This Databricks training is designed to help participants use the Databricks Lakehouse Platform for big data processing, analytics, and machine learning. The course covers Databricks fundamentals, Apache Spark basics, data ingestion, data transformation, Delta Lake, and collaborative analytics workflows. Participants will gain hands-on experience to build scalable data engineering and analytics solutions using Databricks.

• Understand the architecture, data engineering capabilities, and analytics features of Databricks for modern data processing and  machine learning workflows.
• Set up and configure the Databricks environment, clusters, workspaces, data sources, and development components for analytics  projects.
• Design notebooks, data pipelines, transformations, and collaborative analytics workflows using Databricks development approaches.
• Implement data engineering, big data processing, SQL analytics, and machine learning workflows effectively.
• Debug, test, and optimize data pipelines, queries, and cluster performance for scalability and maintainability.
• Build scalable, automated, and production-ready data analytics solutions using Databricks best practices.

 • Data engineers and analytics engineers
 • Data analysts and BI professionals
 • Data scientists and machine learning practitioners
 • Cloud and platform engineers
 • Teams adopting Databricks Lakehouse

 • Basic understanding of data concepts and databases
 • Familiarity with SQL or Python is helpful
 • Interest in big data and analytics platforms

 • Navigating Databricks workspace and notebooks
 • Using Apache Spark for data processing
 • Ingesting and transforming data in Databricks
 • Working with Delta Lake tables
 • Writing SQL and Python in Databricks
 • Collaborating using shared notebooks
 • Managing data pipelines basics
 • Applying Databricks best practices

By the end of this training, participants will be able to build and manage scalable data engineering and analytics workflows using Databricks with confidence. Learners will gain strong fundamentals in Spark, Delta Lake, and collaborative analytics, enabling them to support modern lakehouse-based data platforms.

Introduction to Databricks & Lakehouse Architecture
 • What is Databricks and Lakehouse concept
 • Databricks platform architecture
 • Databricks workspace and clusters
 • Navigating notebooks and UI

Apache Spark Fundamentals
 • Introduction to Apache Spark
 • Spark DataFrames basics
 • Reading and writing data
 • Basic transformations and actions

Data Ingestion & Storage
 • Ingesting data from files and databases
 • Working with cloud storage
 • Managing data formats such as Parquet and CSV
 • Introduction to Delta Lake

Delta Lake Fundamentals
 • Creating Delta tables
 • ACID transactions
 • Time travel basics
 • Managing schema evolution

Data Transformation & ETL Workflows
 • Building ETL pipelines in Databricks
 • Data cleansing and enrichment
 • Handling large datasets
 • Best practices for scalable transformations

Databricks SQL & Analytics
 • Using Databricks SQL
 • Creating views and queries
 • BI integration basics
 • Analytics dashboards overview

Collaboration & Workspace Management
 • Sharing notebooks
 • Version control basics
 • Managing users and permissions
 • Workspace organization best practices

Introduction to Machine Learning on Databricks
 • MLflow basics
 • Managing experiments
 • Simple ML workflows overview
 • Integrating ML with data pipelines

Performance Optimization & Cost Management
 • Cluster configuration basics
 • Caching and performance tuning
 • Monitoring workloads
 • Cost optimization strategies

• Databricks Setup & Data Platform Architecture
• Workspaces, Clusters & Data Source Configuration
• Notebooks, Data Pipelines & Transformation Workflows
• SQL Analytics, Big Data Processing & Machine Learning
• Testing, Debugging & Performance Optimization
• End-to-End Databricks Data Engineering Project

Participants will be evaluated through hands-on Databricks labs, practical data processing exercises, instructor-led reviews, and a final assessment focused on building a complete Databricks data pipeline and analytics workflow.

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Upon successful completion of the training, participants will receive an AcadNXT Certificate of Completion for Databricks. This digital, verifiable certification validates practical Databricks lakehouse, Apache Spark, and data engineering skills and can be shared on LinkedIn and included in professional profiles to enhance data engineering and analytics career credibility.