Curriculum planned

Stage 11 · Cloud & Managed Data Platforms

Databricks Free Edition

A comprehensive Databricks Free Edition course covering the workspace, serverless compute, notebooks and files, Spark/DataFrames, SQL, Delta Lake, Unity Catalog concepts, Lakeflow ingestion and pipelines, jobs/orchestration concepts, MLflow, machine learning, Mosaic AI/agent and vector-search concepts, Genie and dashboards, Lakebase Postgres, governance, security, performance, cost/quota management, CI/CD, migration from Community Edition, and the boundary between free learning labs and paid production capabilities.

Browse planned curriculum Back to all courses

38planned chapters

190reserved lesson paths

Intermediate → Advancedlearning level

Plannedcourse state

Coverage baselineDatabricks Free Edition baseline current in mid-2026: no-cost, serverless-only and quota-limited, replacing Community Edition; curriculum uses Free Edition for hands-on notebooks/SQL/Spark/Delta/Lakeflow/AI experimentation where available while clearly separating full-platform concepts, quota/SLA limitations, Unity Catalog governance concepts, Genie/AI tooling, Lakebase Postgres, MLflow, vector/RAG patterns, and production architecture that may require paid workspaces

Course brief

Use Free Edition as a realistic learning and prototyping environment without confusing it with a production subscription: learn the Lakehouse platform deeply, but mark every reliability, quota, networking, governance, and enterprise feature boundary explicitly.

A comprehensive Databricks Free Edition course covering the workspace, serverless compute, notebooks and files, Spark/DataFrames, SQL, Delta Lake, Unity Catalog concepts, Lakeflow ingestion and pipelines, jobs/orchestration concepts, MLflow, machine learning, Mosaic AI/agent and vector-search concepts, Genie and dashboards, Lakebase Postgres, governance, security, performance, cost/quota management, CI/CD, migration from Community Edition, and the boundary between free learning labs and paid production capabilities.

This syllabus deliberately separates foundations, data/model semantics, internals, reliability, security, performance, operations, and production design so advanced material is not compressed into generic catch-all chapters.

By the end

You will be able to

Navigate a Databricks Free Edition workspace and build notebook/SQL/DataFrame projects using serverless compute, Spark, Delta and shared development workflows within quota constraints
Design medallion/lakehouse pipelines with Lakeflow concepts, Auto Loader/ingestion awareness, Delta tables, streaming, data quality, orchestration and reproducible transformations
Apply Unity Catalog governance concepts, SQL analytics, dashboards, Genie, MLflow, machine learning, vector/RAG and agent-development workflows while identifying Free Edition limitations
Understand Lakebase Postgres and operational-data integration alongside lakehouse data, plus the architecture differences between Free Edition and production Databricks deployments
Package, test, version, monitor, optimize and migrate projects with explicit cost/quota awareness, environment promotion concepts, CI/CD patterns, and production-readiness checklists

Complete planned syllabus

38 chapters · 190 lesson paths.

Every lesson path is reserved now but intentionally not linked until its lesson HTML is actually published. The sequence moves from foundations through advanced implementation, architecture, operations, reliability, security, tuning, and a production capstone.

01

Chapter 1

Databricks Free Edition Foundations: Account/Workspace, No-Cost Serverless Model, Quotas, No SLA, Community Edition Migration, and First Lab

5 lessons

01

Databricks Free Edition Foundations: Account/Workspace, No-Cost Serverless Model, Quotas, No SLA, Community Edition Migration, and First Lab: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter01/Lesson1.html

Planned02

Databricks Free Edition Foundations: Account/Workspace, No-Cost Serverless Model, Quotas, No SLA, Community Edition Migration, and First Lab: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter01/Lesson2.html

Planned03

Databricks Free Edition Foundations: Account/Workspace, No-Cost Serverless Model, Quotas, No SLA, Community Edition Migration, and First Lab: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter01/Lesson3.html

Planned04

Databricks Free Edition Foundations: Account/Workspace, No-Cost Serverless Model, Quotas, No SLA, Community Edition Migration, and First Lab: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter01/Lesson4.html

Planned05

Checkpoint Lab — Databricks Free Edition Foundations: Account/Workspace, No-Cost Serverless Model, Quotas, No SLA, Community Edition Migration, and First Lab: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter01/Lesson5.html

Planned

02

Chapter 2

Workspace Navigation and Assets: Notebooks, Files, Folders, Recents, Search, Repos/Git Concepts, Collaboration, Permissions Awareness, and Organization

5 lessons

01

Workspace Navigation and Assets: Notebooks, Files, Folders, Recents, Search, Repos/Git Concepts, Collaboration, Permissions Awareness, and Organization: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter02/Lesson1.html

Planned02

Workspace Navigation and Assets: Notebooks, Files, Folders, Recents, Search, Repos/Git Concepts, Collaboration, Permissions Awareness, and Organization: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter02/Lesson2.html

Planned03

Workspace Navigation and Assets: Notebooks, Files, Folders, Recents, Search, Repos/Git Concepts, Collaboration, Permissions Awareness, and Organization: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter02/Lesson3.html

Planned04

Workspace Navigation and Assets: Notebooks, Files, Folders, Recents, Search, Repos/Git Concepts, Collaboration, Permissions Awareness, and Organization: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter02/Lesson4.html

Planned05

Checkpoint Lab — Workspace Navigation and Assets: Notebooks, Files, Folders, Recents, Search, Repos/Git Concepts, Collaboration, Permissions Awareness, and Organization: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter02/Lesson5.html

Planned

03

Chapter 3

Serverless Compute Mental Model: Managed Compute, Startup/Autoscaling Concepts, Quota Enforcement, Isolation, Runtime Abstraction, and Free-Edition Limits

5 lessons

01

Serverless Compute Mental Model: Managed Compute, Startup/Autoscaling Concepts, Quota Enforcement, Isolation, Runtime Abstraction, and Free-Edition Limits: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter03/Lesson1.html

Planned02

Serverless Compute Mental Model: Managed Compute, Startup/Autoscaling Concepts, Quota Enforcement, Isolation, Runtime Abstraction, and Free-Edition Limits: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter03/Lesson2.html

Planned03

Serverless Compute Mental Model: Managed Compute, Startup/Autoscaling Concepts, Quota Enforcement, Isolation, Runtime Abstraction, and Free-Edition Limits: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter03/Lesson3.html

Planned04

Serverless Compute Mental Model: Managed Compute, Startup/Autoscaling Concepts, Quota Enforcement, Isolation, Runtime Abstraction, and Free-Edition Limits: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter03/Lesson4.html

