Azure Databricks Basics: Getting Started with Big Data Analy

Name: Azure Databricks Basics: Getting Started with Big Data Analy
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Azure Databricks Fundamentals: An Introduction to Big Data Processing And Understanding the Basics of Data Engineering

Created byStep2C Education

Last updated 7/2025

English

English [Auto],French [Auto],

What you'll learn

Understanding The basics Of Azure Databricks
How to create a Cluster
How to create a Azure Free Workspace
How to create a Data Lakehouse

Course content

4 sections • 27 lectures • 2h 44m total length

What is Databricks5:17
Databricks is a cloud-based lakehouse platform built on Apache Spark for processing big data, combining data lake flexibility with data warehouse ACID transactions across multi-cloud providers.
Architecture Of Databricks6:09
Explore how the lakehouse blends data lake flexibility with data warehouse structure for analytics and machine learning; Databricks uses multi-cloud control plane and data plane with Spark and Delta Lake.
Spark On Databrciks3:06
Spark on Databricks uses a distributed in-memory compute engine to process big data, enabling batch and streaming workloads, with Dbfs storage and support for Scala, Python, PySpark, Java, and R.
How To Create Azure Account3:53
Create an Azure account by signing up with a Microsoft account, validating with captcha and phone verification, entering profile and payment details, and accessing the Azure portal.
How To Create Azure Databricks Workspace4:35
Understand Databricks Workspace Environment4:49
Explore the Databricks workspace environment, organize notebooks with folders, manage data in the catalog, and leverage workflows, compute infrastructure, and SQL tools for data engineering and machine learning.
How To Create A Cluster8:11
Learn to create compute infrastructure in Databricks, selecting all‑purpose or job compute, configuring multi‑node or single‑node clusters, autoscaling, runtime versions, and idle termination settings.
Create Your Very First Note Book8:45
Create and manage a Databricks notebook, set Python as default (with SQL, Scala, or R), add and run code on a demo cluster, and use markdown and cells.
Import Note Book And Create First Data Frame5:58
Learn how to import notebooks into a Databricks workspace, define Spark schemas, create and union data frames, apply filters, and run PySpark with SQL and language magic commands.

What Is Lakehouse7:06
Explore the lakehouse concept by integrating data lake storage with data warehouse features, using delta tables and parquet formats to enable tabular analysis of structured and unstructured data.
Database vs Data Warehouse vs Data Lake vs Delta Lake vs Lakehous10:46
Compare database, data warehouse, and data lake concepts. Discover how Delta Lake enables Lake House architectures with managed and external tables.
What is Medallion Architecture?5:38
Working Of Delta Lake6:04
Explore delta lake, a lakehouse open source storage format that adds reliability to data lakes on Azure Databricks, using delta tables, parquet files, and log files for versioned, consistent reads.
Demo- Create A Delta Lake9:11
Create a Delta Lake table in a Hive metastore catalog, describe details, and insert data to generate parquet files and log files for Delta Lake storage.
Inserting Data Into Delta Lake8:55
Insert data into Delta Lake, observe data and log file growth across Parquet files, and perform updates that create new Parquet and log files while tracking changes with describe history.
Delta Lake : Additional Configurations12:38
Explore Delta Lake additional configurations by copying data from an existing table, creating new tables, using where filters, derived columns, partition by, and inspecting metadata with describe details.
Features Of Delta Lake-Time Travel3:49
Delta Lake time travel lets you access past data by timestamp or version and restore a table to a prior version, while also covering vacuum and optimization features.
Demo- Time Travel4:53
Demo- Restore Options3:28
Explore Delta Lake restore options and versioning in Azure Databricks basics. Learn to restore to a prior version using asof or timestamp, view history, and verify data after restoration.
Demo: How To Use Optimize Operations4:16
Learn how to use Delta Lake's optimize operation to reduce many small files into larger ones, preserving old files, and boost query performance with z-order by id for faster where-clauses.
Demo: Different Types Of Data Bases5:38
Discover how Hive metastore stores database metadata in Azure Databricks, with default and custom databases, and how data saves under dbfs user hive warehouse.
Demo: Different Types Of Tables10:13
Explore managed vs external tables in Databricks, where managed tables store data and metadata and external tables retain only metadata, and learn how drops affect each.
Demo: Use Of Vacuum Comment4:42
Discover how the Delta Lake vacuum command deletes seven-day-old historical files to reduce storage costs, with options to adjust retention in Databricks.

