Databricks for Beginners: Data Analytics - Pro Track

Name: Databricks for Beginners: Data Analytics - Pro Track
Rating: 3.8 (4 reviews)

Practical Databricks+Delta Lake hands‑on course: ETL with PySpark,medallion pipelines,visualization,streaming & basic ML

Created byBilly Lee

Last updated 2/2026

English

What you'll learn

Build end-to-end Delta Lake pipelines (Bronze → Silver → Gold) on Databricks and persist managed Delta tables in Unity Catalog.
Implement robust PySpark ETL: safe type casting, duplicate handling, schema enforcement, and scalable transformations.
Optimize Spark jobs using partitioning, caching, join strategies and Spark UI diagnostics to reduce runtime and cost.
Create production‑ready analytics: advanced SQL (CTEs, window functions, MERGE) and reusable business views.
Produce clear visualizations and reports from Spark data using Databricks built‑in tools and Python libraries (Matplotlib/Seaborn).
Build basic Structured Streaming pipelines with Delta Lake sinks and handle late/duplicate events with watermarking and deduplication.
Apply an introductory ML workflow: feature preparation, model training/evaluation, MLflow tracking, and model persistence.

Course content

5 sections • 42 lectures • 4h 30m total length

Course Intro - Watch for this lecture2:34
About Your Instructor2:30

What is Databricks?7:08
Introduction to Databricks Free(Community) Edition for Unified Data Analytics1:24
This introductory lesson explains what Databricks is, highlights its core features (built on Apache Spark, multi-language support), and demonstrates the capabilities of Databricks Community Edition as a free learning environment for prototyping data engineering, analytics, and ML workflows. The module orients students to the workspace, clusters, and notebooks so they can confidently begin hands‑on exercises without a paid account.
What students will be able to do after this lecture :
Describe Databricks and its role as a unified analytics platform built on Apache Spark.
List supported languages (Python, Scala, SQL, R) and when to use each.
Explain the purpose and limits of Databricks Community Edition and when to use it vs. paid tiers.
Navigate the Databricks workspace: notebooks, Workspace, Data, Compute, and Catalog panels.
Create and attach a notebook to a Community Edition cluster and run basic cells.
Start a simple end‑to‑end workflow: upload a dataset, run Spark/Python/SQL commands, and view results in the notebook.
Follow best practices for a learning environment (cluster management, saving work, basic troubleshooting).
Prepare to move from exploration to EDA and subsequent lessons on data ingestion, cleaning, and visualization.
Overview of the Databricks File System (DBFS) and its features1:06
This lesson introduces DBFS (Databricks File System), the built-in distributed storage layer in Databricks. Students learn how DBFS stores uploaded files (CSV, JSON, images), how to reference files with dbfs:/ paths, and how DBFS integrates with Spark and notebooks to provide reliable, programmatic access to datasets for analysis and pipelines.
What students will be able to do after this lecture :
Explain what DBFS is and its role as the storage layer in Databricks.
Locate uploaded files in DBFS (e.g., /FileStore/tables/) via the UI and programmatically.
List directories and inspect file metadata using dbutils.fs.ls.
Read files from DBFS into Spark DataFrames (spark.read.csv / spark.read.format) and write results back to DBFS.
Use dbfs:/ and /dbfs/ path conventions appropriately in notebooks and local interactions.
Manage simple file tasks (move, copy, remove) with dbutils.fs commands.
Understand best practices for storing datasets in DBFS for reproducible notebooks and downstream jobs.
Prepare data in DBFS for subsequent EDA, visualization, and model-building steps.
Databricks Fundamentals Quiz: Lectures 01-03

How to Create A Databricks Account and Sign In (Text Guide)0:31
Getting Started with Databricks Workspace On Free Edition11:47
Navigating the Databricks Workspace: Left & Top Navigation Bar10:20
Navigating the Databricks Workspace: Right Side-Bar8:01
Your First Databricks Notebook with Python Code: A Step-by-Step Guide (Lecture)1:40
Hands-On Assignment : A Hello World & Simple Math to test the environment
Hands-On Assignment : A Step-by-Step Guide to the 'Hello World' Python Code4:58
Verifying Python Logic with a Simple Math Assignment in Databricks2:31