Planned05

Checkpoint Lab — Serverless Compute Mental Model: Managed Compute, Startup/Autoscaling Concepts, Quota Enforcement, Isolation, Runtime Abstraction, and Free-Edition Limits: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter03/Lesson5.html

Planned

04

Chapter 4

Notebook Engineering: Python/SQL/Scala Concepts, Cells, Widgets/Parameters, Markdown, Results, Visualizations, Debugging, and Reproducible Execution

5 lessons

01

Notebook Engineering: Python/SQL/Scala Concepts, Cells, Widgets/Parameters, Markdown, Results, Visualizations, Debugging, and Reproducible Execution: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter04/Lesson1.html

Planned02

Notebook Engineering: Python/SQL/Scala Concepts, Cells, Widgets/Parameters, Markdown, Results, Visualizations, Debugging, and Reproducible Execution: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter04/Lesson2.html

Planned03

Notebook Engineering: Python/SQL/Scala Concepts, Cells, Widgets/Parameters, Markdown, Results, Visualizations, Debugging, and Reproducible Execution: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter04/Lesson3.html

Planned04

Notebook Engineering: Python/SQL/Scala Concepts, Cells, Widgets/Parameters, Markdown, Results, Visualizations, Debugging, and Reproducible Execution: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter04/Lesson4.html

Planned05

Checkpoint Lab — Notebook Engineering: Python/SQL/Scala Concepts, Cells, Widgets/Parameters, Markdown, Results, Visualizations, Debugging, and Reproducible Execution: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter04/Lesson5.html

Planned

05

Chapter 5

Apache Spark Foundations in Databricks: SparkSession, Driver/Workers Mental Model, Lazy Evaluation, Jobs/Stages/Tasks, and DataFrame-First Development

5 lessons

01

Apache Spark Foundations in Databricks: SparkSession, Driver/Workers Mental Model, Lazy Evaluation, Jobs/Stages/Tasks, and DataFrame-First Development: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter05/Lesson1.html

Planned02

Apache Spark Foundations in Databricks: SparkSession, Driver/Workers Mental Model, Lazy Evaluation, Jobs/Stages/Tasks, and DataFrame-First Development: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter05/Lesson2.html

Planned03

Apache Spark Foundations in Databricks: SparkSession, Driver/Workers Mental Model, Lazy Evaluation, Jobs/Stages/Tasks, and DataFrame-First Development: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter05/Lesson3.html

Planned04

Apache Spark Foundations in Databricks: SparkSession, Driver/Workers Mental Model, Lazy Evaluation, Jobs/Stages/Tasks, and DataFrame-First Development: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter05/Lesson4.html

Planned05

Checkpoint Lab — Apache Spark Foundations in Databricks: SparkSession, Driver/Workers Mental Model, Lazy Evaluation, Jobs/Stages/Tasks, and DataFrame-First Development: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter05/Lesson5.html

Planned

06

Chapter 6

DataFrame APIs: Schemas, Select/Filter/WithColumn, Joins, Aggregations, Windows, Complex Types, Nulls, Functions, and Transformation Design

5 lessons

01

DataFrame APIs: Schemas, Select/Filter/WithColumn, Joins, Aggregations, Windows, Complex Types, Nulls, Functions, and Transformation Design: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter06/Lesson1.html

Planned02

DataFrame APIs: Schemas, Select/Filter/WithColumn, Joins, Aggregations, Windows, Complex Types, Nulls, Functions, and Transformation Design: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter06/Lesson2.html

Planned03

DataFrame APIs: Schemas, Select/Filter/WithColumn, Joins, Aggregations, Windows, Complex Types, Nulls, Functions, and Transformation Design: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter06/Lesson3.html

Planned04

DataFrame APIs: Schemas, Select/Filter/WithColumn, Joins, Aggregations, Windows, Complex Types, Nulls, Functions, and Transformation Design: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter06/Lesson4.html

Planned05

Checkpoint Lab — DataFrame APIs: Schemas, Select/Filter/WithColumn, Joins, Aggregations, Windows, Complex Types, Nulls, Functions, and Transformation Design: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter06/Lesson5.html

Planned

07

Chapter 7

Spark SQL and Databricks SQL: SQL Editor, Warehouses/Serverless SQL Concepts, Catalog Resolution, Query History, Parameters, and Analyst Workflows

5 lessons

01

Spark SQL and Databricks SQL: SQL Editor, Warehouses/Serverless SQL Concepts, Catalog Resolution, Query History, Parameters, and Analyst Workflows: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter07/Lesson1.html

Planned02

Spark SQL and Databricks SQL: SQL Editor, Warehouses/Serverless SQL Concepts, Catalog Resolution, Query History, Parameters, and Analyst Workflows: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter07/Lesson2.html

Planned03

Spark SQL and Databricks SQL: SQL Editor, Warehouses/Serverless SQL Concepts, Catalog Resolution, Query History, Parameters, and Analyst Workflows: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter07/Lesson3.html

Planned04

Spark SQL and Databricks SQL: SQL Editor, Warehouses/Serverless SQL Concepts, Catalog Resolution, Query History, Parameters, and Analyst Workflows: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter07/Lesson4.html

Planned05

Checkpoint Lab — Spark SQL and Databricks SQL: SQL Editor, Warehouses/Serverless SQL Concepts, Catalog Resolution, Query History, Parameters, and Analyst Workflows: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter07/Lesson5.html

Planned

08

Chapter 8

Files and Data Ingestion: CSV/JSON/Parquet, Volumes/Object Storage Concepts, Uploads, Schema Inference, Corrupt Records, and Small-File Awareness

5 lessons

01

Files and Data Ingestion: CSV/JSON/Parquet, Volumes/Object Storage Concepts, Uploads, Schema Inference, Corrupt Records, and Small-File Awareness: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter08/Lesson1.html

Planned02

Files and Data Ingestion: CSV/JSON/Parquet, Volumes/Object Storage Concepts, Uploads, Schema Inference, Corrupt Records, and Small-File Awareness: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter08/Lesson2.html

Planned03

Files and Data Ingestion: CSV/JSON/Parquet, Volumes/Object Storage Concepts, Uploads, Schema Inference, Corrupt Records, and Small-File Awareness: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter08/Lesson3.html

Planned04

Files and Data Ingestion: CSV/JSON/Parquet, Volumes/Object Storage Concepts, Uploads, Schema Inference, Corrupt Records, and Small-File Awareness: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter08/Lesson4.html

