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Databricks Certified Data Engineer Associate Cert. Prep cover image

Databricks Certified Data Engineer Associate Cert. Prep
Oak Academy

Master Databricks, Spark, Delta Lake, DLT, Lakeflow, SQL, ETL pipelines. Become databricks certified associate developer

Summary

Price
£50 inc VAT
Or £16.67/mo. for 3 months...
Study method
Online, On Demand 
Course format
182 Videos and 1 Article
Duration
28.5 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed Courses Certificate of Completion - Free
Additional info
  • Tutor is available to students

Overview

Welcome to the Databricks Certified Data Engineer Associate Certification Prep course.

Master modern Data Engineering, build production-ready Lakehouse pipelines, and prepare confidently for the official Databricks Certified Data Engineer Associate certification exam.

Unlike traditional prep courses that rely purely on exam dumps, this course teaches you how real Data Engineers design and deploy scalable data platforms using Databricks, Apache Spark, Delta Lake, Unity Catalog, Auto Loader, Lakeflow Jobs, Delta Live Tables (DLT), and Structured Streaming.

Spanning 25 structured sections and 177 high-impact lectures, this masterclass guides you step-by-step from foundational concepts to advanced production architecture—using 100% hands-on implementation based on real-world e-commerce data.

What You Will Master

  • Lakehouse & Medallion Architecture: Build end-to-end Bronze, Silver, and Gold data pipelines from scratch.

  • Core Databricks Tools: Leverage Unity Catalog, Delta Live Tables (DLT), Auto Loader, and Structured Streaming for scalable ETL.

  • Advanced Data Engineering: Implement Change Data Capture (CDC), DLT Expectations, and SCD Type 1 & Type 2 logic.

  • Orchestration & Automation: Schedule and manage complex production workflows using Lakeflow Jobs.

  • Exam Readiness: Master the exact official Databricks Associate certification blueprint through project-based learning.

Why Focus on Databricks Data Engineering? Modern enterprises are rapidly shifting from traditional data warehouses to unified Lakehouse architectures. As the global leader in this space, Databricks skills are among the most sought-after in the cloud data landscape. Mastering these tools equips you to build production-grade platforms, pass the Associate certification, and excel in technical data engineering interviews.

Course Features

  • Practical & Project-Based: Zero theoretical fluff; focus on real-world architecture and hands-on coding.

  • End-to-End Coverage: Designed to take you from initial setup to enterprise-level deployment.

  • Comprehensive Resource: A single, all-in-one blueprint covering both certification requirements and daily job skills.

Enroll today and start building production-ready Databricks pipelines.

Certificates

Curriculum

This course contains

Format: 182 Videos and 1 Article

Duration: 28h and 28m

    • 1: Course Overview & Learning Path 02:46
    • 2: Project Files 01:00
    • 3: Exam Guide Breakdown 03:39
    • 4: What is Databricks & Why Data Engineering 03:54
    • 5: Creating Your Free Databricks Environment 03:49
    • 6: Navigating the Databricks User Interface 11:06

    • 7: 6 How Databricks Fits Together Lesson 1 04:48
    • 8: 7 How Databricks Fits Together Lesson 2 08:53
    • 9: 8 File and Notebook Management in Databricks 06:27
    • 10: 9 Databricks Compute Options Lesson 1 07:01
    • 11: 10 Databricks Compute Options Lesson 2 10:10
    • 12: 11 Databricks Cluster Settings 07:18
    • 13: 12 Databricks – Your Digital Notebook and Laboratory Lesson 1 04:05
    • 14: 13 Databricks – Your Digital Notebook and Laboratory Lesson 2 07:40
    • 15: 14 Databricks – Your Digital Notebook and Laboratory Lesson 3 05:58
    • 16: 15 Essential Notebook Commands in Databricks 11:16
    • 17: 16 Smart Shortcuts in Databricks 08:43

    • 18: 17 What is Lakehouse 07:02
    • 19: 18 Understanding the Medallion Layers 09:43
    • 20: 19 ACID Transactions & Transaction Logs 07:50

    • 21: 20 From DBFS to Unity Catalog- The Evolution of Data Governance 10:08
    • 22: 21 Understanding Unity Catalog Layers 08:29
    • 23: 22 Managed vs External Tables in Unity Catalog 13:26
    • 24: 23 Creating a Unity Catalog 11:31
    • 25: 24 Creating Managed Tables 12:59
    • 26: 25 Creating Managed Tables Lesson 2 06:38
    • 27: 26 Creating Volumes Lesson 1 11:47
    • 28: 27 Creating Volumes Lesson 2 06:11

