Course Description:

This comprehensive course offers an in-depth exploration of Apache Spark and Databricks, tailored for aspiring or current data professionals looking to enhance their skills in data engineering. It provides a foundational understanding of Apache Spark’s architecture, essential components like DataFrames, RDDs, and Spark SQL, as well as practical applications using Databricks. The curriculum includes detailed notes, step-by-step guides on Spark and Databricks installations, and best practices for data transformation and analysis. Notably, the course features two major projects focused on building real-world data pipelines using Spotify and eCommerce datasets.

What Students Will Learn:

• Core principles and components of Apache Spark and how they interoperate.
• Operational knowledge of Spark’s DataFrame API, transformations, actions, and lazy evaluations.
• Management and optimization techniques for Spark data processing.
• Comprehensive understanding of Databricks, including its components like Delta Lake and DBFS.
• Hands-on experience in setting up, deploying, and managing data pipelines.
• Insights into production-level application deployment, monitoring, and debugging within a Spark environment.

Pre-requisites or Skills Necessary:

Participants should have a basic understanding of programming, preferably in Python, as well as fundamental concepts of databases. Knowledge of data processing and some familiarity with cloud services would be advantageous but not required.

Course Modules:

• Introduction to Apache Spark and its ecosystem
• Deep dive into Spark’s Architecture and Core APIs including DataFrames and RDDs
• Setting up and using Apache Spark and Databricks
• Understanding and implementing joins, UDFs, and data types in Spark
• Data ingestion and processing using various sources like CSV, JSON, Parquet
• Integration of Spark with Databricks for optimized data handling
• Project-based learning with end-to-end data engineering projects
• Detailed guides on Spark deployment, debugging, and monitoring

Who This Course Is For:

• Cloud Data Engineers
• IT Analysts
• Technical Consultants
• Web Developers
• Data Engineers in service companies
• Systems Engineers
• Anyone aiming to transition into a Data Engineering role within a product company

Real-World Applications:

Skills acquired from this course can be directly applied in real-world data processing tasks. Learners will be equipped to handle massive datasets efficiently, build and maintain scalable data pipelines, troubleshoot and debug issues in Spark applications, and leverage Databricks for enhanced data engineering tasks. These capabilities are essential for roles in data infrastructure teams across various industries, and they help companies make data-driven decisions more effectively.

Bonuses Included:

• Access to all Python code templates for practice
• Discounts on future courses
• An interactive community of learners
• Opportunities to suggest course content improvements
• Gamified learning experiences
• Priority in feedback responses and support