Curriculum planned

Stage 10 · Lakehouse Table Formats

Delta Lake

A comprehensive Delta Lake course covering the transaction log, checkpoints, ACID semantics, protocol versions and table features, schema enforcement/evolution, MERGE/UPDATE/DELETE, deletion vectors, Change Data Feed, time travel, liquid clustering, optimization, Structured Streaming, Spark 4 integration, Delta Connect awareness, object storage, interoperability, governance, recovery, performance, and production lakehouse design.

Browse planned curriculum Back to all courses

32planned chapters

160reserved lesson paths

Advancedlearning level

Plannedcourse state

Coverage baselineDelta Lake 4.0.x baseline aligned with Apache Spark 4.0.x, covering the Delta transaction log, optimistic concurrency, protocol/table features, schema enforcement/evolution, MERGE and row-level changes, deletion vectors, liquid clustering, Change Data Feed, generated/identity columns, UniForm/interoperability concepts, Delta Connect preview awareness, streaming, maintenance, security, and production lakehouse operations

Course brief

Treat Delta as a transaction-log protocol over data files: understand exactly how JSON commits, checkpoints, actions, protocol/table features, optimistic conflict checks, and file-level rewrites produce ACID behavior before relying on higher-level optimization or streaming conveniences.

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

Read and reason about the Delta transaction log, JSON commit actions, Parquet checkpoints, protocol versions, table features, snapshots, and optimistic concurrency
Build reliable batch and streaming tables using schema enforcement/evolution, MERGE, UPDATE, DELETE, Change Data Feed, time travel, generated columns, and constraints
Tune data layout with partitioning, compaction, data skipping, deletion vectors and liquid clustering while measuring small-file and rewrite amplification
Integrate Delta with Spark 4, Structured Streaming, object storage and interoperability features while understanding Delta Connect and cross-engine capability boundaries
Operate secure production Delta tables with maintenance, retention, vacuum safety, observability, backup/recovery, governance, migration, and regression-tested upgrades

Complete planned syllabus

32 chapters · 160 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.

Chapter 1

Delta Lake Foundations: Lakehouse Motivation, Delta 4.0.x, Spark 4 Compatibility, Object Storage, and Reproducible Local Lab

5 lessons

Delta Lake Foundations: Lakehouse Motivation, Delta 4.0.x, Spark 4 Compatibility, Object Storage, and Reproducible Local Lab: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter01/Lesson1.html

Planned02

Delta Lake Foundations: Lakehouse Motivation, Delta 4.0.x, Spark 4 Compatibility, Object Storage, and Reproducible Local Lab: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter01/Lesson2.html

Planned03

Delta Lake Foundations: Lakehouse Motivation, Delta 4.0.x, Spark 4 Compatibility, Object Storage, and Reproducible Local Lab: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter01/Lesson3.html

Planned04

Delta Lake Foundations: Lakehouse Motivation, Delta 4.0.x, Spark 4 Compatibility, Object Storage, and Reproducible Local Lab: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter01/Lesson4.html

Planned05

Checkpoint Lab — Delta Lake Foundations: Lakehouse Motivation, Delta 4.0.x, Spark 4 Compatibility, Object Storage, and Reproducible Local Lab: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter01/Lesson5.html

Planned

Chapter 2

Transaction Log Fundamentals: _delta_log, Commit JSON Files, Versions, Atomic Publication, Snapshots, and Reader Reconstruction

5 lessons

Transaction Log Fundamentals: _delta_log, Commit JSON Files, Versions, Atomic Publication, Snapshots, and Reader Reconstruction: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter02/Lesson1.html

Planned02

Transaction Log Fundamentals: _delta_log, Commit JSON Files, Versions, Atomic Publication, Snapshots, and Reader Reconstruction: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter02/Lesson2.html

Planned03

Transaction Log Fundamentals: _delta_log, Commit JSON Files, Versions, Atomic Publication, Snapshots, and Reader Reconstruction: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter02/Lesson3.html

Planned04

Transaction Log Fundamentals: _delta_log, Commit JSON Files, Versions, Atomic Publication, Snapshots, and Reader Reconstruction: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter02/Lesson4.html

Planned05

Checkpoint Lab — Transaction Log Fundamentals: _delta_log, Commit JSON Files, Versions, Atomic Publication, Snapshots, and Reader Reconstruction: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter02/Lesson5.html

Planned

Chapter 3

Actions and Table State: add/remove/metaData/protocol/txn/commitInfo Actions, File Statistics, Tombstones, and Logical Snapshots

5 lessons

Actions and Table State: add/remove/metaData/protocol/txn/commitInfo Actions, File Statistics, Tombstones, and Logical Snapshots: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter03/Lesson1.html

Planned02

Actions and Table State: add/remove/metaData/protocol/txn/commitInfo Actions, File Statistics, Tombstones, and Logical Snapshots: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter03/Lesson2.html

