Apache Spark Programming with Databricks

Course Description:

The Apache Spark Programming with Databricks course is an intensive 16-hour program designed to provide participants with hands-on experience in using Apache Spark on the Databricks platform. This course covers essential Spark programming concepts, advanced features, and best practices to process, analyze, and manage large-scale data efficiently.
Participants will explore the Spark ecosystem, including Spark SQL, DataFrames, and the Delta Lake. The course also dives into advanced topics like user-defined functions (UDFs), query optimization, and real-time data processing with Spark Streaming. By the end of the course, learners will be equipped to build scalable, high-performance data pipelines and analytics solutions using Databricks.
By the end of this course, participants will be able to:
• Understand the architecture and features of Apache Spark and the Databricks platform.
• Use Spark SQL and DataFrames to process and transform data.
• Apply advanced data processing techniques, including UDFs and vectorized UDFs.
• Optimize Spark queries and manage data partitioning for performance improvements.
• Implement real-time data processing with the Spark Streaming API.
• Use Delta Lake for reliable data storage and versioning in Spark workflows.

Prerequisites:

This course is designed for intermediate-level learners. Participants should have:
• Basic programming knowledge, preferably in Python or Scala.
• Familiarity with data processing concepts and tools.
• Understanding of SQL for querying structured data.
• Knowledge of distributed computing (optional but beneficial).

Audience Profile:

This course is ideal for:
1. Data Engineers: Professionals building and managing large-scale data pipelines.
2. Data Scientists: Individuals leveraging Spark for data analysis and machine learning workflows.
3. Big Data Developers: Developers seeking to improve their Spark programming skills on Databricks.
4. IT Professionals: Those supporting or exploring Spark-based data processing in enterprise environments.
5. Students and Enthusiasts: Learners interested in big data processing and analytics.

Course Duration: 16 hours