Planned05

Checkpoint Lab — Files and Data Ingestion: CSV/JSON/Parquet, Volumes/Object Storage Concepts, Uploads, Schema Inference, Corrupt Records, and Small-File Awareness: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter08/Lesson5.html

Planned

09

Chapter 9

Delta Lake Fundamentals: ACID Tables, Transaction Log, Schema Enforcement, Time Travel, MERGE/UPDATE/DELETE, and Why Delta Is the Default Lakehouse Table

5 lessons

01

Delta Lake Fundamentals: ACID Tables, Transaction Log, Schema Enforcement, Time Travel, MERGE/UPDATE/DELETE, and Why Delta Is the Default Lakehouse Table: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter09/Lesson1.html

Planned02

Delta Lake Fundamentals: ACID Tables, Transaction Log, Schema Enforcement, Time Travel, MERGE/UPDATE/DELETE, and Why Delta Is the Default Lakehouse Table: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter09/Lesson2.html

Planned03

Delta Lake Fundamentals: ACID Tables, Transaction Log, Schema Enforcement, Time Travel, MERGE/UPDATE/DELETE, and Why Delta Is the Default Lakehouse Table: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter09/Lesson3.html

Planned04

Delta Lake Fundamentals: ACID Tables, Transaction Log, Schema Enforcement, Time Travel, MERGE/UPDATE/DELETE, and Why Delta Is the Default Lakehouse Table: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter09/Lesson4.html

Planned05

Checkpoint Lab — Delta Lake Fundamentals: ACID Tables, Transaction Log, Schema Enforcement, Time Travel, MERGE/UPDATE/DELETE, and Why Delta Is the Default Lakehouse Table: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter09/Lesson5.html

Planned

10

Chapter 10

Delta Advanced Patterns: Change Data Feed, Constraints, Generated/Identity Columns Concepts, Optimization, Clustering, Retention, and Streaming Integration

5 lessons

01

Delta Advanced Patterns: Change Data Feed, Constraints, Generated/Identity Columns Concepts, Optimization, Clustering, Retention, and Streaming Integration: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter10/Lesson1.html

Planned02

Delta Advanced Patterns: Change Data Feed, Constraints, Generated/Identity Columns Concepts, Optimization, Clustering, Retention, and Streaming Integration: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter10/Lesson2.html

Planned03

Delta Advanced Patterns: Change Data Feed, Constraints, Generated/Identity Columns Concepts, Optimization, Clustering, Retention, and Streaming Integration: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter10/Lesson3.html

Planned04

Delta Advanced Patterns: Change Data Feed, Constraints, Generated/Identity Columns Concepts, Optimization, Clustering, Retention, and Streaming Integration: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter10/Lesson4.html

Planned05

Checkpoint Lab — Delta Advanced Patterns: Change Data Feed, Constraints, Generated/Identity Columns Concepts, Optimization, Clustering, Retention, and Streaming Integration: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter10/Lesson5.html

Planned

11

Chapter 11

Medallion Architecture: Bronze/Silver/Gold Layers, Data Contracts, Incremental Processing, Idempotency, Late Data, Reprocessing, and Data Products

5 lessons

01

Medallion Architecture: Bronze/Silver/Gold Layers, Data Contracts, Incremental Processing, Idempotency, Late Data, Reprocessing, and Data Products: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter11/Lesson1.html

Planned02

Medallion Architecture: Bronze/Silver/Gold Layers, Data Contracts, Incremental Processing, Idempotency, Late Data, Reprocessing, and Data Products: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter11/Lesson2.html

Planned03

Medallion Architecture: Bronze/Silver/Gold Layers, Data Contracts, Incremental Processing, Idempotency, Late Data, Reprocessing, and Data Products: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter11/Lesson3.html

Planned04

Medallion Architecture: Bronze/Silver/Gold Layers, Data Contracts, Incremental Processing, Idempotency, Late Data, Reprocessing, and Data Products: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter11/Lesson4.html

Planned05

Checkpoint Lab — Medallion Architecture: Bronze/Silver/Gold Layers, Data Contracts, Incremental Processing, Idempotency, Late Data, Reprocessing, and Data Products: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter11/Lesson5.html

Planned

12

Chapter 12

Structured Streaming: Sources/Sinks, Checkpoints, Watermarks, Stateful Operations, foreachBatch, Delta Streaming, Recovery, and Exactly-Once Boundaries

5 lessons

01

Structured Streaming: Sources/Sinks, Checkpoints, Watermarks, Stateful Operations, foreachBatch, Delta Streaming, Recovery, and Exactly-Once Boundaries: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter12/Lesson1.html

Planned02

Structured Streaming: Sources/Sinks, Checkpoints, Watermarks, Stateful Operations, foreachBatch, Delta Streaming, Recovery, and Exactly-Once Boundaries: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter12/Lesson2.html

Planned03

Structured Streaming: Sources/Sinks, Checkpoints, Watermarks, Stateful Operations, foreachBatch, Delta Streaming, Recovery, and Exactly-Once Boundaries: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter12/Lesson3.html

Planned04

Structured Streaming: Sources/Sinks, Checkpoints, Watermarks, Stateful Operations, foreachBatch, Delta Streaming, Recovery, and Exactly-Once Boundaries: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter12/Lesson4.html

Planned05

Checkpoint Lab — Structured Streaming: Sources/Sinks, Checkpoints, Watermarks, Stateful Operations, foreachBatch, Delta Streaming, Recovery, and Exactly-Once Boundaries: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter12/Lesson5.html

Planned

13

Chapter 13

Lakeflow Connect Concepts: Managed Ingestion, Standard Connectors, CDC/Incremental Sources, Scheduling, Governance, and Free-Edition Availability Boundaries

5 lessons

01

Lakeflow Connect Concepts: Managed Ingestion, Standard Connectors, CDC/Incremental Sources, Scheduling, Governance, and Free-Edition Availability Boundaries: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter13/Lesson1.html

Planned02

Lakeflow Connect Concepts: Managed Ingestion, Standard Connectors, CDC/Incremental Sources, Scheduling, Governance, and Free-Edition Availability Boundaries: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter13/Lesson2.html

Planned03

Lakeflow Connect Concepts: Managed Ingestion, Standard Connectors, CDC/Incremental Sources, Scheduling, Governance, and Free-Edition Availability Boundaries: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter13/Lesson3.html

Planned04

Lakeflow Connect Concepts: Managed Ingestion, Standard Connectors, CDC/Incremental Sources, Scheduling, Governance, and Free-Edition Availability Boundaries: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter13/Lesson4.html

