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

1. Understand the architecture and capabilities of Databricks and Apache Spark.
2. Build and orchestrate scalable data pipelines using Delta Lake and Databricks Workflows.
3. Perform data exploration and analytics using SQL and notebooks.
4. Implement feature engineering and train machine learning models using MLflow.
5. Integrate Databricks with cloud storage and BI tools.
6. Automate data operations with job scheduling and parameterized pipelines.
7. Ensure data quality, lineage, and governance within the Databricks Lakehouse.
8. Deploy models in production using Databricks' ML lifecycle management tools.

Databricks - Course Syllabus

1. Introduction to Databricks

• Introduction to Databricks
• What is Databricks? Platform Overview
• Key Features of Databricks Workspace
• Databricks Architecture and Components
• Databricks vs Traditional Data Platforms

2. Getting Started with Databricks

• Setting Up a Databricks Workspace
• Databricks Notebook Basics
• Importing and Organizing Datasets in Databricks
• Exploring Databricks Clusters
• Databricks Community Edition: Features and Limitations

3. Data Engineering in Databricks

• Introduction to ETL in Databricks
• Using Apache Spark with Databricks
• Working with Delta Lake in Databricks
• Incremental Data Loading Using Delta Lake
• Data Schema Evolution in Databricks

4. Data Analysis with Databricks

• Running SQL Queries in Databricks
• Creating and Visualizing Dashboards
• Optimizing Queries in Databricks SQL
• Working with Databricks Connect for BI Tools
• Using the Databricks SQL REST API

5. Machine Learning & Data Science

• Introduction to Machine Learning with Databricks
• Feature Engineering in Databricks
• Building ML Models with Databricks MLFlow
• Hyperparameter Tuning in Databricks
• Deploying ML Models with Databricks

6. Integration and APIs

• Integrating Databricks with Azure Data Factory
• Connecting Databricks with AWS S3 Buckets
• Databricks REST API Basics
• Connecting Power BI with Databricks
• Integrating Snowflake with Databricks

7. Performance Optimization

• Understanding Databricks Auto-Scaling
• Cluster Performance Optimization Techniques
• Partitioning and Bucketing in Databricks
• Managing Metadata with Hive Tables in Databricks
• Cost Optimization in Databricks

8. Security and Compliance

• Securing Data in Databricks Using Role-Based Access Control (RBAC)
• Setting Up Secure Connections in Databricks
• Managing Encryption in Databricks
• Auditing and Monitoring in Databricks

9. Real-World Applications

• Real-Time Streaming Analytics with Databricks
• Data Warehousing Use Cases in Databricks
• Building Customer Segmentation Models with Databricks
• Predictive Maintenance Using Databricks
• IoT Data Analysis in Databricks

10. Advanced Topics in Databricks

• Using GraphFrames for Graph Processing in Databricks
• Time Series Analysis with Databricks
• Data Lineage Tracking in Databricks
• Building Custom Libraries for Databricks
• CI/CD Pipelines for Databricks Projects

11. Closing & Best Practices

• Best Practices for Managing Databricks Projects

Upon successful completion, learners receive a Course Completion Certificate from Uplatz, validating their expertise in Databricks for data engineering, analytics, and machine learning.

This certification is a powerful credential for roles involving modern data architecture, cloud analytics, and AI engineering. It adds significant value to your professional portfolio, especially for those targeting cloud platforms like AWS, Azure, and GCP.

Additionally, this course prepares learners for official Databricks certification exams such as:

• Databricks Certified Data Engineer Associate
• Databricks Certified Machine Learning Associate
• Databricks Lakehouse Fundamentals

Learners gain not only hands-on practice but also the theoretical foundation necessary to pursue these globally recognized certifications.

By completing this course, you'll open the door to high-demand roles in the cloud data and AI ecosystem, including:

1. Cloud Data Engineer
2. Databricks Developer
3. Data Analytics Engineer
4. Machine Learning Engineer
5. Big Data Architect
6. AI/Data Consultant

Companies across industries—especially those using Azure, AWS, and GCP—are adopting Databricks to modernize their data infrastructure and drive innovation. This course prepares you for success in cloud-first, data-driven environments.

1. What is Databricks and how does it differ from traditional data platforms?
Databricks is a cloud-based data platform that unifies data engineering, analytics, and machine learning. Unlike traditional platforms, it uses Apache Spark for distributed processing and integrates data lakes with data warehouses in a "Lakehouse" architecture, enabling seamless collaboration across teams.

2. How does Delta Lake enhance data reliability and consistency in Databricks?
Delta Lake introduces ACID transactions, schema enforcement, and time travel to cloud storage, ensuring data consistency, version control, and reliability even in complex ETL workflows and streaming use cases.

3. What is the role of MLflow in the Databricks machine learning lifecycle?
MLflow is an open-source platform integrated into Databricks that manages the entire ML lifecycle, including experiment tracking, model packaging, deployment, and the model registry. It promotes reproducibility and scalability of ML workflows.

4. How would you build a data pipeline in Databricks using notebooks and workflows?
You would create modular notebooks for ingestion, transformation, and loading. Then use Databricks Workflows to schedule and orchestrate these notebooks as a pipeline with parameters, conditional logic, and retry policies.

5. What are the advantages of using the Lakehouse architecture over separate data lakes and warehouses?
The Lakehouse architecture combines the scalability of data lakes with the performance and reliability of data warehouses, reducing data duplication, lowering costs, and enabling real-time analytics and machine learning on the same platform.

6. How can Databricks be integrated with BI tools and external data sources?
Databricks supports connectors for Power BI, Tableau, and JDBC/ODBC. It can also integrate with cloud storage (S3, ADLS), relational databases, and REST APIs, making it easy to consume and publish data.

7. What security features does Databricks provide for enterprise data governance?
Databricks offers role-based access control (RBAC), Unity Catalog for fine-grained access, audit logging, encryption at rest and in transit, and compliance with standards like HIPAA, GDPR, and SOC 2.

8. What metrics would you monitor to evaluate the performance of Databricks workloads?
Key metrics include Spark job execution time, cluster utilization, job failure rate, cost per job, data throughput, and task retries. Monitoring tools in Databricks provide detailed execution graphs and logs for performance tuning.

1. Is this course beginner-friendly?
   Yes, it starts from foundational concepts and progresses to advanced implementations, suitable for both newcomers and experienced professionals.
2. Do I need prior knowledge of Spark or Python?
   Some familiarity with Python and data processing is helpful, but not mandatory. All concepts are explained with hands-on examples.
3. Is there hands-on lab work included?
   Absolutely. The course includes guided labs, notebooks, and assignments for real-world practice.
4. Will I receive a certificate?
   Yes, a Course Completion Certificate from Uplatz is awarded upon completion.
5. Can I prepare for official Databricks certifications through this course?
   Yes, the course content aligns with Databricks' certification tracks and helps you prepare for official exams.
6. How long will I have access to the course?
   Lifetime access is provided so you can learn at your own pace.
7. Is this course suitable for cloud platforms like AWS, Azure, or GCP?
   Yes, Databricks integrates with all major clouds. Concepts covered are cloud-agnostic but include deployment tips for each.