UDEMY.Master.Databricks.Certification.Data.Engg.Professional.2026.BOOKWARE-MiMiR

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MiMiR

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2026-10-10

NFO

Mimir, Keeper of the Well of Wisdom
  Master Databricks (Certification - Data Engg. Professional)
  https://www.udemy.com/course/master-databricks-certification-
  data-engg-professional/
  Year         : 2026
  Language     : English
  Level        : All Levels
  Category     : IT & Software
  Subcategory  : IT Certifications
  Duration     : 32h 19m
  Lectures     : 243
  Rating       : 5.0/5 (15 reviews)
  Students     : 151
  INSTRUCTOR(S)
  HEADLINE
    Learn Databricks Data Platform - End to End with projects
  WHAT YOU'LL LEARN
  * Databricks Data Intelligence Platform
  * Lakehouse architecture using Databricks Delta Tables
  * Spark Execution on Databricks
  * Databricks SQL
  * Medallion Architecture on Databricks
  * Databricks on Azure & AWS
  * Implement Data Engineering Projects using Databricks
    Platform
  REQUIREMENTS
  * PySpark
  * Python
  * SQL
  * Azure Cloud
  WHO IS THIS COURSE FOR
  * Data Engineers, Data Architects, Data Analyst, Database
    Administrators
  * Solution Architects, Technical Manager
  * Project Manager, Platform Manager
  DESCRIPTION
  In this course, I will be explaining to most famous data
  platform - Databricks. The reach of this data platform in
    beyond
  comprehension. Any organization that deals with data, thinks
    of
  Databricks as the first technology tool to work with that
    data.
  It is imperative that any person who wants to pursue Data
  Engineering or Data Architect as a career choice will come
  across the backbones of it - Spark & Databricks. Following are
  the high level topics: Unity Catalog Delta Tables Medallion
  Architecture using Databricks Batch & Stream Processing using
  Spark on Databricks Databricks Notebook & Job Pipeline
  Orchestration using Lakeflow Data Sharing Data Warehousing on
  Databricks Lakebase Git & CI/CD on Databricks Data Governance
  Databricks SQL & CLI Compute on Databricks Hands-On practice
    is
  divided into 2 parts: Hands-On Lab & Demo using Finance
    dataset
  Exercise - Build Customer 360 Application using
  CRM & ERP dataset. Note: Building customer 360 application for
  analytics from ERP and CRM data is relatively very complex.
    The
  purpose of the exercise is to ingest data, create bronze,
    silver
  & gold layer. Gold layer processing would depend on the
  dimensional schema that would be provided in the exercise
  itself. To perform complete coding exercise for Customer 360
  will take anywhere from 16 - 24 hrs minimum. The dataset is
  divided into 2 parts - Set 1 & Set 2. Set 2 is much smaller
    than
  Set 1. Use Set 2 to perform development and testing exercise
    and
  build the pipeline using Set 1, post which performance tuning
  should be taken up based on students discretion. Google drive
  link of project datasets will be provided on-request. For
  personal plan or subscription based enrollments, it will be
