This DataBricks Fundamentals course offers an in-depth, hands-on exploration of the DataBricks platform, guiding participants through its features and advantages for efficient data processing. Explore the DataBricks architecture, from clusters and notebooks to runtime integration with Apache Spark. Gain practical insights into data import, exploration, and transformation using Spark DataFrames and Spark SQL. Master advanced tasks such as machine learning workflows with MLlib and leveraging the power of DataBricks Delta for optimized data storage and operations. Learn about performance optimization techniques, job scheduling, and automation, ensuring streamlined workflows. Collaborative development practices and advanced data pipelines creation are also covered, empowering teams to implement best practices. With a focus on security, governance, and auditing, participants will leave equipped with the skills to unlock the full potential of DataBricks for their data-driven initiatives.
Purpose
| Learn the features and benefits of using DataBricks for efficient data processing. |
Audience
| Data Engineers and Data Scientists that rely on big data analytics and machine learning applications. |
Role
| Software Developers | Data Engineers | Data Scientists |
Skill level
| Intermediate |
Style
| Lecture | Hands-on Activites |
Duration
| 4 days |
Related technologies
| Apache Spark | MLlib | SQL |
Productivity objectives
- Explain the advantages of using DataBricks
- Describe the architecture and core components of DataBricks
- Examine DataBricks tools and techniques
- Apply use of the tools for efficient data processing