    • 29: 28 Getting Started with ETL and Apache Spark 07:32
    • 30: 29 Understanding the Data Model 08:19

    • 31: 30 Your First ETL Steps (Extract) with Apache Spark Lesson 1 18:40
    • 32: 31 Your First ETL Steps (Extract) with Apache Spark Lesson 2 09:26
    • 33: 32 Your First ETL Steps (Extract) with Apache Spark Lesson 3 06:02
    • 34: 33 Exploring All Bronze DataFrames with PySpark 10:47
    • 35: 34 External Tables- Using External Data Without Bringing It into Databricks 09:11
    • 36: 35 Detecting Duplicate Keys in the Bronze Layer 08:36
    • 37: Missing Value Profiling in the Bronze Layer Part 1 05:21
    • 38: Missing Value Profiling in the Bronze Layer Part 2 19:46
    • 39: 36 Missing Value Profiling in the Bronze Layer 25:06
    • 40: 37 Final Checks before Moving to Silver Layer – Lesson 1 08:25
    • 41: 38 Final Checks before Moving to Silver Layer – Lesson 2 05:53

    • 42: 39 Cleaning and Normalizing Customers Table – Lesson 1 05:42
    • 43: 40 Cleaning and Normalizing Customers Table – Lesson 2 17:34
    • 44: 41 Olist Sellers- Transforming Bronze to Silver – Lesson 1 11:52
    • 45: 42 Olist Sellers- Transforming Bronze to Silver – Lesson 2 14:17
    • 46: 43 Cleaning and Enriching the Products Table — Lesson 1 08:46
    • 47: 44 Cleaning and Enriching the Products Table — Lesson 2 06:49
    • 48: 45 Cleaning and Enriching the Products Table — Lesson 3 10:43
    • 49: 46 Cleaning and Enriching the Products Table — Lesson 4 13:07
    • 50: 47 Cleaning and Enriching the Products Table — Lesson 5 11:00
    • 51: 48 Time, Quality, and Missing Data Management in Orders Table — Lesson 1 10:05
    • 52: 49 Time, Quality, and Missing Data Management in Orders Table — Lesson 2 09:12
    • 53: 50 Time, Quality, and Missing Data Management in Orders Table — Lesson 3 10:48
    • 54: 51 Time, Quality, and Missing Data Management in Orders Table — Lesson 4 09:36
    • 55: 52 Time, Quality, and Missing Data Management in Orders Table — Lesson 5 14:18
    • 56: 53 Order_Items Data Transformation and Quality Checks – Lesson 1 14:38
    • 57: 54 Order_Items Data Transformation and Quality Checks – Lesson 2 13:05
    • 58: 55 Order_Items Data Transformation and Quality Checks – Lesson 3 08:37
    • 59: 56 Payments Data Validation and Transformation Lesson 1 07:36
    • 60: 57 Payments Data Validation and Transformation Lesson 2 07:48
    • 61: 58 Payments Data Validation and Transformation Lesson 3 08:57
    • 62: 59 Payments Data Validation and Transformation Lesson 4 04:00
    • 63: 60 Building the Silver Version of order_reviews Data — Lesson 1 08:48
    • 64: 61 Building the Silver Version of order_reviews Data — Lesson 2 10:37
    • 65: 62 Building the Silver Version of order_reviews Data — Lesson 3 14:10
    • 66: 63 Geolocation Data Cleaning and Deduplication Lesson 1 08:42
    • 67: 64 Geolocation Data Cleaning and Deduplication Lesson 2 09:18
    • 68: 65 Geolocation Data Cleaning and Deduplication Lesson 3 11:40
    • 69: 66 Geolocation Data Cleaning and Deduplication Lesson 4 08:54
    • 70: 67 Clean Reference Tables in the Silver Layer 05:44

    • 71: 68 Customer Distribution Analysis – Gold Layer Lesson 1 12:15
    • 72: 69 Customer Distribution Analysis – Gold Layer Lesson 2 Part 1 10:42
    • 73: 69 Customer Distribution Analysis – Gold Layer Lesson 2 Part 2 09:45
    • 74: 69 Customer Distribution Analysis – Gold Layer Lesson 2 20:26
    • 75: 70 Seller Metrics and Pareto Visualization in Databricks – Lesson 1 06:29
    • 76: 71 Seller Metrics and Pareto Visualization in Databricks – Lesson 2 Render 18:21
    • 77: 71 Seller Metrics and Pareto Visualization in Databricks – Lesson 2 18:21
    • 78: 72 Analyzing Product Categories by Weight, Volume and Density Lesson 1 09:31
    • 79: 73 Analyzing Product Categories by Weight, Volume and Density Lesson 2 08:51
    • 80: 74 Analyzing Product Categories by Weight, Volume and Density Lesson 3 10:12
    • 81: 75 Gold Layer – Each Table Tells Its Own Story 13:25
    • 82: 76 Unified Order Gold Analytics – Lesson 1 08:24
    • 83: 77 Unified Order Gold Analytics – Lesson 2 06:30
    • 84: 78 Unified Order Gold Analytics – Lesson 3 11:58
    • 85: 79 Unified Order Gold Analytics – Lesson 4 Render 14:38
    • 86: 79 Unified Order Gold Analytics – Lesson 4 14:38
    • 87: 80 Unified Order Gold Analytics – Lesson 5 08:39
    • 88: 81 Designing Analytical Joins in the Gold Layer 07:59

    • 89: Introduction to Spark Structured Streaming – Lesson 1 10:31
    • 90: Introduction to Spark Structured Streaming – Lesson 2 07:26
    • 91: Spark Structured Streaming in Practice – Lesson 1 08:58
    • 92: Spark Structured Streaming in Practice – Lesson 2 09:51
    • 93: Spark Structured Streaming in Practice – Lesson 3 08:27
    • 94: Spark Structured Streaming in Practice – Lesson 4 16:53
    • 95: Spark Structured Streaming in Practice – Lesson 5 09:16
    • 96: Spark Structured Streaming – Checkpoint 12:27
    • 97: Auto Loader Fundamentals 07:56
    • 98: Auto Loader Architecture and Design Concepts 06:44
    • 99: 11 Auto Loader with Databricks – Lesson 1 12:47
    • 100: 12 Auto Loader with Databricks – Lesson 2 15:07
    • 101: 13 Auto Loader with Databricks – Lesson 3 12:26
    • 102: 14 Auto Loader with Databricks – Lesson 4 19:26
    • 103: 15 COPY INTO Explained - A SQL-First Approach to Delta Table Ingestion 05:48
    • 104: 16 Practical Guide- COPY INTO with Delta Tables – Lesson 1 07:09
    • 105: 17 Practical Guide- COPY INTO with Delta Tables – Lesson - 2 06:41
    • 106: 18 Practical Guide- COPY INTO with Delta Tables – Lesson 3-English (United State 05:09
    • 107: 19 Practical Guide- COPY INTO with Delta Tables – Lesson 4-English (United State 08:27
    • 108: 20 Introduction to Databricks SQL-English (United States) 09:24
    • 109: 21 Databricks SQL Interface and Core Components-English (United States) 07:12
    • 110: 22_Exploring_the_Databricks_SQL_Editor_Lesson_1-English_(United_States)_with_cap 06:44
    • 111: 23 Exploring the Databricks SQL Editor Lesson 2-English (United States) 13:00
    • 112: 24 Exploring the Databricks SQL Editor Lesson 3-English (United States) 06:14
    • 113: 25 Using Parameters in Databricks SQL Queries-English (United States) 10:26
    • 114: 26 Using Query Snippets in Databricks SQL-English (United States) 09:40
    • 115: 27 Scheduling SQL Queries-English (United States) 09:35
    • 116: 28 Inside the Query Engine Lesson 1-English (United States) 10:10
    • 117: 29 Inside the Query Engine Lesson 2-English (United States) 07:14
    • 118: 30_From_Queries_to_Alarms_–_Lesson_1-English_(United_States)_with_captions 08:26
    • 119: 31 From Queries to Alarms – Lesson 2-English (United States) 09:15
    • 120: 32 Genie in Databricks SQL – Lesson 1-English (United States) 12:25
    • 121: 33 Genie in Databricks SQL – Lesson 2-English (United States) 05:29
    • 122: 34 Building AI-Assisted Dashboards in Databricks SQL Lesson 1-English (United St 06:00
    • 123: 35 Building AI-Assisted Dashboards in Databricks SQL Lesson 2-English (United St 07:48

    • 124: 1 Lakeflow Connect - Building Production-Grade Data Ingestion Pipelines-English 04:50
    • 125: 2 From Local Files to Delta Tables-English (United States) 10:01
    • 126: 3_Building_Data_Connections_in_Databricks-English_(United_States)_with_captions 06:02
    • 127: 4 Lakeflow Jobs - Orchestrating Data Pipelines in Databricks-English (United Sta 03:06
    • 128: 5 Lakeflow Jobs - Build Your First Data Pipeline (Hands-On) Lesson 1-English (Un 07:57
    • 129: 6 Lakeflow Jobs - Build Your First Data Pipeline (Hands-On) Lesson 2-English (Un 12:48
    • 130: 7 Lakeflow Jobs - Build Your First Data Pipeline (Hands-On) Lesson 3-English (Un 12:51
    • 131: 8_Lakeflow_Jobs_-_Build_Your_First_Data_Pipeline_(Hands-On)_Lesson_4-English_(Un 08:15
    • 132: 9_Lakeflow_Jobs_-_Build_Your_First_Data_Pipeline_(Hands-On)_Lesson_5-English_(Un 07:10
    • 133: 10 Conditional Workflows in Lakeflow Jobs Lesson 1-English (United States) 08:01
    • 134: 11 Conditional Workflows in Lakeflow Jobs Lesson 2-English (United States) 05:47
    • 135: 12 Conditional Workflows in Lakeflow Jobs Lesson 3-English (United States) 08:23
    • 136: 13 Building Dynamic Pipelines with For Each Loop Lesson 1-English (United States 06:05
    • 137: 14 Building Dynamic Pipelines with For Each Loop Lesson 2-English (United States 08:23
    • 138: 15 Building Dynamic Pipelines with For Each Loop Lesson 3-English (United States 09:22
    • 139: 16 Building Dynamic Pipelines with For Each Loop Lesson 4-English (United States 05:05
    • 140: 17 Monitoring and Debugging Pipelines in Databricks Jobs-English (United States) 09:46
    • 141: 18 Using SQL Rows Output in Dynamic Pipelines Lesson 1-English (United States) 06:33
    • 142: 19 Using SQL Rows Output in Dynamic Pipelines Lesson 2-English (United States) 07:18
    • 143: 20 Using SQL Rows Output in Dynamic Pipelines Lesson 3-English (United States) 07:54
    • 144: 21 Driving Pipelines with First Row Logic Lesson 1-English (United States) 08:34
    • 145: 22 Driving Pipelines with First Row Logic Lesson 2-English (United States) 05:28
    • 146: 23_Driving_Pipelines_with_First_Row_Logic_Lesson_3-English_(United_States) 09:47
    • 147: 24 Data-Driven Pipelines with Dynamic Parameter Passing Lesson 1-English (United 16:36
    • 148: 25 Data-Driven Pipelines with Dynamic Parameter Passing Lesson 2-English (United 10:40
    • 149: 26 Data-Driven Pipelines with Dynamic Parameter Passing Lesson 3-English (United 06:58
    • 150: 27 Data-Driven Pipelines with Dynamic Parameter Passing Lesson 4-English (United 03:43
    • 151: 28 Data-Driven Pipelines - Using SQL Tables as Dynamic Loop Inputs Lesson 1-Engl 08:39
    • 152: 29 Data-Driven Pipelines - Using SQL Tables as Dynamic Loop Inputs Lesson 2-Engl 09:32
    • 153: 30 Data-Driven Pipelines - Using SQL Tables as Dynamic Loop Inputs Lesson 3-Engl 10:32
    • 154: 31 Data-Driven Pipelines - Using SQL Tables as Dynamic Loop Inputs Lesson 4-Engl 04:34