  shared over a period of time. Hands-on Finance dataset will be
  provided to all candidates post enrollment itself. After
  completing this course you should be able to clear the
    following
  certification Databricks Certified Data Engineer Professional
  Databricks Certified Data Engineer Associate
  COURSE CONTENT
  Chapter 1: CHAPTER 1 : Course Introduction
    1. 1.1 Course & Data Platform Introduction
    2. 1.2 Course TOPICS
    3. 1.3 Course LABs & Membership Program
    4. 1.4 About Me
  Chapter 2: COURSE RESOURCES
    5. Downloadable resources
  Chapter 3: CHAPTER 2 : Introduction to Databricks
    6. 2.1 Intro to Data Workflow
    7. 2.2 Databricks Data Intelligence Platform
    8. 2.3 Sign-up for Databricks
    9. 2.4 Pre-Requisites of the Course
  Chapter 4: CHAPTER 3 : Databricks on Azure & AWS
    10. 3.1 Databricks and Azure
    11. 3.4 Datasets for Hands-On and Exercises
    12. 3.2 HANDS-ON - Create Databricks Account & Workspace on
        Azure
    13. 3.5 Recycle Workspace for Free Tier Access
  Chapter 5: CHAPTER 4 : Data & Metadata in Databricks
    14. 4.2 DBx Objects, Unity Catalog
    15. 4.3 HANDS-ON - Create Unity Catalog
    16. 4.1 Setup Databricks Tools
    17. 4.4 CertBytes - Metastore
    18. 4.6 HANDS-ON - Create Catalogs
    19. 4.7 CertBytes - Catalogs
    20. 4.5 Catalogs
    21. 4.8 Schemas
    22. 4.9 HANDS-ON - Create Schema
    23. 4.10 CertBytes - Schema
    24. 4.11 Volumes
    25. 4.12 HANDS-ON - Create Volumes
    26. 4.13 CertBytes - Volumes
    27. 4.14 Tables & Views
    28. 4.15 Databricks CLI Authentication
    29. 4.16 HANDS-ON - Databricks CLI Authentication
    30. 4.17 HANDS-ON - CertBytes
    31. 4.18 Intro to DBx Notebooks
    32. 4.19 Explore Notebooks
    33. 4.20 DBx Utilities - dbutils
    34. 4.21 HANDS-ON - dbutils File System Commands
    35. 4.22 HANDS-ON - dbutils Secrets Commands
    36. 4.23 Object Permissions, Summary
  Chapter 6: CHAPTER 5 : Data Lifecycle Concepts
    37. 5.1 Sources in Data Lifecycle
    38. 5.2 OLTP & OLAP
    39. 5.3 Lakehouse & Delta Table
    40. 5.4 Delta Lake Implementation
    41. 5.5 Medallion Architecture in DBx
  Chapter 7: CHAPTER 6A : Databricks Compute
    42. 6.1 DBx Serverless Compute
    43. 6.2 HANDS-ON - Create Serverless Compute
    44. 6.3 DBx Classic Compute
    45. 6.4 HANDS-ON - Create Classic Compute
    46. 6.5 DBx SQL Warehouse Compute
    47. 6.6 DBx Instance Pool
    48. 6.7 DBx Photon Execution Engine
    49. 6.8 DBx Lakeguard - User Isolation
    50. 6.9 Compute Type Selection
    51. 6.10 Workspace Compute Settings
  Chapter 8: CHAPTER 6B : Ingest & Load Data into Databricks
    52. 6.11 HANDS-ON - Create All DBx Objects
    53. 6.13 ADLS Storage Connector
    54. 6.14 Cloud Service Connector
    55. 6.15 HANDS-ON - Create OLTP Postgres Source
    56. 6.16 HANDS-ON - Create Databricks Connection
    57. 6.12 Databricks Connectors
    58. 6.17 Lakehouse Query Federation
    59. 6.18 HANDS-ON - Lakehouse Federation
    60. 6.19 remote_query Function, Catalog Federation
    61. 6.20 Lakeflow, Slowly Changing Dimensions (SCD)
    62. 6.21 Lakeflow Connect
