Course Objectives
1. Understand the Databricks Lakehouse Platform
Learn the fundamentals of the Databricks Lakehouse architecture and understand how modern organizations combine data lakes and data warehouses to build scalable analytics solutions.
2. Develop Data Processing Skills Using Databricks and Apache Spark
Gain hands-on experience working with Spark DataFrames, SQL analytics, data transformations, and distributed data processing techniques used in real-world data engineering projects.
3. Build Reliable Data Pipelines with Delta Lake
Learn how to create, manage, and optimize Delta Lake tables using ACID transactions, time travel, data versioning, and performance optimization techniques.
4. Design and Automate Modern ETL Workflows
Develop practical skills in building ETL pipelines using Delta Live Tables, workflow automation, data quality validation, and Bronze-Silver-Gold architecture patterns.
5. Prepare for Databricks Data Engineering Careers and Certification
Build job-ready skills through hands-on projects, production deployment practices, and certification-focused learning aligned with Databricks Data Engineer Associate concepts.
Popular Techniques Covered in This Course
Databricks Lakehouse Platform
Databricks Workspace Management
Databricks SQL Analytics
Apache Spark DataFrames
Delta Lake Fundamentals
Data Cleaning and Transformation
Delta Live Tables (DLT)
ETL Pipeline Development
Databricks Workflows and Job Scheduling
Data Engineering Best Practices
Get Hands-on Knowledge about Real-Time Projects
Project 1
1. Retail Sales Analytics Data Pipeline
In this project, learners will build an end-to-end retail analytics solution using Databricks. Sales data from multiple sources will be cleaned, transformed, and stored in Delta Lake tables. Students will create dashboards and reports that help businesses analyze sales performance, customer behavior, and product demand
Project 2
2. Healthcare Data Processing Platform
Students will develop a healthcare data pipeline that integrates patient records, appointment information, and operational data into a centralized platform. Using Spark and Delta Lake, learners will perform data transformation, validation, and reporting.
Project 3
3. Customer Data Quality and Cleansing Solution
This project focuses on improving the quality of customer data collected from different business systems. Learners will identify duplicate records, handle missing values, standardize data formats, and implement validation rules using Spark and Databricks.
Project 4
4. Bronze-Silver-Gold Data Lakehouse Implementation
In this project, students will design and build a complete Lakehouse architecture using Delta Live Tables. Raw data will be processed through Bronze, Silver, and Gold layers with automated data quality checks and lineage tracking.
Project 5
5. Automated ETL and Reporting Workflow
Learners will create a production-ready ETL solution using Databricks Workflows and Delta Lake. The pipeline will extract data, perform transformations, schedule automated jobs, and generate analytics-ready datasets. This project demonstrates how organizations automate large-scale data processing and reporting operations.
Key Features
Edubrights offers Databricks for Beginners: Data & AI Fundamentals Training in virtual mode with expert trainers. Here are the key features,
40 Hours Course Duration
100% Job Oriented Training
Industry Expert Faculties
Free Demo Class Available
Completed 500+ Batches
Certification Guidance
Curriculum
Module 1: Lakehouse Platform Introduction
- What is Databricks Lakehouse: data lake + warehouse hybrid architecture
- Traditional vs modern data platforms: ETL limitations overcome
- Unity Catalog overview: governance, access control basics
- Free Community Edition setup: account creation and first workspace
Module 2: Workspace Navigation & Clusters
- Databricks workspace interface: notebooks, clusters, SQL warehouses
- Cluster types: All-Purpose vs Jobs clusters comparison
- Starting/stopping clusters: cost management for students
- Cluster configuration: instance types, auto-scaling basics
Module 3: Notebooks & Python Fundamentals
- Multi-language notebooks: Python, SQL, Scala support
- Cell execution: run order, parameters, widgets creation
- Importing datasets: CSV upload, Chennai retail sales examples
- Data exploration: head(), describe(), shape() operations
Module 4: SQL Analytics Mastery
- Databricks SQL editor: query interface and saved queries
- Advanced SQL: CTEs, window functions, pivot/unpivot
- Creating dashboards: charts, filters, scheduled refreshes
- Materialized views: performance optimization techniques
Module 5: Delta Lake Essentials
- ACID transactions on data lakes: reliability guarantees
- Delta table creation: managed vs external tables
- Core commands: MERGE, OPTIMIZE, Z-ORDER clustering
- Time travel: querying historical data versions
Module 6: Apache Spark DataFrames
- Spark DataFrames vs Pandas: distributed processing advantages
- Key transformations: filter, groupBy, join operations
- Spark SQL integration: mixing DataFrame API and SQL
- Caching strategies: when and how to cache datasets
Module 7: Data Cleaning Pipelines
- Handling missing data: drop, fill, imputation strategies
- Outlier detection and removal techniques
- Pandas UDFs: complex Python functions at scale
- Data validation: schema checks and quality assertions
Module 8: Delta Live Tables (DLT)
- Declarative ETL: pipeline-as-code approach
- Bronze-Silver-Gold architecture implementation
- Streaming vs batch processing pipelines
- Data quality expectations and lineage tracking
Module 9: Production Deployment
- Databricks Workflows: job scheduling and orchestration
- CLI basics: automation and CI/CD integration
- Portfolio project: end-to-end Chennai healthcare ETL pipeline
- Associate Data Engineer certification exam blueprint
Receive Training From Our Skilled and Effective Trainers
Experience in the Industry Learn from Databricks-certified data engineers and ML engineers who have built production lakehouse platforms, Delta Live Tables pipelines, and ML workflows on Databricks for data-driven enterprises.