Planned05

Checkpoint Lab — Lakeflow Connect Concepts: Managed Ingestion, Standard Connectors, CDC/Incremental Sources, Scheduling, Governance, and Free-Edition Availability Boundaries: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter13/Lesson5.html

Planned

14

Chapter 14

Lakeflow Declarative Pipelines: Pipeline Graphs, Streaming Tables/Materialized Views Concepts, Expectations/Data Quality, Incremental Semantics, and Operations

5 lessons

01

Lakeflow Declarative Pipelines: Pipeline Graphs, Streaming Tables/Materialized Views Concepts, Expectations/Data Quality, Incremental Semantics, and Operations: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter14/Lesson1.html

Planned02

Lakeflow Declarative Pipelines: Pipeline Graphs, Streaming Tables/Materialized Views Concepts, Expectations/Data Quality, Incremental Semantics, and Operations: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter14/Lesson2.html

Planned03

Lakeflow Declarative Pipelines: Pipeline Graphs, Streaming Tables/Materialized Views Concepts, Expectations/Data Quality, Incremental Semantics, and Operations: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter14/Lesson3.html

Planned04

Lakeflow Declarative Pipelines: Pipeline Graphs, Streaming Tables/Materialized Views Concepts, Expectations/Data Quality, Incremental Semantics, and Operations: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter14/Lesson4.html

Planned05

Checkpoint Lab — Lakeflow Declarative Pipelines: Pipeline Graphs, Streaming Tables/Materialized Views Concepts, Expectations/Data Quality, Incremental Semantics, and Operations: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter14/Lesson5.html

Planned

15

Chapter 15

Lakeflow Jobs and Orchestration Concepts: Tasks, Dependencies, Parameters, Schedules, Retries, Triggers, Notifications, and Free-vs-Paid Capability Boundaries

5 lessons

01

Lakeflow Jobs and Orchestration Concepts: Tasks, Dependencies, Parameters, Schedules, Retries, Triggers, Notifications, and Free-vs-Paid Capability Boundaries: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter15/Lesson1.html

Planned02

Lakeflow Jobs and Orchestration Concepts: Tasks, Dependencies, Parameters, Schedules, Retries, Triggers, Notifications, and Free-vs-Paid Capability Boundaries: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter15/Lesson2.html

Planned03

Lakeflow Jobs and Orchestration Concepts: Tasks, Dependencies, Parameters, Schedules, Retries, Triggers, Notifications, and Free-vs-Paid Capability Boundaries: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter15/Lesson3.html

Planned04

Lakeflow Jobs and Orchestration Concepts: Tasks, Dependencies, Parameters, Schedules, Retries, Triggers, Notifications, and Free-vs-Paid Capability Boundaries: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter15/Lesson4.html

Planned05

Checkpoint Lab — Lakeflow Jobs and Orchestration Concepts: Tasks, Dependencies, Parameters, Schedules, Retries, Triggers, Notifications, and Free-vs-Paid Capability Boundaries: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter15/Lesson5.html

Planned

16

Chapter 16

Unity Catalog Foundations: Catalog/Schema/Table/Volume/Function Hierarchy, Three-Level Namespace, Ownership, Privileges, and Governance Mental Model

5 lessons

01

Unity Catalog Foundations: Catalog/Schema/Table/Volume/Function Hierarchy, Three-Level Namespace, Ownership, Privileges, and Governance Mental Model: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter16/Lesson1.html

Planned02

Unity Catalog Foundations: Catalog/Schema/Table/Volume/Function Hierarchy, Three-Level Namespace, Ownership, Privileges, and Governance Mental Model: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter16/Lesson2.html

Planned03

Unity Catalog Foundations: Catalog/Schema/Table/Volume/Function Hierarchy, Three-Level Namespace, Ownership, Privileges, and Governance Mental Model: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter16/Lesson3.html

Planned04

Unity Catalog Foundations: Catalog/Schema/Table/Volume/Function Hierarchy, Three-Level Namespace, Ownership, Privileges, and Governance Mental Model: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter16/Lesson4.html

Planned05

Checkpoint Lab — Unity Catalog Foundations: Catalog/Schema/Table/Volume/Function Hierarchy, Three-Level Namespace, Ownership, Privileges, and Governance Mental Model: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter16/Lesson5.html

Planned

17

Chapter 17

Unity Catalog Governance: Grants, Groups, Service Principals Awareness, Row/Column Controls Concepts, Lineage, Tags, Audit, and Environment Separation

5 lessons

01

Unity Catalog Governance: Grants, Groups, Service Principals Awareness, Row/Column Controls Concepts, Lineage, Tags, Audit, and Environment Separation: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter17/Lesson1.html

Planned02

Unity Catalog Governance: Grants, Groups, Service Principals Awareness, Row/Column Controls Concepts, Lineage, Tags, Audit, and Environment Separation: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter17/Lesson2.html

Planned03

Unity Catalog Governance: Grants, Groups, Service Principals Awareness, Row/Column Controls Concepts, Lineage, Tags, Audit, and Environment Separation: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter17/Lesson3.html

Planned04

Unity Catalog Governance: Grants, Groups, Service Principals Awareness, Row/Column Controls Concepts, Lineage, Tags, Audit, and Environment Separation: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter17/Lesson4.html

Planned05

Checkpoint Lab — Unity Catalog Governance: Grants, Groups, Service Principals Awareness, Row/Column Controls Concepts, Lineage, Tags, Audit, and Environment Separation: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter17/Lesson5.html

Planned

18

Chapter 18

Data Sharing and Federation Concepts: Delta Sharing, Lakehouse Federation, External Locations/Credentials, Cross-System Access, and Free-Edition Limits

5 lessons

01

Data Sharing and Federation Concepts: Delta Sharing, Lakehouse Federation, External Locations/Credentials, Cross-System Access, and Free-Edition Limits: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter18/Lesson1.html

Planned02

Data Sharing and Federation Concepts: Delta Sharing, Lakehouse Federation, External Locations/Credentials, Cross-System Access, and Free-Edition Limits: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter18/Lesson2.html

Planned03

Data Sharing and Federation Concepts: Delta Sharing, Lakehouse Federation, External Locations/Credentials, Cross-System Access, and Free-Edition Limits: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter18/Lesson3.html

Planned04

Data Sharing and Federation Concepts: Delta Sharing, Lakehouse Federation, External Locations/Credentials, Cross-System Access, and Free-Edition Limits: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter18/Lesson4.html

Planned05

Checkpoint Lab — Data Sharing and Federation Concepts: Delta Sharing, Lakehouse Federation, External Locations/Credentials, Cross-System Access, and Free-Edition Limits: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter18/Lesson5.html

Planned

19

Chapter 19

Databricks SQL Performance: Query Profile, Photon Concepts, Predicate Pushdown, File Pruning, Join Strategies, Caching, Statistics, and Layout Tuning

5 lessons

01

Databricks SQL Performance: Query Profile, Photon Concepts, Predicate Pushdown, File Pruning, Join Strategies, Caching, Statistics, and Layout Tuning: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter19/Lesson1.html

Planned02

Databricks SQL Performance: Query Profile, Photon Concepts, Predicate Pushdown, File Pruning, Join Strategies, Caching, Statistics, and Layout Tuning: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter19/Lesson2.html

Planned03

Databricks SQL Performance: Query Profile, Photon Concepts, Predicate Pushdown, File Pruning, Join Strategies, Caching, Statistics, and Layout Tuning: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter19/Lesson3.html

Planned04

Databricks SQL Performance: Query Profile, Photon Concepts, Predicate Pushdown, File Pruning, Join Strategies, Caching, Statistics, and Layout Tuning: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter19/Lesson4.html

Planned05

Checkpoint Lab — Databricks SQL Performance: Query Profile, Photon Concepts, Predicate Pushdown, File Pruning, Join Strategies, Caching, Statistics, and Layout Tuning: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter19/Lesson5.html

Planned

20

Chapter 20

Spark Performance Engineering: Partitioning, Shuffle, Skew, AQE, Broadcast Joins, Caching, UDF Avoidance, Arrow, Memory Concepts, and Benchmarking

5 lessons

01

Spark Performance Engineering: Partitioning, Shuffle, Skew, AQE, Broadcast Joins, Caching, UDF Avoidance, Arrow, Memory Concepts, and Benchmarking: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter20/Lesson1.html

Planned02

Spark Performance Engineering: Partitioning, Shuffle, Skew, AQE, Broadcast Joins, Caching, UDF Avoidance, Arrow, Memory Concepts, and Benchmarking: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter20/Lesson2.html

Planned03

Spark Performance Engineering: Partitioning, Shuffle, Skew, AQE, Broadcast Joins, Caching, UDF Avoidance, Arrow, Memory Concepts, and Benchmarking: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter20/Lesson3.html

Planned04

Spark Performance Engineering: Partitioning, Shuffle, Skew, AQE, Broadcast Joins, Caching, UDF Avoidance, Arrow, Memory Concepts, and Benchmarking: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter20/Lesson4.html

Planned05

Checkpoint Lab — Spark Performance Engineering: Partitioning, Shuffle, Skew, AQE, Broadcast Joins, Caching, UDF Avoidance, Arrow, Memory Concepts, and Benchmarking: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter20/Lesson5.html

Planned

21

Chapter 21

MLflow Tracking: Experiments, Runs, Parameters, Metrics, Artifacts, Models, Reproducibility, Autologging, and Collaborative Experiment Management

5 lessons

01

MLflow Tracking: Experiments, Runs, Parameters, Metrics, Artifacts, Models, Reproducibility, Autologging, and Collaborative Experiment Management: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter21/Lesson1.html

Planned02

MLflow Tracking: Experiments, Runs, Parameters, Metrics, Artifacts, Models, Reproducibility, Autologging, and Collaborative Experiment Management: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter21/Lesson2.html

Planned03

MLflow Tracking: Experiments, Runs, Parameters, Metrics, Artifacts, Models, Reproducibility, Autologging, and Collaborative Experiment Management: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter21/Lesson3.html

Planned04

MLflow Tracking: Experiments, Runs, Parameters, Metrics, Artifacts, Models, Reproducibility, Autologging, and Collaborative Experiment Management: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter21/Lesson4.html

Planned05

Checkpoint Lab — MLflow Tracking: Experiments, Runs, Parameters, Metrics, Artifacts, Models, Reproducibility, Autologging, and Collaborative Experiment Management: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter21/Lesson5.html

Planned

22

Chapter 22

Machine Learning Workflow: Feature Preparation, Train/Validation/Test, Spark ML/scikit-learn Concepts, Evaluation, Hyperparameter Search Awareness, and Leakage Prevention

5 lessons

01

Machine Learning Workflow: Feature Preparation, Train/Validation/Test, Spark ML/scikit-learn Concepts, Evaluation, Hyperparameter Search Awareness, and Leakage Prevention: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter22/Lesson1.html

Planned02

Machine Learning Workflow: Feature Preparation, Train/Validation/Test, Spark ML/scikit-learn Concepts, Evaluation, Hyperparameter Search Awareness, and Leakage Prevention: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter22/Lesson2.html

Planned03

Machine Learning Workflow: Feature Preparation, Train/Validation/Test, Spark ML/scikit-learn Concepts, Evaluation, Hyperparameter Search Awareness, and Leakage Prevention: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter22/Lesson3.html

Planned04

Machine Learning Workflow: Feature Preparation, Train/Validation/Test, Spark ML/scikit-learn Concepts, Evaluation, Hyperparameter Search Awareness, and Leakage Prevention: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter22/Lesson4.html

Planned05

Checkpoint Lab — Machine Learning Workflow: Feature Preparation, Train/Validation/Test, Spark ML/scikit-learn Concepts, Evaluation, Hyperparameter Search Awareness, and Leakage Prevention: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter22/Lesson5.html

Planned

23

Chapter 23

Model Registry and MLOps Concepts: Model Versions/Aliases, Promotion, Serving Concepts, Monitoring, CI/CD, Governance, and Free-Edition Boundaries

5 lessons

01

Model Registry and MLOps Concepts: Model Versions/Aliases, Promotion, Serving Concepts, Monitoring, CI/CD, Governance, and Free-Edition Boundaries: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter23/Lesson1.html

Planned02

Model Registry and MLOps Concepts: Model Versions/Aliases, Promotion, Serving Concepts, Monitoring, CI/CD, Governance, and Free-Edition Boundaries: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter23/Lesson2.html

Planned03

Model Registry and MLOps Concepts: Model Versions/Aliases, Promotion, Serving Concepts, Monitoring, CI/CD, Governance, and Free-Edition Boundaries: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter23/Lesson3.html

Planned04

Model Registry and MLOps Concepts: Model Versions/Aliases, Promotion, Serving Concepts, Monitoring, CI/CD, Governance, and Free-Edition Boundaries: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter23/Lesson4.html

Planned05

Checkpoint Lab — Model Registry and MLOps Concepts: Model Versions/Aliases, Promotion, Serving Concepts, Monitoring, CI/CD, Governance, and Free-Edition Boundaries: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter23/Lesson5.html

Planned

24

Chapter 24

Mosaic AI and Foundation Model Workflows: Model Access Concepts, Prompting, AI Playground/Endpoints Awareness, Evaluation, Safety, Cost, and Governance

5 lessons

01

Mosaic AI and Foundation Model Workflows: Model Access Concepts, Prompting, AI Playground/Endpoints Awareness, Evaluation, Safety, Cost, and Governance: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter24/Lesson1.html

Planned02

Mosaic AI and Foundation Model Workflows: Model Access Concepts, Prompting, AI Playground/Endpoints Awareness, Evaluation, Safety, Cost, and Governance: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter24/Lesson2.html

Planned03

Mosaic AI and Foundation Model Workflows: Model Access Concepts, Prompting, AI Playground/Endpoints Awareness, Evaluation, Safety, Cost, and Governance: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter24/Lesson3.html

Planned04

Mosaic AI and Foundation Model Workflows: Model Access Concepts, Prompting, AI Playground/Endpoints Awareness, Evaluation, Safety, Cost, and Governance: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter24/Lesson4.html

Planned05

Checkpoint Lab — Mosaic AI and Foundation Model Workflows: Model Access Concepts, Prompting, AI Playground/Endpoints Awareness, Evaluation, Safety, Cost, and Governance: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter24/Lesson5.html

Planned

25

Chapter 25

Vector Search and RAG Concepts: Embeddings, Vector Indexes, Chunking, Retrieval, Hybrid Search, Reranking, Evaluation, and Lakehouse Data Integration

5 lessons

01

Vector Search and RAG Concepts: Embeddings, Vector Indexes, Chunking, Retrieval, Hybrid Search, Reranking, Evaluation, and Lakehouse Data Integration: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter25/Lesson1.html

Planned02

Vector Search and RAG Concepts: Embeddings, Vector Indexes, Chunking, Retrieval, Hybrid Search, Reranking, Evaluation, and Lakehouse Data Integration: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter25/Lesson2.html

Planned03

Vector Search and RAG Concepts: Embeddings, Vector Indexes, Chunking, Retrieval, Hybrid Search, Reranking, Evaluation, and Lakehouse Data Integration: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter25/Lesson3.html

Planned04

Vector Search and RAG Concepts: Embeddings, Vector Indexes, Chunking, Retrieval, Hybrid Search, Reranking, Evaluation, and Lakehouse Data Integration: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter25/Lesson4.html

Planned05

Checkpoint Lab — Vector Search and RAG Concepts: Embeddings, Vector Indexes, Chunking, Retrieval, Hybrid Search, Reranking, Evaluation, and Lakehouse Data Integration: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter25/Lesson5.html

Planned

26

Chapter 26

AI Agents: Agent Development Concepts, Tool Calling, Retrieval, Evaluation, Tracing, Governance, Deployment Awareness, and Free-Edition Experimentation

5 lessons

01

AI Agents: Agent Development Concepts, Tool Calling, Retrieval, Evaluation, Tracing, Governance, Deployment Awareness, and Free-Edition Experimentation: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter26/Lesson1.html

Planned02

AI Agents: Agent Development Concepts, Tool Calling, Retrieval, Evaluation, Tracing, Governance, Deployment Awareness, and Free-Edition Experimentation: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter26/Lesson2.html

Planned03

AI Agents: Agent Development Concepts, Tool Calling, Retrieval, Evaluation, Tracing, Governance, Deployment Awareness, and Free-Edition Experimentation: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter26/Lesson3.html

Planned04

AI Agents: Agent Development Concepts, Tool Calling, Retrieval, Evaluation, Tracing, Governance, Deployment Awareness, and Free-Edition Experimentation: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter26/Lesson4.html

Planned05

Checkpoint Lab — AI Agents: Agent Development Concepts, Tool Calling, Retrieval, Evaluation, Tracing, Governance, Deployment Awareness, and Free-Edition Experimentation: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter26/Lesson5.html

Planned

27

Chapter 27

Genie and Conversational Analytics: Natural-Language Data Exploration, Semantic Context, Verified Queries/Instructions Concepts, Trust, Evaluation, and Permissions

5 lessons

01

Genie and Conversational Analytics: Natural-Language Data Exploration, Semantic Context, Verified Queries/Instructions Concepts, Trust, Evaluation, and Permissions: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter27/Lesson1.html

Planned02

Genie and Conversational Analytics: Natural-Language Data Exploration, Semantic Context, Verified Queries/Instructions Concepts, Trust, Evaluation, and Permissions: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter27/Lesson2.html

Planned03

Genie and Conversational Analytics: Natural-Language Data Exploration, Semantic Context, Verified Queries/Instructions Concepts, Trust, Evaluation, and Permissions: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter27/Lesson3.html

Planned04

Genie and Conversational Analytics: Natural-Language Data Exploration, Semantic Context, Verified Queries/Instructions Concepts, Trust, Evaluation, and Permissions: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter27/Lesson4.html

Planned05

Checkpoint Lab — Genie and Conversational Analytics: Natural-Language Data Exploration, Semantic Context, Verified Queries/Instructions Concepts, Trust, Evaluation, and Permissions: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter27/Lesson5.html

Planned

28

Chapter 28

Dashboards and Visualization: SQL Visualizations, Interactive Dashboards, Parameters/Filters, Sharing, Performance, Storytelling, and Governance

5 lessons

01

Dashboards and Visualization: SQL Visualizations, Interactive Dashboards, Parameters/Filters, Sharing, Performance, Storytelling, and Governance: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter28/Lesson1.html

Planned02

Dashboards and Visualization: SQL Visualizations, Interactive Dashboards, Parameters/Filters, Sharing, Performance, Storytelling, and Governance: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter28/Lesson2.html

Planned03

Dashboards and Visualization: SQL Visualizations, Interactive Dashboards, Parameters/Filters, Sharing, Performance, Storytelling, and Governance: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter28/Lesson3.html

Planned04

Dashboards and Visualization: SQL Visualizations, Interactive Dashboards, Parameters/Filters, Sharing, Performance, Storytelling, and Governance: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter28/Lesson4.html

Planned05

Checkpoint Lab — Dashboards and Visualization: SQL Visualizations, Interactive Dashboards, Parameters/Filters, Sharing, Performance, Storytelling, and Governance: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter28/Lesson5.html

Planned

29

Chapter 29

Lakebase Postgres: Managed PostgreSQL Concepts, Transactional Applications, SQL, Connections, App Data, Synchronization with Lakehouse, and Free-Edition Labs