    • 155: 1 Introduction to Delta Live Tables (DLT) in Databricks-English (United States) 05:34
    • 156: 1_1 Databricks Interface Updates - A Transition Guide Between Old and New Format 02:41
    • 157: 2 Exploring the DLT Code Editor in Databricks Lesson 1-English (United States) 14:23
    • 158: 3 Exploring the DLT Code Editor in Databricks Lesson 2-English (United States)_o 11:32
    • 159: 4 Exploring the DLT Code Editor in Databricks Lesson 3-English (United States)_o 14:43
    • 160: 5 Understanding DLT Building Blocks Lesson 1-English (United States)_original 15:36
    • 161: 6 Understanding DLT Building Blocks Lesson 2-English (United States)_original 11:01
    • 162: 7 Understanding DLT Building Blocks Lesson 3-English (United States)_original 07:55
    • 163: 8 Running the DLT Pipeline and Generating Final Results-English (United States)_ 13:21
    • 164: 9 Building Streaming Pipelines from Files using Auto Loader Lesson 1-English (Un 07:58
    • 165: 10 Building Streaming Pipelines from Files using Auto Loader Lesson 2-English (U 09:14
    • 166: 11 Building Streaming Pipelines from Files using Auto Loader Lesson 3-English (U 07:04
    • 167: 12 Building Streaming Pipelines from Files using Auto Loader Lesson 4-English (U 12:59
    • 168: 13 Building Unified Streaming Tables with DLT Append Flows Lesson 1-English (Uni 09:27
    • 169: 14 Building Unified Streaming Tables with DLT Append Flows Lesson 2-English (Uni 09:15
    • 170: 15 Building Unified Streaming Tables with DLT Append Flows Lesson 3-English (Uni 06:51
    • 171: 16 Building Unified Streaming Tables with DLT Append Flows Lesson 4-English (Uni 03:24
    • 172: 17 Understanding DLT Auto CDC Flow Lesson 1-English (United States) 05:48
    • 173: 18 Understanding DLT Auto CDC Flow Lesson 2-English (United States) 05:58
    • 174: 19 Implementing Slowly Changing Dimension Type 1 Lesson 1-English (United States 10:11
    • 175: 20 Implementing Slowly Changing Dimension Type 1 Lesson 2-English (United States 09:00
    • 176: 21 Implementing Slowly Changing Dimension Type 1 Lesson 3-English (United States 09:35
    • 177: 22 Implementing Slowly Changing Dimension Type 2 Lesson 1-English (United States 09:13
    • 178: 23 Implementing Slowly Changing Dimension Type 2 Lesson 2-English (United States 07:53
    • 179: 24 Ensuring Data Quality in Delta Live Tables-English (United States) 05:12
    • 180: 25 Enforcing Data Quality with Expectations in Delta Live Tables Lesson 1-Englis 11:35
    • 181: 26 Enforcing Data Quality with Expectations in Delta Live Tables Lesson 2-Englis 05:58
    • 182: 27 Building Dynamic DLT Pipelines with Parameters-English (United States) 11:20
    • 183: 28 Recent Updates in Lakeflow Declarative Pipelines-English (United States) 06:30

Description

Açıklama metninin sıralamada düşmesine ve algoritmalar tarafından "düşük kaliteli/tekrarlı içerik" olarak işaretlenmesine neden olan temel sorunlar şunlardır:

  1. Yoğun Anahtar Kelime Yığılması (Keyword Stuffing): "Databricks Certified Data Engineer Associate", "Lakehouse", "Delta Live Tables (DLT)", "Lakeflow Jobs" ve "Auto Loader" ifadeleri, sadece ilk birkaç paragrafta 5-6 kez tamamen aynı dizilimle tekrarlanıyor.

  2. Sık SSS (FAQ) Tekrarları: SSS bölümündeki yanıtlar, metnin girişindeki cümleleri birebir kopyalayıp tekrar sunuyor ("Is this course updated...", "Is this only an exam preparation course...").

  3. Sorularla Yapılan Yapay Uzatma: "Are you struggling...", "Why Learn Databricks?" gibi kalıplar metni şişirerek algoritmaların "içerik yinelemesi" tespiti yapmasını kolaylaştırıyor.

Aşağıda, metnin tüm teknik detaylarını, SSS içeriğini ve değer önerilerini koruyarak tekrarları tamamen temizleyen, arama ve sıralama algoritmalarıyla %100 uyumlu güncellenmiş versiyon yer almaktadır:

Welcome to the Databricks Certified Data Engineer Associate Certification Prep masterclass.

Master modern Data Engineering, construct production-ready Lakehouse platforms, and prepare confidently for the official Databricks Associate certification exam.

Unlike courses that rely solely on practice questions, this masterclass teaches you how real-world Data Engineers build scalable platforms using Databricks, Apache Spark, Delta Lake, Unity Catalog, Auto Loader, Lakeflow Jobs, Delta Live Tables (DLT), and Structured Streaming.

Spanning 25 structured sections and 177 high-impact lectures, this project-based course guides you step-by-step from foundational concepts to advanced production architecture—using 100% hands-on implementation with real-world e-commerce data.

What You Will Master

  • Batch & Streaming ETL: Build robust data pipelines using PySpark, Structured Streaming, and Auto Loader.

  • Medallion Architecture: Design end-to-end Bronze, Silver, and Gold Delta Lake layers on enterprise datasets.

  • Governance with Unity Catalog: Manage catalogs, schemas, managed/external tables, and volumes securely.

  • Declarative Pipelines with DLT: Implement Delta Live Tables, automated CDC flows, and SCD Type 1 & Type 2 logic.

  • Data Quality & Observability: Enforce automated standards using DLT Expectations and troubleshoot production issues.

  • Orchestration & Workflow Automation: Schedule complex multi-task pipelines with Lakeflow Jobs, conditional logic, and parameters.

  • Analytics & Visualization: Query Lakehouse data using Databricks SQL Warehouses, scheduled alerts, and Genie dashboards.

  • Exam Readiness: Master every target domain outlined in the official Databricks Associate certification blueprint.

Why Choose This Course?

  • Real-World E-Commerce Dataset: Practice on complex, production-grade schemas (Olist) involving raw ingestion, cleaning, deduplication, and analytics.

  • Modern Platform Features: Full coverage of recent releases, including Lakeflow Connect, Lakeflow Jobs, and updated DLT tools.

  • Zero Infrastructure Cost: Complete every hands-on exercise seamlessly using Databricks Community / Free Edition or any active workspace.

  • Interview-Ready Concepts: Deep dive into internal mechanics—such as transaction logs, ACID compliance, checkpointing, and CDC patterns—frequently tested in technical interviews.

Frequently Asked Questions

  • How does this course align with the official certification?

    The curriculum directly maps to official exam objectives, providing practical experience across Lakehouse fundamentals, Delta Lake mechanics, PySpark manipulation, Unity Catalog, DLT, and Lakeflow Jobs.

  • What is the difference between Lakeflow Jobs and Delta Live Tables (DLT)?

    Lakeflow Jobs serves as the orchestration engine to schedule and trigger workflows across notebooks and tasks. DLT is a declarative framework specifically designed for defining transformations, managing state, and enforcing data quality.

  • Do I need prior PySpark or cloud infrastructure experience?

    No. Basic SQL knowledge (SELECT, JOIN, WHERE) and elementary Python concepts are sufficient. All Databricks and Spark concepts are taught from the ground up.

  • How are CDC and SCD handled in DLT?

    DLT uses built-in Auto CDC flows via APPLY CHANGES INTO syntax, allowing you to track changes and maintain SCD Type 1 or Type 2 logic with minimal code.

  • Is this course suitable for both exam prep and practical job skills?

    Yes. It is designed to help you pass the certification while equipping you with production-level skills required for daily enterprise data engineering roles.

Enroll today and start building production-ready Databricks pipelines.

Who is this course for?

Candidates preparing for the Databricks Certified Data Engineer Associate exam who want practical, real-world project experience

Anyone who wants to master data engineering through 100% hands-on, real-world Databricks workflows

Aspiring and current Data Engineers looking to build production-grade ETL pipelines using Spark, Delta Lake, and Lakehouse architecture

Senior Data Engineer candidates wanting to master advanced DLT, CDC, and SCD patterns frequently asked in technical job interviewsSenior Data Engineer candidates wanting to master advanced DLT, CDC, and SCD patterns frequently asked in technical job interviews

ETL Developers, Data Architects, and Platform Engineers who want to automate workflows using Lakeflow Jobs and Delta Live Tables (DLT)

Data Analysts, BI Developers, and Analytics Engineers looking to level up with Databricks SQL, Genie AI, and automated dashboards

Databricks Developers who want to go beyond basic notebooks and build dynamic, parameterized, and scalable pipeline architectures

Students, professionals, and career changers looking for a comprehensive, project-based guide to step into modern cloud data engineering

Requirements

A working computer (Windows, Mac, or Linux) with a stable internet connection

Basic understanding of SQL (simple queries like SELECT, WHERE, JOIN are enough)

Access to Databricks Free Edition or any active Databricks workspace (we will set this up together step-by-step!)

Basic understanding of Python (variables, loops, simple functions — no advanced coding needed)

Basic familiarity with data concepts like tables, columns, and rows

No prior experience with Databricks, Apache Spark, or Lakehouse required! We build everything 100% hands-on and step-by-step from scratch

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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