Overview of the Databricks Data Lifecycle Workflow (DDLW)3:23
The Databricks Data Lifecycle Workflow (DDLW)
Understanding Data Storage in Databricks: From DBFS to Volumes6:40
Data Storage & Unity Catalog Volumes
Preparing Your Sample Data for Upload (Hands-On)6:04
Preparing Sample Data for Upload
Creating a Unity Catalog Volume4:30
Creating a Unity Catalog Volume
Uploading Files to Your Unity Catalog Volume (Hands-On)4:44
Uploading Files to Your Unity Catalog Volume (Hands-On)
Verifying and Accessing Files in a Volume (Hands-On)10:37
Verifying and Accessing Files in a Volume (Hands-On)
Managing Files and Directories in Volumes (Hands-On)8:59
Managing Files and Directories in Unity Catalog Volumes
Applying Volume Data: Your First Analysis (Transition to Assignment)5:49
Applying Volume Data: Your First Analysis
Practical Data Handling & Exploration with Volumes in Databricks (PDHEV)
PDHEV - A crucial piece of starting code7:24
PDHEV - Solution for Data Inspection (Q1-Q3) : Count, Select, Filter in PySpark7:23
PDHEV - Solution for Data Transformation & File Management (Q4 - Q7)14:24
Practical Hands-On Assignment: Practical Data Handling & Exploration with Unity
Understanding the Databricks Data Lifecycle Workflow (DDLW)13:39
The Core Vocabulary of A Data Engineer (Data Analytics) working in Spark5:55

Specific datasets : Evaluation of the Kaggle URL2:39
URL: https://kaggle.com/datasets/thedevastator/netflix-imdb-scores/
Verdict: EXCELLENT CHOICE.
Here is why this specific dataset fits your "Pro Track" perfectly:

Complexity: It is better than the standard "Netflix Titles" dataset often used in beginner courses to professional track. This version includes IMDb scores and vote counts. This allows for the "Advanced SQL" and "Visualization" sections to be much richer (e.g., "What is the correlation between runtime and IMDb score?").
Data Quality Issues : This dataset likely has some messiness (null values in ratings, mixed date formats). This is perfect for Section 5 (Transformation) where you teach data cleaning.
Continuity: If you used a similar Netflix dataset in your previous "Beginner" course, this feels familiar to students but adds new dimensions (the scores/ratings) that justify the "Pro" label.
Size: It is small enough to process quickly during a live demo (no long waiting times) but large enough to look like real data.
One minor tip for the recording: The actual file inside that Kaggle download might be named NetflixTVShows.csv or similar. In your script/lab, just make sure you tell students to rename it to netflix_titles.csv locally before uploading, or adjust your code to match the downloaded filename. This prevents confusion during the "File not found" errors.
How to Download Dataset from Kaggle and creating a new account3:10
Step 1 : Download Dataset from Kaggle
1.1. Open a new tab in your web browser and navigate to Kaggle.
1.2. Search for the dataset named “Netflix IMDb Scores” (or use this URL: https://kaggle.com/datasets/thedevastator/netflix-imdb-scores/).

Note: You must register or log in to Kaggle to download datasets.
Find it on the searching bar and it is to show where Netflix movie dataset is and user can download from the Kaggle site as the capture is shown.

Step 2: Download the Dataset
2.1. Click to the right-side and Download button
2.2. To use the dataset, you must register kaggle.com
Click the “Download” button on Kaggle to save the dataset ZIP file to your local machine.
Data Acquisition & Unity Catalog Basics to perform EDA:Exploratory Data Analysis6:16
Reading Data from the Unity Catalog5:17
Data Inspection & Cleaning6:36
Data Inspection & Cleaning Code Explanation-01 COALESCE in Action Before & After1:50
Data Inspection & Cleaning Code Explanation 02-Checking for nulls in a column5:47
Data Inspection & Cleaning Code Explanation 03-Casting a column with IntegerType5:54
Rapid Data Profiling: Ensuring Data Quality Before Netflix Questions and Visual2:36
Data Profiling Limitation and Workarounds in Databricks Free Edition12:06
Exploratory Analysis (The "Netflix Questions")12:03
Hands-On Lab for Visualization & Saving Insight: Ingestion (The Bronze Layer)5:13
Hands-On Lab for Visualization & Saving Insight: Cleaning Refine (Silver Prep.)4:56
Hands-On Lab for Visualization & Saving Insight: Visualizing Insights SQL&Python8:41
Hands-On Lab for Visualization & Saving Insight: Persisting the Silver Layer Tbl4:20
Hands-on Assignment: Analyzing the Silver Transformation
Hands-On Assignment : Analyzing the Silver Transformation (Anaswer)9:21
Tip)Hands-On Lab for Visualization & Saving Results: Permanent Data Modification6:35
Data Acquisition & Reading (Lectures 01 & 02)
Data Inspection & Cleaning (Lecture 3 Series)
EDA & Visualization (Lectures 04 & 05)
Advanced Handling & Troubleshooting (L05-1, Silver Transformation, Errors)

Requirements

Basic programming familiarity (comfort with reading and editing Python code).
Fundamental SQL knowledge (SELECT, JOIN, GROUP BY).
A laptop/desktop with internet access.
A Databricks account (Community Edition or trial) — instructions provided in Section 2
Recommended but not required: basic pandas or Jupyter notebook experience for faster onboarding.
No prior Spark or Databricks experience required — this course starts with workspace setup and guides you step‑by‑step.

