Data Engineering with Databricks

Master Data Engineering on Databricks Lakehouse Platform Learn Databricks architecture, cluster management & notebook analysis Build reliable ETL pipelines with Delta Lake for data transformation Implement advanced data processing techniques with Apache Spark Course Highlights: Create & scale Databricks clusters for workloads Load data from diverse sources into notebooks Explore, visualize & profile datasets with notebooks Version control & share notebooks via Git integration Read & ingest data in various file formats Transform data with SQL & DataFrame operations Handle complex data types like arrays, structs, timestamps Deduplicate, join & flatten nested data structures Identify & fix data quality issues with UDFs Load cleansed data into Delta Lake for reliability Build production-ready pipelines with Delta Live Tables Schedule & monitor workloads using Databricks Jobs Secure data access with Unity Catalog Gain comprehensive skills in data engineering on Databricks through hands-on labs, real-world projects and best practices for the modern data lakehouse.

• Use Databricks for data engineering and ML workloads
• Create and design ML pipelines
• Use Llamafile and other local LLMs like Mixtral

• Beginner experience in programming languages.
• Git for version control.
• Using a text editor for development.