Backgrounds at the Top Our Databricks trainers have delivered data platform projects at technology companies, financial services firms, and healthcare organisations where Databricks is the central platform for data engineering and machine learning.
Clear & Effective Teaching Databricks architecture, Spark DataFrames, Delta Lake, Databricks SQL, MLflow, Delta Live Tables, Unity Catalog, and governance are explained clearly with real lakehouse data platform examples.
Hands-On Learning Focus Students build end-to-end lakehouse pipelines, work with Delta tables, create DLT pipelines, track ML experiments with MLflow, and configure Unity Catalog governance through structured lab exercises.
Up-to-Date Knowledge Trainers keep content current with the latest Databricks platform releases, Databricks AI and GenAI capabilities, Unity Catalog enhancements, and evolving lakehouse architecture best practices.
Databricks Data Engineer Associate Professional Certification
Edubrights Offers the Databricks Data Engineer Associate Professional Certification validates your ability to build, manage, and optimize data engineering solutions using the Databricks Lakehouse Platform. This certification demonstrates practical knowledge of Delta Lake, Apache Spark, Databricks SQL, Delta Live Tables, ETL pipeline development, data transformation, workflow orchestration, and production deployment practices.
Course FAQs
1. What is Databricks and why is it used?
Databricks is a cloud-based data platform that helps organizations process, manage, and analyze large volumes of data using Apache Spark and Lakehouse architecture.
2. Who should enroll in this Databricks Data Engineering Course?
This course is suitable for students, freshers, data analysts, software professionals, and aspiring data engineers who want to build practical skills in modern data platforms.
3. Do I need programming experience to learn Databricks?
Basic knowledge of SQL or Python is helpful, but beginners can learn Databricks by following the structured training and hands-on exercises included in the course.
4. What is the Databricks Lakehouse Platform?
The Databricks Lakehouse Platform combines the flexibility of data lakes with the performance and management capabilities of data warehouses in a unified environment.
5. What is Delta Lake and why is it important?
Delta Lake improves data reliability by providing ACID transactions, schema enforcement, data versioning, and performance optimization for large-scale analytics workloads.
6. What is Delta Live Tables (DLT)?
Delta Live Tables is a framework that simplifies the development and management of reliable data pipelines through automated workflows and built-in data quality checks.
7. What job roles can I apply for after completing this course?
You can pursue roles such as Data Engineer, Databricks Engineer, ETL Developer, Big Data Engineer, Data Analyst, and Cloud Data Engineer.
8. Does the course include practical projects?
Yes. The course includes hands-on projects that help learners build real-world data pipelines, ETL workflows, and analytics solutions using Databricks.
9. Is Databricks in demand in the job market?
Yes. Databricks is widely adopted by organizations for data engineering, analytics, machine learning, and cloud data management, creating strong demand for skilled professionals.
10. Will this course help me prepare for Databricks certification?
Yes. The curriculum covers key concepts related to Databricks Data Engineering and helps learners build practical experience useful for certification preparation.
11. What tools are used in Databricks?
Description: SQL, Python, Spark, and ML libraries.
12. Can beginners learn Databricks?
Description: Yes, it is suitable for beginners with data basics.
13. Is Databricks in demand?
Description: Yes, it is widely used in data engineering and AI industries.
14. What is ETL in Databricks?
Description: ETL is extracting, transforming, and loading data into systems.
15. Does Databricks support machine learning?
Description: Yes, it supports full ML lifecycle workflows.
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