Sample Question And Answer Set -Part 14:24
Apply the Delta Lake optimize command to compact small files into larger ones, boosting query performance by reducing io operations; use z order by to optimize column-filtered queries.
Sample Questions And Answers Set - Part 23:43
Learn how the vacuum command in Delta Lake removes old data and cleans up files beyond the retention period, with a default seven-day retention and time-travel risks.
Sample Question And Answer Set -Part 33:25
Delta Lake stores data in Parquet format and uses a JSON transaction log, enabling time travel to access older versions by version as of or timestamp as of.
Sample Question And Answer Set -Part 45:13
Learn how to grant and understand delta table permissions in Azure Databricks using SQL statements like modify, select, insert, delete, alter, drop, and truncate, covering data access and schema changes.

Requirements

Internet Access
Laptop or Desktop Computer
Azure Account (to follow this lab)

Description

Unlock the Power of Big Data Processing with Azure Databricks

In today's data-driven world, organizations rely on advanced analytics and machine learning to extract valuable insights from vast amounts of data. Azure Databricks is a unified analytics platform that empowers data professionals to efficiently process, analyze, and derive actionable insights from large datasets.

In this comprehensive course, you will gain a deep understanding of Azure Databricks and its pivotal role in big data processing and analytics. We will explore the key features and benefits of Azure Databricks for data engineering, data science, and machine learning, and how it enables organizations to accelerate their data-driven initiatives.

The journey begins with creating a Community Account on Azure Databricks, where you will learn the steps to sign up for a community account and access the community edition to explore its features. Next, we will delve into creating an Azure Free Workspace in the Azure portal, covering the process of configuring workspace settings and effectively managing resources.

You will then be introduced to the concept of clusters in Azure Databricks, understanding their significance and different types, along with practical guidance on creating and configuring clusters to meet specific workload requirements.

With hands-on exercises, you will learn how to create notebooks in Azure Databricks for data exploration and analysis. We will cover the essential features of the notebook interface, empowering you to leverage its capabilities effectively.

Moving forward, we will explore the concept of a data lakehouse and its benefits, followed by step-by-step instructions on creating a data lakehouse architecture using Azure Databricks. Additionally, you will gain insights into the Medallion architecture and its layers (Bronze, Silver, Gold), and learn how to implement Medallion architecture principles in Azure Databricks for effective data management and governance.

Finally, we will uncover the workings of Delta Lake, a powerful component of Azure Databricks that ensures reliable data lakes by providing features such as ACID transactions, time travel, and schema evolution. You will understand how Delta Lake seamlessly integrates with Azure Databricks for data ingestion, transformation, and analytics, enabling you to build robust data pipelines with ease.

Whether you are a data engineer, data scientist, or business analyst, this course equips you with the knowledge and skills needed to harness the full potential of Azure Databricks for your data-driven initiatives. Join us on this journey to unlock the power of big data processing with Azure Databricks

Who this course is for:

Data Engineers: Professionals responsible for designing, building, and maintaining data pipelines and processing workflows.
Data Scientists: Individuals looking to leverage Azure Databricks for advanced analytics, machine learning, and AI projects.
Data Analysts: Professionals seeking to gain insights from large datasets and perform exploratory data analysis using Azure Databricks.
Business Intelligence Professionals: Individuals interested in using Azure Databricks to extract, transform, and visualize data for decision-making purposes.
Software Developers: Developers looking to integrate Azure Databricks into their applications for real-time analytics, batch processing, and data-driven decision-making.
Students and Aspiring Data Professionals: Students and individuals new to the field of data science and big data who want to learn about Azure Databricks and its applications in modern data processing.
IT Professionals and Architects: Professionals responsible for designing and implementing data architectures and solutions in cloud environments, including Azure.

Azure Databricks Basics: Getting Started with Big Data Analy

What you'll learn

Explore related topics

Course content

Introductions To Azure Databricks9 lectures • 51min

Databricks Lakehouse Platform14 lectures • 1hr 37min

Practice Test Questions And Answer Set4 lectures • 17min

Practice Test0

Requirements

Description

Who this course is for: