Title: Data Engineering with Databricks
Location: US
Company: Pragmatic AI Labs
About this course:
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
Become an expert in modern data engineering on Databricks' unified lakehouse platform. Master ETL pipelines, data transformations with Apache Spark, and Delta Lake for reliable data management.
Duration: 3 weeks
Curriculum:
- Module 1: Databricks Lakehouse Platform Fundamentals
- Module 2: Data Transformation and Pipelines
Show interest and get access to the course