 4: Delta Lake and Advanced Features

• Delta Lake architecture and ACID transactions.
• Time travel and version control of datasets.
• Optimizing storage and indexing for performance.
• Managing slowly changing dimensions with Delta Lake.

Module 5: Real-Time and Batch Data Engineering

• Designing hybrid batch and streaming pipelines.
• Structured streaming in Databricks.
• Event-driven architectures with Kafka integration.
• Use cases: fraud detection, IoT analytics, and more.

Module 6: Governance, Security, and Compliance

• Access controls and role-based permissions.
• Data lineage, auditing, and monitoring.
• Compliance with GDPR, HIPAA, and regulatory frameworks.
• Secure collaboration across teams.

Module 7: Machine Learning and AI Integration

• Preparing training datasets at scale.
• Feature engineering with Databricks Feature Store.
• Integration with MLflow for model management.
• Deploying ML models within Lakehouse pipelines.

Module 8: Cloud and Multi-Cloud Integration

• Deploying Databricks on AWS, Azure, and GCP.
• Hybrid and multi-cloud Lakehouse strategies.
• Interoperability with enterprise data warehouses.
• Best practices for cost optimization in the cloud.

Module 9: Advanced Optimization Techniques

• Performance tuning for queries and pipelines.
• Partitioning, caching, and indexing strategies.
• Managing workloads with clusters and autoscaling.
• Optimizing large-scale analytics workloads.

Module 10: Future Trends

• End-to-end Databricks Lakehouse project.
• Real-world case studies of Lakehouse adoption.
• AI-driven automation in Databricks.
• Future trends in Lakehouse and applied data engineering.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better