    63. 6.22 Auto Loader
    64. 6.24 HANDS-ON - CloudFiles Configuration
    65. 6.25 Auto Loader Schema Evolutions
    66. 6.27 COPY INTO
    67. 6.28 HANDS-ON - COPY INTO
    68. 6.23 HANDS-ON - Auto Loader
    69. 6.29 Database CDC Connector, Query Based Connector
    70. 6.30 HANDS-ON - Configure Database CDC Connector
    71. 6.31 Summary - Ingest & Load Data
  Chapter 9: EXERCISE : Customer 360 (CRM & ERP) Use Case
    Explanation
    72. E1. CRM & ERP Introduction
    73. E2. Business Flow & Entity Relation
    74. E3. Ingest & Load
  Chapter 10: CHAPTER 7 : Data Processing - Delta Tables
    75. 7.1 Data Processing Agenda
    76. 7.2 Managed Tables
    77. 7.3 HANDS-ON - Managed Table & ACID Transaction
    78. 7.4 Deletion Vector Files
    79. 7.5 HANDS-ON - Deletion Vector & REORG Command
    80. 7.6 Time Travel
    81. 7.7 HANDS-ON - Time Travel, Log Retention, File
        Retention
    82. 7.8 Table Maintenance - VACUUM, OPTIMIZE, ANALYZE
    83. 7.9 HANDS-ON - Table Maintenance Operations
    84. 7.10 Auto Table Maintenance
    85. 7.11 Enable & Disable Predictive Optimization
    86. 7.12 Table Partitions
    87. 7.13 HANDS-ON - Create & Load Partitioned Tables
    88. 7.14 Change Data Feed
    89. 7.15 HANDS-ON - Change Data Feed
    90. 7.16 Table Cloning, External Foreign & Temporary Tables
    91. 7.17 Table Type Comparison
    92. 7.18 Schema Enforcement, Table Constraints & Summary
    93. 7.19 Dynamic File Pruning & Data Skipping
    94. 7.20 Disk Caching
    95. 7.21 Predictive IO, Archival, Auto-TTL
    96. 7.22 _metadata Column
    97. 7.23 Isolation (Lock) Levels
    98. 7.24 HANS-ON - Serializable Isolation Level
  Chapter 11: CHAPTER 8 : Data Processing - Spark on Databricks
    99. 8.1 RECAP - Apache Spark
    100. 8.2 RECAP - Spark Partition & Task
    101. 8.3 Spark on Databricks
    102. 8.4 HANDS-ON - "spark.catalog" Command
    103. 8.5 Handling NULL Values in Spark
    104. 8.6 Silver Layer Processing
    105. 8.7 HANDS-ON - Silver Layer Processing Use Case
    106. 8.8 HANDS-ON - Silver Process 1 - Set Up
    107. 8.9 HANDS-ON - Silver Process 2  - Cleanse, Curate &
         Enrich
    108. 8.10 Create Classic Compute Cluster
    109. 8.11 Spark & DBx Notebook Execution
    110. 8.12 HANDS-ON - Spark UI on Classic Compute
    111. 8.13 Serverless Execution Architecture
    112. 8.14 HANDS-ON - Spark UI on Serverless
    113. 8.15 Gold Layer Processing
    114. 8.16 HANDS-ON - Gold Processing Use Case
    115. 8.17 HANDS-ON - Gold Processing & Spark UI on Classic
         Compute
    116. 8.18 HANDS-ON - Gold Processing & Spark UI on
         Serverless
    117. 8.19 PRACTICE - Gold Processing
    118. 8.20 User Defined Function (UDF)
    119. 8.21 HANDS-ON - UDF Scalar & Table Functions
  Chapter 12: EXERCISE : Customer 360 (CRM & ERP) - Bronze,
    Silver & Gold Layer Processing
    120. E4. Bronze Setup & Processing
    121. E5. Silver Layer Processing
    122. E6. Gold Layer Processing
  Chapter 13: CHAPTER 9 : SQL Warehouse (OLAP) & Lakebase (OLTP)
    123. 9.1 Data Warehouse
    124. 9.2 HANDS-ON - DBx Warehouse Console
    125. 9.3 HANDS-ON - SQL Warehouse & Editor
    126. 9.4 Lakebase OLTP Database Introduction
    127. 9.5 Lakebase Objects & Console
    128. 9.6 Lakebase Branches
    129. 9.7 HANDS-ON - Explore Project & Branch in Lakebase
         Console
    130. 9.8 HANDS-ON - Create Branch & Connect to it
    131. 9.9 HANDS-ON - DB Operations in Lakebase
    132. 9.10 Lakehouse Integration
  Chapter 14: CHAPTER 10A : Pipeline Orchestration - Lakeflow
    Jobs
    133. 10.a.1 Introduction to Lakeflow Jobs
    134. 10.a.2 Lakeflow Tasks
    135. 10.a.3 HANDS-ON - Task Execution
    136. 10.a.4 Task Parameters & Values
    137. 10.a.5 HANDS-ON - Use Case & Execution Explanation
    138. 10.a.6 HANDS-ON - Convert Notebooks to Tasks
    139. 10.a.7 HANDS-ON - Task Parameters & Values
    140. 10.a.8 HANDS-ON - Pass Parm between Python & SQL
    141. 10.a.9 "If else" Task
    142. 10.a.10 HANDS-ON - "If else" Task Use Case
    143. 10.a.11 HANDS-ON - "If else" Task
    144. 10.a.12 "For each" Task
    145. 10.a.13 HANDS-ON - "For each" Task
    146. 10.a.14 "Run if" Task
    147. 10.a.15 "Disable" Task
    148. 10.a.16 Other Tasks
    149. 10.a.17 Job Triggers
    150. 10.a.18 HANDS-ON - "Add File" Job Trigger
    151. 10.a.19 HANDS-ON - "Table Update" Job Trigger
    152. 10.a.20 Backfill Jobs
    153. 10.a.21 Database CDC using Lakeflow Jobs
    154. 10.a.22 Job Privileges & Git Integration
  Chapter 15: CHAPTER 10B : Pipeline Orchestration - Spark
    Declarative Pipeline
    155. 10.b.1 Python Decorators & Pipeline Editor IDE
    156. 10.b.2 Spark Declarative Pipeline Introduction
    157. 10.b.3 Materialized View Target
    158. 10.b.4 HANDS-ON - Create Materialized View
    159. 10.b.5 Sink Target
    160. 10.b.6 HANDS-ON - Create Sink
    161. 10.b.7 Streaming Table Target
    162. 10.b.8 HANDS-ON - Create Streaming Tables
    163. 10.b.9 Append Flow
    164. 10.b.10 HANDS-ON - Append Flow Part 1
    165. 10.b.11 HANDS-ON - Append Flow Part 2
    166. 10.b.12 Auto CDC Flow
    167. 10.b.13 PRACTICE - Auto CDC Flow
    168. 10.b.14 Flows & Targets
    169. 10.b.15 Update Flow, Temp View & Data Quality
    170. 10.b.16 HANDS-ON - Medallion Architecture using SDP
    171. 10.b.17 HANDS-ON - Bronze Targets & Flows
    172. 10.b.18 HANDS-ON - Create Postgres CDC Source
    173. 10.b.19 HANDS-ON - Create Pipeline to Ingest CDC Data
    174. 10.b.20 HANDS-ON - Auto CDC Flow
    175. 10.b.21 HANDS-ON - Silver Targets & Flows
    176. 10.b.22 HANDS-ON - Gold Targets & Flows
    177. 10.b.23 HANDS-ON - SDP Monitoring
    178. 10.b.24 Modifying SDPs
    179. 10.b.25 Pipeline Configuration
    180. 10.b.26 HANDS-ON - Pipeline Configuration
    181. 10.b.27 Lakeflow Jobs Vs Spark Declarative Pipeline
    182. 10.b.28 SDP using SQL
  Chapter 16: CHAPTER 10C : Lakeflow Designer & Data Sharing
    183. 10.c.1 Lakeflow Designer Intro
    184. 10.c.2 HANDS-ON - Lakeflow Designer
    185. 10.c.3 Data Sharing
    186. 10.c.4 HANDS-ON - Data Sharing
    187. 10.c.5 DBx Marketplace Sharing
    188. 10.c.6 Clean Rooms
    189. 10.c.7 SUMMARY
  Chapter 17: EXERCISE : Customer 360 - SQL Warehouse & Pipeline