Planned03

Actions and Table State: add/remove/metaData/protocol/txn/commitInfo Actions, File Statistics, Tombstones, and Logical Snapshots: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter03/Lesson3.html

Planned04

Actions and Table State: add/remove/metaData/protocol/txn/commitInfo Actions, File Statistics, Tombstones, and Logical Snapshots: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter03/Lesson4.html

Planned05

Checkpoint Lab — Actions and Table State: add/remove/metaData/protocol/txn/commitInfo Actions, File Statistics, Tombstones, and Logical Snapshots: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter03/Lesson5.html

Planned

Chapter 4

Checkpoints and Log Scaling: Parquet Checkpoints, Multipart/Modern Checkpoint Concepts, Log Replay, Retention, and Metadata Read Performance

5 lessons

Checkpoints and Log Scaling: Parquet Checkpoints, Multipart/Modern Checkpoint Concepts, Log Replay, Retention, and Metadata Read Performance: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter04/Lesson1.html

Planned02

Checkpoints and Log Scaling: Parquet Checkpoints, Multipart/Modern Checkpoint Concepts, Log Replay, Retention, and Metadata Read Performance: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter04/Lesson2.html

Planned03

Checkpoints and Log Scaling: Parquet Checkpoints, Multipart/Modern Checkpoint Concepts, Log Replay, Retention, and Metadata Read Performance: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter04/Lesson3.html

Planned04

Checkpoints and Log Scaling: Parquet Checkpoints, Multipart/Modern Checkpoint Concepts, Log Replay, Retention, and Metadata Read Performance: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter04/Lesson4.html

Planned05

Checkpoint Lab — Checkpoints and Log Scaling: Parquet Checkpoints, Multipart/Modern Checkpoint Concepts, Log Replay, Retention, and Metadata Read Performance: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter04/Lesson5.html

Planned

Chapter 5

ACID and Optimistic Concurrency: Snapshot Isolation, Write Conflicts, Validation, Retries, Concurrent Appends, and Conflict Matrices

5 lessons

ACID and Optimistic Concurrency: Snapshot Isolation, Write Conflicts, Validation, Retries, Concurrent Appends, and Conflict Matrices: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter05/Lesson1.html

Planned02

ACID and Optimistic Concurrency: Snapshot Isolation, Write Conflicts, Validation, Retries, Concurrent Appends, and Conflict Matrices: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter05/Lesson2.html

Planned03

ACID and Optimistic Concurrency: Snapshot Isolation, Write Conflicts, Validation, Retries, Concurrent Appends, and Conflict Matrices: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter05/Lesson3.html

Planned04

ACID and Optimistic Concurrency: Snapshot Isolation, Write Conflicts, Validation, Retries, Concurrent Appends, and Conflict Matrices: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter05/Lesson4.html

Planned05

Checkpoint Lab — ACID and Optimistic Concurrency: Snapshot Isolation, Write Conflicts, Validation, Retries, Concurrent Appends, and Conflict Matrices: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter05/Lesson5.html

Planned

Chapter 6

Protocol Versions and Table Features: Reader/Writer Protocols, Feature Negotiation, Irreversible Upgrades, Compatibility, and Fleet Rollouts

5 lessons

Protocol Versions and Table Features: Reader/Writer Protocols, Feature Negotiation, Irreversible Upgrades, Compatibility, and Fleet Rollouts: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter06/Lesson1.html

Planned02

Protocol Versions and Table Features: Reader/Writer Protocols, Feature Negotiation, Irreversible Upgrades, Compatibility, and Fleet Rollouts: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter06/Lesson2.html

Planned03

Protocol Versions and Table Features: Reader/Writer Protocols, Feature Negotiation, Irreversible Upgrades, Compatibility, and Fleet Rollouts: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter06/Lesson3.html

Planned04

Protocol Versions and Table Features: Reader/Writer Protocols, Feature Negotiation, Irreversible Upgrades, Compatibility, and Fleet Rollouts: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter06/Lesson4.html

Planned05

Checkpoint Lab — Protocol Versions and Table Features: Reader/Writer Protocols, Feature Negotiation, Irreversible Upgrades, Compatibility, and Fleet Rollouts: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter06/Lesson5.html

Planned

Chapter 7

Schema Enforcement: Write-Time Validation, Type Compatibility, Nested Structures, Nullability, Data Contracts, and Preventing Silent Drift

5 lessons

Schema Enforcement: Write-Time Validation, Type Compatibility, Nested Structures, Nullability, Data Contracts, and Preventing Silent Drift: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter07/Lesson1.html

Planned02

Schema Enforcement: Write-Time Validation, Type Compatibility, Nested Structures, Nullability, Data Contracts, and Preventing Silent Drift: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter07/Lesson2.html