5 lessons

01

Lakebase Postgres: Managed PostgreSQL Concepts, Transactional Applications, SQL, Connections, App Data, Synchronization with Lakehouse, and Free-Edition Labs: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter29/Lesson1.html

Planned02

Lakebase Postgres: Managed PostgreSQL Concepts, Transactional Applications, SQL, Connections, App Data, Synchronization with Lakehouse, and Free-Edition Labs: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter29/Lesson2.html

Planned03

Lakebase Postgres: Managed PostgreSQL Concepts, Transactional Applications, SQL, Connections, App Data, Synchronization with Lakehouse, and Free-Edition Labs: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter29/Lesson3.html

Planned04

Lakebase Postgres: Managed PostgreSQL Concepts, Transactional Applications, SQL, Connections, App Data, Synchronization with Lakehouse, and Free-Edition Labs: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter29/Lesson4.html

Planned05

Checkpoint Lab — Lakebase Postgres: Managed PostgreSQL Concepts, Transactional Applications, SQL, Connections, App Data, Synchronization with Lakehouse, and Free-Edition Labs: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter29/Lesson5.html

Planned

30

Chapter 30

Apps and Application Development Concepts: Databricks Apps Awareness, APIs, Secrets, Authentication, Lakebase/SQL Backends, Deployment, and Product Boundaries

5 lessons

01

Apps and Application Development Concepts: Databricks Apps Awareness, APIs, Secrets, Authentication, Lakebase/SQL Backends, Deployment, and Product Boundaries: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter30/Lesson1.html

Planned02

Apps and Application Development Concepts: Databricks Apps Awareness, APIs, Secrets, Authentication, Lakebase/SQL Backends, Deployment, and Product Boundaries: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter30/Lesson2.html

Planned03

Apps and Application Development Concepts: Databricks Apps Awareness, APIs, Secrets, Authentication, Lakebase/SQL Backends, Deployment, and Product Boundaries: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter30/Lesson3.html

Planned04

Apps and Application Development Concepts: Databricks Apps Awareness, APIs, Secrets, Authentication, Lakebase/SQL Backends, Deployment, and Product Boundaries: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter30/Lesson4.html

Planned05

Checkpoint Lab — Apps and Application Development Concepts: Databricks Apps Awareness, APIs, Secrets, Authentication, Lakebase/SQL Backends, Deployment, and Product Boundaries: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter30/Lesson5.html

Planned

31

Chapter 31

Security and Secrets: Workspace Identity, Tokens/OAuth Concepts, Secrets, Least Privilege, Data Exfiltration Risks, Notebook Hygiene, and Free-Edition Constraints

5 lessons

01

Security and Secrets: Workspace Identity, Tokens/OAuth Concepts, Secrets, Least Privilege, Data Exfiltration Risks, Notebook Hygiene, and Free-Edition Constraints: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter31/Lesson1.html

Planned02

Security and Secrets: Workspace Identity, Tokens/OAuth Concepts, Secrets, Least Privilege, Data Exfiltration Risks, Notebook Hygiene, and Free-Edition Constraints: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter31/Lesson2.html

Planned03

Security and Secrets: Workspace Identity, Tokens/OAuth Concepts, Secrets, Least Privilege, Data Exfiltration Risks, Notebook Hygiene, and Free-Edition Constraints: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter31/Lesson3.html

Planned04

Security and Secrets: Workspace Identity, Tokens/OAuth Concepts, Secrets, Least Privilege, Data Exfiltration Risks, Notebook Hygiene, and Free-Edition Constraints: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter31/Lesson4.html

Planned05

Checkpoint Lab — Security and Secrets: Workspace Identity, Tokens/OAuth Concepts, Secrets, Least Privilege, Data Exfiltration Risks, Notebook Hygiene, and Free-Edition Constraints: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter31/Lesson5.html

Planned

32

Chapter 32

Observability: Query History, Spark UI/Logs, Pipeline/Job Metrics Concepts, MLflow Traces, Cost/Quota Signals, Alerts Awareness, and Troubleshooting

5 lessons

01

Observability: Query History, Spark UI/Logs, Pipeline/Job Metrics Concepts, MLflow Traces, Cost/Quota Signals, Alerts Awareness, and Troubleshooting: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter32/Lesson1.html

Planned02

Observability: Query History, Spark UI/Logs, Pipeline/Job Metrics Concepts, MLflow Traces, Cost/Quota Signals, Alerts Awareness, and Troubleshooting: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter32/Lesson2.html

Planned03

Observability: Query History, Spark UI/Logs, Pipeline/Job Metrics Concepts, MLflow Traces, Cost/Quota Signals, Alerts Awareness, and Troubleshooting: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter32/Lesson3.html

Planned04

Observability: Query History, Spark UI/Logs, Pipeline/Job Metrics Concepts, MLflow Traces, Cost/Quota Signals, Alerts Awareness, and Troubleshooting: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter32/Lesson4.html

Planned05

Checkpoint Lab — Observability: Query History, Spark UI/Logs, Pipeline/Job Metrics Concepts, MLflow Traces, Cost/Quota Signals, Alerts Awareness, and Troubleshooting: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter32/Lesson5.html

Planned

33

Chapter 33

Quota and Cost Engineering: Free Edition Fair Usage, Daily/Monthly Shutdown Behavior, Serverless Quotas, Efficient Labs, Paid Cost Concepts, and Capacity Planning

5 lessons

01

Quota and Cost Engineering: Free Edition Fair Usage, Daily/Monthly Shutdown Behavior, Serverless Quotas, Efficient Labs, Paid Cost Concepts, and Capacity Planning: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter33/Lesson1.html

Planned02

Quota and Cost Engineering: Free Edition Fair Usage, Daily/Monthly Shutdown Behavior, Serverless Quotas, Efficient Labs, Paid Cost Concepts, and Capacity Planning: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter33/Lesson2.html

Planned03

Quota and Cost Engineering: Free Edition Fair Usage, Daily/Monthly Shutdown Behavior, Serverless Quotas, Efficient Labs, Paid Cost Concepts, and Capacity Planning: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter33/Lesson3.html

Planned04

Quota and Cost Engineering: Free Edition Fair Usage, Daily/Monthly Shutdown Behavior, Serverless Quotas, Efficient Labs, Paid Cost Concepts, and Capacity Planning: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter33/Lesson4.html

Planned05

Checkpoint Lab — Quota and Cost Engineering: Free Edition Fair Usage, Daily/Monthly Shutdown Behavior, Serverless Quotas, Efficient Labs, Paid Cost Concepts, and Capacity Planning: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter33/Lesson5.html

