Introduction to DataBricks

Course Description

The Introduction to Databricks course offers a beginner-friendly yet comprehensive foundation in big data processing, data engineering, and machine learning using the Databricks platform. Designed for individuals and teams looking to understand the power of Apache Spark in a collaborative cloud-based environment, this course introduces the essential components of Databricks and how it simplifies large-scale data workflows.

Through guided instruction and hands-on exercises, participants will learn how to create notebooks, configure clusters, query data, visualize outputs, and manage structured streaming. The course also introduces Delta Lake, Databricks Jobs, and integrations with Azure and AWS, giving learners a practical perspective on managing real-time and batch data pipelines.

This course serves as a stepping stone for data professionals transitioning into modern data platforms and cloud-native big data processing.

Key Learning Objectives

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

• Understand the core architecture of Databricks and its integration with Apache Spark.
• Navigate the Databricks workspace, create clusters, and build notebooks to execute code interactively.
• Connect to and work with structured and semi-structured data sources, including using basic SQL for querying.
• Create visual representations of data and use built-in visualization tools to derive insights.
• Learn how Delta Lake enhances reliability and performance in data lakes using ACID transactions.
• Set up and run automated data pipelines with Databricks Jobs and manage their parameters and alerts.
• Explore the cross-platform capabilities of Databricks on Azure and AWS environments.

Prerequisites

To get the most out of this course, learners should have:

• A basic understanding of data analysis concepts.
• Familiarity with SQL and Python (helpful but not mandatory).
• General awareness of cloud platforms such as Azure or AWS.

This course is built for accessibility and offers a gentle entry point for those new to Spark, cloud environments, or Databricks.

Audience Profile

This course is ideal for:

• Data Engineers and ETL Developers new to Databricks or Apache Spark.
• Data Analysts looking to explore distributed data processing and visualization at scale.
• Machine Learning Practitioners seeking to understand the infrastructure behind scalable model development.
• Cloud Engineers or DevOps professionals building data pipelines using Azure or AWS.
• Beginners in Big Data who want a strong foundational introduction to Spark-based workflows.

No prior experience with Databricks is required, making it suitable for both technical and semi-technical learners entering the big data ecosystem.

Career Growth & Industry Demand

Databricks has become a core platform in modern data engineering, data science, and AI workflows due to its unified approach to analytics and big data. Companies using Spark and Databricks seek professionals with foundational knowledge of how to develop, manage, and optimize data workloads in the cloud.

Job Roles After Completing This Course

• Junior Data Engineer
• Spark Developer
• Big Data Analyst
• ETL Developer
• Cloud Data Platform Associate
• Data Science Intern / Trainee

Industries That Hire Databricks Professional

• Manufacturing: Companies in this sector leverage Databricks to enhance production efficiency, implement predictive maintenance, and optimize supply chain operations. ​
• Retail and E-Commerce: Retailers utilize Databricks for customer segmentation, personalized marketing, and demand forecasting to improve customer experiences and inventory management. ​
• Healthcare: Healthcare organizations employ Databricks to integrate and analyze patient data, leading to improved patient care and streamlined operations.
• Financial Services: Financial institutions use Databricks for real-time fraud detection, risk analysis, and to ensure compliance with regulatory standards.
• Supply Chain and Logistics: Companies in this industry adopt Databricks to enhance efficiency, optimize operations, and improve demand forecasting. ​

Why Enroll in This Course?

• Foundational Knowledge: Get a strong start in Databricks, Spark, and cloud-based analytics workflows.
• Industry-Relevant Skills: Learn what modern data teams use in real-world scenarios.
• Cloud-Ready Learning: Explore Azure and AWS deployments to stay platform-agnostic.
• Hands-On Practice: Work directly with notebooks, jobs, and Delta tables to build confidence.
• Step Toward Certification: Build the skills needed to pursue Databricks certifications or more advanced data engineering roles.
• Future-Proof Your Career: Equip yourself with the platform knowledge that’s driving modern data ecosystems.