Master Azure Databricks

Master Azure Databricks, available at $59.99, has an average rating of 4.45, with 193 lectures, 3 quizzes, based on 72 reviews, and has 545 subscribers.

You will learn about Azure Databricks Fundamentals RDD & PySpark DataFrame Spark Structure Streaming Databricks Advance concept (Delta lake,SQL Warehouse,Security,Devops,Administration) This course is ideal for individuals who are Data Engineering Students & Developers or Bigdata Developer or Python & SQL Developer It is particularly useful for Data Engineering Students & Developers or Bigdata Developer or Python & SQL Developer.

Enroll now: Master Azure Databricks

Summary

Title: Master Azure Databricks

Price: $59.99

Average Rating: 4.45

Number of Lectures: 193

Number of Quizzes: 3

Number of Published Lectures: 161

Number of Published Quizzes: 3

Number of Curriculum Items: 196

Number of Published Curriculum Objects: 164

Original Price: ?1,499

Quality Status: approved

Status: Live

What You Will Learn

Azure Databricks Fundamentals

RDD & PySpark DataFrame

Spark Structure Streaming

Databricks Advance concept (Delta lake,SQL Warehouse,Security,Devops,Administration)

Who Should Attend

Data Engineering Students & Developers

Bigdata Developer

Python & SQL Developer

Target Audiences

Data Engineering Students & Developers

Bigdata Developer

Python & SQL Developer

Module 1 :

What is Data Pipeline

What is Azure databricks

Azure Databricks Architecture

Azure Account Setup

WorkSpace Setup

Module 2:

Navigate the Workspace

Runtimes

Clusters

Notebooks

Libraries

Repos

Databricks File System (DBFS)

DBUTILS

Widgets

Workflows

Metastore – Setup external Metastore

Module 3 :

What is RDD

Creating RDD

RDD transformations

RDD Actions

RDD Joins

Pair RDD

Broadcast Variables

Accumulators

Convert RDD to DataFrame

Import & Read data

Create a table using the UI

Create a table in a Notebook

Module 4 :

Create DataFrames

Define Schema

Functions

Casting Operations

Filter Transformation

Update, Update ALL & UpdateByName

OrderBy & SortBY

GroupBy

Remove Duplicates

Window Functions

Date and Timestamp Functions

UDF (User Defined Function)

JOIN

Handle corrupt records using the badRecordsPath

File metadata column

Module 5 :

Read Parquet File

Read CSV Files

Read JSON Files

Read XML Files

Read Excel file

SQL databases using JDBC

Azure blob storage

Module 6 :

What is Spark Structure Streaming

Data Source & Sink

Rate & File Source

Kafka Source

Sink : Console, Memory, File & Custom

Build Streaming ETL

Stream ETL 1 : Setup Event Hub

Streaming ETL 2 : Event Hub Producer

Streaming ETL 3 : Integrate Event Hubs with Data Bricks

Streaming ETL 4 : Transformation

Streaming ETL 5 : Ingest into Azure Data storage

Twitter Sentiment Analysis – Introduction

Setup Twitter Developer Account

Twitter Sentiment Analysis – II

Twitter Sentiment Analysis – III

Module 7 :

Components in Databricks SQL

Configuring a SQL Endpoint

Creating a Table from a CSV File

Create Queries

Parameterized Query

Query Profile

Building Visualization (Table, BAR & PIE )

Building Line Chart & Counter Chart

Adding Charts to Dashboards

Defining a Query Alert

Access Control on Databricks SQL Objects

Lab: Data Object Access Control

Transfer Ownership

Access SQL Endpoint from Python

Databricks SQL CLI

Databricks SQL CLI

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Structure

Chapter 2: Azure DataBricks

Lecture 1: What is Data Pipeline

Lecture 2: Azure databricks

Lecture 3: Azure Databricks Architecture

Lecture 4: Azure Account Setup

Lecture 5: WorkSpace Setup

Chapter 3: Workspace, Notebook, Job, DBFS

Lecture 1: Agenda

Lecture 2: Navigate the Workspace

Lecture 3: Databricks Runtimes

Lecture 4: Clusters Part 1

Lecture 5: Cluster Part 2

Lecture 6: Notebooks

Lecture 7: Libraries

Lecture 8: Repos for Git integration

Lecture 9: Databricks File System (DBFS)