Description

Practical Databricks + Delta Lake hands‑on course: ETL with PySpark, medallion pipelines, visualization, streaming & basic ML

This Pro Track course is designed to teach practical, job-ready Databricks skills specifically tailored for data engineers and analytics professionals. Beginning with the essentials of workspace setup and data ingestion, you will progressively build repeatable Bronze to Silver to Gold data pipelines using Delta Lake technology. Throughout the course, you will perform robust data cleaning and complex transformations using PySpark, and apply advanced SQL patterns to support comprehensive analytics. The curriculum places a strong emphasis on defensive engineering practices, including safe type casting, handling duplicates effectively, enforcing schemas, and implementing thorough testing strategies to ensure that your data pipelines run reliably and consistently in production environments.

In addition, you will learn how to design and execute transformation workflows efficiently, schedule Databricks Jobs for automation, and optimize Spark performance by leveraging techniques such as partitioning, caching, and analyzing the Spark UI. The course also includes modules on visualization and reporting, demonstrating how to convert cleaned Spark outputs into persuasive and insightful charts using Databricks' built-in visualization tools as well as popular Python libraries like Matplotlib and Seaborn. Furthermore, the Pro Track covers fundamental concepts of structured streaming, including stateful operations, and introduces an introductory machine learning workflow utilizing MLlib and MLflow for experiment tracking and management.

Hands-on labs, real-world case studies based on datasets such as Netflix and IMDb, and downloadable notebooks provide ample opportunity to practice the entire end-to-end process. This includes ingesting raw data, cleaning and validating it, building Delta tables, creating data pipelines, visualizing insights, and deploying basic jobs. By the end of the course, you will be capable of implementing production-grade data pipelines, optimizing Spark jobs for performance, and presenting reliable, actionable analytics to stakeholders. Additionally, students who enroll during Early Access receive priority support through Q&A sessions and invitations to live interactive sessions for enhanced learning.

Explore related topics

Databricks Essentials
- Workspace navigation in depth
- Clusters vs SQL Warehouses
- Notebooks: Python vs SQL vs Scala
Spark Fundamentals
- RDDs vs DataFrames vs Datasets
- Transformations and actions
- Spark execution model (jobs, stages, tasks)
Data Engineering on Databricks
- Ingesting data (files, databases, APIs)
- Incremental loads & scheduling with Jobs
- Delta Lake basics (ACID tables, time travel)
SQL Analytics on Databricks
- Joins, window functions, aggregations
- Building dashboards in Databricks SQL
- Query optimization basics
Delta Lake & Lakehouse Concepts
- Bronze / Silver / Gold architecture
- Schema evolution & enforcement
- CDC and streaming with Delta
Data Quality & Observability
- Null/duplicate checks at scale
- Expectations (e.g., with libraries like Great Expectations)
- Monitoring data pipelines
ML & Advanced Analytics
- Using MLflow in Databricks
- Feature engineering in notebooks
- Basic clustering/regression examples
Cost & Governance
- Cluster sizing and cost control
- Access control, table permissions
- Audit/logging basics

Who this course is for:

Junior to mid‑level data engineers and analytics engineers who need to build reliable ETL pipelines and production Delta Lake workflows.
Data analysts and BI engineers who want to scale analyses from notebooks to repeatable pipelines and produce shareable visual reports.
Software engineers or backend engineers transitioning into data engineering who know basic Python/SQL and want hands‑on Spark/Databricks experience.
Team leads or technical contributors who must validate data quality, optimize Spark jobs, or implement simple streaming/ML workflows

Databricks for Beginners: Data Analytics - Pro Track

What you'll learn

Explore related topics

Course content

Course Introduction2 lectures • 5min

Introduction to Databricks & Environment Setup3 lectures • 10min

Getting Started with Databricks Workspace7 lectures • 40min

Understanding of DDLW : Data Ingestion & Management with Unity Catalog Volumes13 lectures • 1hr 40min

Exploration Data Analysis(EDA) & Basic Data Handling In Databricks Unity Catalog17 lectures • 1hr 43min

Requirements

Description

Who this course is for: