Udemy - Databricks Spark Streaming, Delta Lake and SQL, AI Analytics torrent

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