Lecture 10: DBUTILS

Lecture 11: Widgets

Lecture 12: Workflows

Lecture 13: Metastore – Setup external Metastore

Lecture 14: Metastore – Setup external Metastore II

Chapter 4: RDD, Database, Tables

Lecture 1: Agenda

Lecture 2: RDD Introduction

Lecture 3: Lab : Creating RDD

Lecture 4: Lab : RDD transformations

Lecture 5: Lab :RDD Actions

Lecture 6: Lab : RDD Joins

Lecture 7: Lab : Pair RDD

Lecture 8: Lab : Broadcast Variables

Lecture 9: Lab : Accumulators

Lecture 10: Lab : Convert RDD to DataFrame

Lecture 11: Lab:Import & read data

Lecture 12: Lab: Create a table using the UI

Lecture 13: Lab : Create a table in a Notebook

Chapter 5: DataFrame

Lecture 1: Agenda

Lecture 2: What is DataFrame

Lecture 3: Lab : Create DataFrames

Lecture 4: Lab : Define Schema

Lecture 5: Lab : Functions

Lecture 6: Lab : Casting Operations

Lecture 7: Lab : Filter Transformation

Lecture 8: Lab: Update, Update ALL & UpdateByName

Lecture 9: Lab : OrderBy & SortBY

Lecture 10: Lab : GroupBy

Lecture 11: Lab : Remove Duplicates

Lecture 12: Lab : Window Functions

Lecture 13: Lab : Date and Timestamp Functions

Lecture 14: Lab : UDF (User Defined Function)

Lecture 15: Lab : JOIN

Lecture 16: Lab : Handle corrupt records using the badRecordsPath

Lecture 17: Lab:File metadata column

Chapter 6: Data Source

Lecture 1: Agenda

Lecture 2: Lab : Read and Write Parquet File

Lecture 3: Lab : Read and Write CSV Files

Lecture 4: Lab : Read and Write JSON Files

Lecture 5: Lab : Read and Write XML Files

Lecture 6: Lab : Read and Write Excel file

Lecture 7: Lab : Read and Write SQL databases using JDBC

Lecture 8: Lab : Read and Write Azure blob storage

Chapter 7: Spark Structure Streaming

Lecture 1: Agenda

Lecture 2: What is Spark Structure Streaming

Lecture 3: Data Source & Sink

Lecture 4: Lab : Rate & File Source

Lecture 5: Lab : Kafka Source

Lecture 6: Lab : Sink : Console, Memory, File & Custom

Lecture 7: Lab : Build Streaming ETL

Lecture 8: Lab : Setup Event Hub

Lecture 9: Lab : Event Hub Producer

Lecture 10: Lab : Integrate Event Hubs with Data Bricks

Lecture 11: Lab : Transformation

Lecture 12: Streaming ETL 5 : Ingest into Azure Data storage

Lecture 13: Twitter Sentiment Analysis – Introduction

Lecture 14: Setup Twitter Developer Account

Lecture 15: Twitter Sentiment Analysis – II

Lecture 16: Twitter Sentiment Analysis – III

Chapter 8: DataBricks SQL

Lecture 1: Agenda

Lecture 2: Components in Databricks SQL

Lecture 3: Lab : Configuring a SQL Endpoint

Lecture 4: Lab : Creating a Table from a CSV File

Lecture 5: Lab : Create Queries

Lecture 6: Lab : Parameterized Query

Lecture 7: Lab : Query Profile

Lecture 8: Lab : Building Visualization (Table, BAR & PIE )

Lecture 9: Lab : Building Line Chart & Counter Chart

Lecture 10: Lab : Adding Charts to Dashboards

Lecture 11: Lab : Defining a Query Alert

Lecture 12: Lab : Access Control on Databricks SQL Objects

Lecture 13: Lab: Data Object Access Control

Lecture 14: Lab : Transfer Ownership

Lecture 15: Lab : Access SQL Endpoint from Python

Instructors

A. K Kumar
BigData Architect

Rating Distribution

1 stars: 5 votes

2 stars: 4 votes

3 stars: 10 votes

4 stars: 21 votes

5 stars: 32 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!