Udemy - Databricks Spark Streaming, Delta Lake and SQL, AI Analyt...

Databricks Spark Streaming, Delta Lake & SQL, AI Analytics

https://WebToolTip.com

Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 30m | Size: 3.34 GB

Spark Structured Streaming, Auto Loader, COPY INTO, Delta Lake and Databricks SQL Genie with AI-powered data engineering

What you'll learn
Real-Time Data Processing with Spark Structured Streaming
Understanding batch vs streaming data processing
Spark Structured Streaming fundamentals
Micro-batch processing in Spark
Streaming sources, transformations, and sinks
Building streaming pipelines in Databricks
Writing streaming data with writeStream
Understanding triggers and streaming execution control
Using checkpointing for fault tolerance
Building reliable streaming pipelines with Delta Lake
Scalable Data Ingestion with Databricks Auto Loader
Auto Loader fundamentals and architecture
File discovery and incremental ingestion
Schema inference and schema management
Schema evolution and rescue mode
Building Auto Loader pipelines in Databricks
Writing Auto Loader output to Delta Lake
Understanding checkpoint and state management
Inspecting Auto Loader results and Delta logs
SQL-Based Data Ingestion with COPY INTO
Understanding COPY INTO in Databricks
Loading files into Delta tables using SQL
Batch and semi-batch ingestion patterns
Incremental loading without duplicate ingestion
Creating Bronze tables with COPY INTO
Transforming Bronze data into Silver tables
Working with Delta Lake ingestion workflows
Introduction to Databricks SQL
Understanding the Databricks SQL interface
Exploring the SQL Editor
Writing and running analytical SQL queries
Working with multiple SQL queries
Exporting SQL query results
Using parameters in Databricks SQL queries
Using Query Snippets for reusable SQL logic
Scheduling SQL queries
Understanding SQL Warehouses
Query History and Query Profile
Monitoring SQL query performance
Understanding query execution details
Creating alerts from SQL query results
Building monitoring logic with Databricks SQL alerts
Using Genie in Databricks SQL
Asking questions with natural language
Building AI-assisted dashboards
Turning processed data into business insights
Presenting Lakehouse data to business users

Requirements
A basic understanding of Databricks fundamentals, including notebooks, clusters, and Delta tables, is recommended
A working computer: Windows, Mac, or Linux
A stable internet connection
Access to Databricks Free Edition or any Databricks workspace
Basic understanding of SQL
Basic understanding of data tables, columns, and rows
Basic familiarity with Python or PySpark is helpful but not mandatory
Interest in data engineering, analytics, and modern data platforms
Curiosity about Databricks, Spark, Delta Lake, and Lakehouse architecture
No advanced Spark knowledge required
No prior Structured Streaming experience required
No prior Auto Loader experience required
No advanced BI or dashboarding experience required
Motivation to learn how real-world data pipelines and analytics systems work