    Orchestration
    190. E7. Handle Business Queries using SQL Warehouse
    191. E8. Data Pipeline Orchestration
  Chapter 18: CHAPTER 11 : Stream Processing in Databricks
    192. 11.1 Lambda & Kappa Architecture
    193. 11.2 RECAP - Streaming Terminologies
    194. 11.3 RECAP - Streaming Output Modes
    195. 11.4 RECAP - Streaming State Store
    196. 11.5 RECAP - Event Time, Processing Time & Watermark
    197. 11.6 Streaming in DBx
    198. 11.7 Azure EventHub
    199. 11.8 HANDS-ON - Create EventHub (Kafka)
    200. 11.9 HANDS-ON 1 - Setup Streaming on DBx
    201. 11.10 HANDS-ON 1 - Send Streaming Data to EventHub
    202. 11.11 HANDS-ON 1 - Display EventHub Data as-is
    203. 11.12 HANDS-ON 1 - Display Parsed EventHub Data
    204. 11.13 HANDS-ON 2 - Stream Silver Processing
    205. 11.14 HANDS-ON 3 - Stream Gold Processing
    206. 11.15 HANDS-ON 4 - Stream Processing using Lakeflow Job
    207. 11.16 HANDS-ON 5 - Stream-to-stream Join
    208. 11.17 Real-Time Mode
  Chapter 19: CHAPTER 12A : Security in Databricks
    209. 12.a.1 Databricks Identities
    210. 12.a.2 HANDS-ON - Create Databricks Identities
    211. 12.a.3 Authenticating to DBx using Entra ID
    212. 12.a.4 Entitlements
    213. 12.a.5 HANDS-ON - Grant Entitlements to Users & Groups
    214. 12.a.6 OAuth & PAT Authentication
    215. 12.a.7 HANDS-ON - OAuth & PAT Authentication
    216. 12.a.8 Access Control List (ACL)
    217. 12.a.9 HANDS-ON - Grant Object Permissions to
         Identities using ACLs
    218. 12.a.10 DBx Networking Architecture
    219. 12.a.11 VNet Injection
    220. 12.a.12 HANDS-ON - Update Workspace to VNet Injected
    221. 12.a.13 HANDS-ON - Create New Workspace with VNet
         Injection
    222. 12.a.14 IP Access List
    223. 12.a.15 HANDS-ON - IP Access List
    224. 12.a.16 Context Based Policies
    225. 12.a.17 HANDS-ON - Context-based Ingress & Egress
         Controls
    226. 12.a.18 Service Endpoint & Firewall Rule
    227. 12.a.19 Network Security Perimeter (NSP)
    228. 12.a.20 Combining All Network Security Features
         Together
    229. 12.a.21 DBx Network Reference Architectures
    230. 12.a.22 Secrets Management in DBx
    231. 12.a.23 HANDS-ON - Secret Management in DBx and Unity
         Catalog
  Chapter 20: CHAPTER 12B : Governance using Unity Catalog
    232. 12.b.1 Compliance Security Profile
    233. 12.b.2 Governance with Unity Catalog
    234. 12.b.3 Workspace Catalog Binding
    235. 12.b.4 Row Filter & Column Mask
    236. 12.b.5 Attribute Based Access Control (ABAC)
    237. 12.b.6 Data Discovery
    238. 12.b.7 Data Lineage
    239. 12.b.8 Data Classification & Data Quality Monitoring
  Chapter 21: CHAPTER 13 : Databricks Administration & CI/CD
    240. 13.1 DBx Administration
    241. 13.2 DBx System Tables
    242. 13.4 Compute Policies, Disaster Recovery (DR) & CI/CD
  Chapter 22: FUTURE UPDATES
    243. Will be updated in future
  DATES
  Published    : 2026-08-09
  Last Updated : 2026-08-14
If you fear the truth, dont come to my well.

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