Planned

34

Chapter 34

Testing and Data Quality: Unit Tests, DataFrame Equality, Expectations, SQL Assertions, Contract Tests, Deterministic Fixtures, and Failure Injection

5 lessons

01

Testing and Data Quality: Unit Tests, DataFrame Equality, Expectations, SQL Assertions, Contract Tests, Deterministic Fixtures, and Failure Injection: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter34/Lesson1.html

Planned02

Testing and Data Quality: Unit Tests, DataFrame Equality, Expectations, SQL Assertions, Contract Tests, Deterministic Fixtures, and Failure Injection: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter34/Lesson2.html

Planned03

Testing and Data Quality: Unit Tests, DataFrame Equality, Expectations, SQL Assertions, Contract Tests, Deterministic Fixtures, and Failure Injection: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter34/Lesson3.html

Planned04

Testing and Data Quality: Unit Tests, DataFrame Equality, Expectations, SQL Assertions, Contract Tests, Deterministic Fixtures, and Failure Injection: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter34/Lesson4.html

Planned05

Checkpoint Lab — Testing and Data Quality: Unit Tests, DataFrame Equality, Expectations, SQL Assertions, Contract Tests, Deterministic Fixtures, and Failure Injection: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter34/Lesson5.html

Planned

35

Chapter 35

Git, CI/CD, and Environment Promotion: Version Control, Databricks Asset Bundles Concepts, Dev/Test/Prod, Secrets, Automated Validation, and Release Patterns

5 lessons

01

Git, CI/CD, and Environment Promotion: Version Control, Databricks Asset Bundles Concepts, Dev/Test/Prod, Secrets, Automated Validation, and Release Patterns: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter35/Lesson1.html

Planned02

Git, CI/CD, and Environment Promotion: Version Control, Databricks Asset Bundles Concepts, Dev/Test/Prod, Secrets, Automated Validation, and Release Patterns: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter35/Lesson2.html

Planned03

Git, CI/CD, and Environment Promotion: Version Control, Databricks Asset Bundles Concepts, Dev/Test/Prod, Secrets, Automated Validation, and Release Patterns: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter35/Lesson3.html

Planned04

Git, CI/CD, and Environment Promotion: Version Control, Databricks Asset Bundles Concepts, Dev/Test/Prod, Secrets, Automated Validation, and Release Patterns: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter35/Lesson4.html

Planned05

Checkpoint Lab — Git, CI/CD, and Environment Promotion: Version Control, Databricks Asset Bundles Concepts, Dev/Test/Prod, Secrets, Automated Validation, and Release Patterns: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter35/Lesson5.html

Planned

36

Chapter 36

Migration from Community Edition and Portability: Workspace Assets, Notebooks, Data, Libraries, Runtime Differences, Free Edition Replacement, and Validation

5 lessons

01

Migration from Community Edition and Portability: Workspace Assets, Notebooks, Data, Libraries, Runtime Differences, Free Edition Replacement, and Validation: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter36/Lesson1.html

Planned02

Migration from Community Edition and Portability: Workspace Assets, Notebooks, Data, Libraries, Runtime Differences, Free Edition Replacement, and Validation: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter36/Lesson2.html

Planned03

Migration from Community Edition and Portability: Workspace Assets, Notebooks, Data, Libraries, Runtime Differences, Free Edition Replacement, and Validation: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter36/Lesson3.html

Planned04

Migration from Community Edition and Portability: Workspace Assets, Notebooks, Data, Libraries, Runtime Differences, Free Edition Replacement, and Validation: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter36/Lesson4.html

Planned05

Checkpoint Lab — Migration from Community Edition and Portability: Workspace Assets, Notebooks, Data, Libraries, Runtime Differences, Free Edition Replacement, and Validation: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter36/Lesson5.html

Planned

37

Chapter 37

Production Architecture Beyond Free Edition: Networking, Private Connectivity, HA/SLA, Enterprise Identity, Governance, Workload Isolation, DR, and Why Production Needs Different Controls

5 lessons

01

Production Architecture Beyond Free Edition: Networking, Private Connectivity, HA/SLA, Enterprise Identity, Governance, Workload Isolation, DR, and Why Production Needs Different Controls: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter37/Lesson1.html

Planned02

Production Architecture Beyond Free Edition: Networking, Private Connectivity, HA/SLA, Enterprise Identity, Governance, Workload Isolation, DR, and Why Production Needs Different Controls: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter37/Lesson2.html

Planned03

Production Architecture Beyond Free Edition: Networking, Private Connectivity, HA/SLA, Enterprise Identity, Governance, Workload Isolation, DR, and Why Production Needs Different Controls: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter37/Lesson3.html

Planned04

Production Architecture Beyond Free Edition: Networking, Private Connectivity, HA/SLA, Enterprise Identity, Governance, Workload Isolation, DR, and Why Production Needs Different Controls: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter37/Lesson4.html

Planned05

Checkpoint Lab — Production Architecture Beyond Free Edition: Networking, Private Connectivity, HA/SLA, Enterprise Identity, Governance, Workload Isolation, DR, and Why Production Needs Different Controls: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter37/Lesson5.html

Planned

38

Chapter 38

Production-Style Capstone in Free Edition: Build a Governed Lakehouse/AI Prototype, Measure Quotas, Test Failure Paths, Document Paid-Platform Gaps, and Produce a Migration Plan

5 lessons

01

Production-Style Capstone in Free Edition: Build a Governed Lakehouse/AI Prototype, Measure Quotas, Test Failure Paths, Document Paid-Platform Gaps, and Produce a Migration Plan: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter38/Lesson1.html

Planned02

Production-Style Capstone in Free Edition: Build a Governed Lakehouse/AI Prototype, Measure Quotas, Test Failure Paths, Document Paid-Platform Gaps, and Produce a Migration Plan: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter38/Lesson2.html

Planned03

Production-Style Capstone in Free Edition: Build a Governed Lakehouse/AI Prototype, Measure Quotas, Test Failure Paths, Document Paid-Platform Gaps, and Produce a Migration Plan: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter38/Lesson3.html

Planned04

Production-Style Capstone in Free Edition: Build a Governed Lakehouse/AI Prototype, Measure Quotas, Test Failure Paths, Document Paid-Platform Gaps, and Produce a Migration Plan: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter38/Lesson4.html

Planned05

Checkpoint Lab — Production-Style Capstone in Free Edition: Build a Governed Lakehouse/AI Prototype, Measure Quotas, Test Failure Paths, Document Paid-Platform Gaps, and Produce a Migration Plan: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter38/Lesson5.html

Planned