Planned03

Schema Enforcement: Write-Time Validation, Type Compatibility, Nested Structures, Nullability, Data Contracts, and Preventing Silent Drift: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter07/Lesson3.html

Planned04

Schema Enforcement: Write-Time Validation, Type Compatibility, Nested Structures, Nullability, Data Contracts, and Preventing Silent Drift: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter07/Lesson4.html

Planned05

Checkpoint Lab — Schema Enforcement: Write-Time Validation, Type Compatibility, Nested Structures, Nullability, Data Contracts, and Preventing Silent Drift: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter07/Lesson5.html

Planned

Chapter 8

Schema Evolution: mergeSchema/Auto-Merge Concepts, ADD/RENAME/DROP Columns, Column Mapping, Nested Evolution, and Safe Consumer Migration

5 lessons

Schema Evolution: mergeSchema/Auto-Merge Concepts, ADD/RENAME/DROP Columns, Column Mapping, Nested Evolution, and Safe Consumer Migration: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter08/Lesson1.html

Planned02

Schema Evolution: mergeSchema/Auto-Merge Concepts, ADD/RENAME/DROP Columns, Column Mapping, Nested Evolution, and Safe Consumer Migration: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter08/Lesson2.html

Planned03

Schema Evolution: mergeSchema/Auto-Merge Concepts, ADD/RENAME/DROP Columns, Column Mapping, Nested Evolution, and Safe Consumer Migration: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter08/Lesson3.html

Planned04

Schema Evolution: mergeSchema/Auto-Merge Concepts, ADD/RENAME/DROP Columns, Column Mapping, Nested Evolution, and Safe Consumer Migration: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter08/Lesson4.html

Planned05

Checkpoint Lab — Schema Evolution: mergeSchema/Auto-Merge Concepts, ADD/RENAME/DROP Columns, Column Mapping, Nested Evolution, and Safe Consumer Migration: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter08/Lesson5.html

Planned

Chapter 9

Table Creation and DDL: SQL/DataFrame APIs, Managed vs External Concepts, Properties, Comments, Constraints, Defaults, and Generated Metadata

5 lessons

Table Creation and DDL: SQL/DataFrame APIs, Managed vs External Concepts, Properties, Comments, Constraints, Defaults, and Generated Metadata: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter09/Lesson1.html

Planned02

Table Creation and DDL: SQL/DataFrame APIs, Managed vs External Concepts, Properties, Comments, Constraints, Defaults, and Generated Metadata: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter09/Lesson2.html

Planned03

Table Creation and DDL: SQL/DataFrame APIs, Managed vs External Concepts, Properties, Comments, Constraints, Defaults, and Generated Metadata: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter09/Lesson3.html

Planned04

Table Creation and DDL: SQL/DataFrame APIs, Managed vs External Concepts, Properties, Comments, Constraints, Defaults, and Generated Metadata: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter09/Lesson4.html

Planned05

Checkpoint Lab — Table Creation and DDL: SQL/DataFrame APIs, Managed vs External Concepts, Properties, Comments, Constraints, Defaults, and Generated Metadata: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter09/Lesson5.html

Planned

Chapter 10

DML Fundamentals: INSERT, OVERWRITE, REPLACE WHERE, DELETE, UPDATE, and File-Rewrite Consequences

5 lessons

DML Fundamentals: INSERT, OVERWRITE, REPLACE WHERE, DELETE, UPDATE, and File-Rewrite Consequences: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter10/Lesson1.html

Planned02

DML Fundamentals: INSERT, OVERWRITE, REPLACE WHERE, DELETE, UPDATE, and File-Rewrite Consequences: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter10/Lesson2.html

Planned03

DML Fundamentals: INSERT, OVERWRITE, REPLACE WHERE, DELETE, UPDATE, and File-Rewrite Consequences: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter10/Lesson3.html

Planned04

DML Fundamentals: INSERT, OVERWRITE, REPLACE WHERE, DELETE, UPDATE, and File-Rewrite Consequences: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter10/Lesson4.html

Planned05

Checkpoint Lab — DML Fundamentals: INSERT, OVERWRITE, REPLACE WHERE, DELETE, UPDATE, and File-Rewrite Consequences: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter10/Lesson5.html

Planned

Chapter 11

MERGE Deep Dive: Matched/Not-Matched Clauses, Upserts, Deduplication, CDC Inputs, Ambiguous Matches, and Performance Tuning

5 lessons

MERGE Deep Dive: Matched/Not-Matched Clauses, Upserts, Deduplication, CDC Inputs, Ambiguous Matches, and Performance Tuning: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter11/Lesson1.html

Planned02

MERGE Deep Dive: Matched/Not-Matched Clauses, Upserts, Deduplication, CDC Inputs, Ambiguous Matches, and Performance Tuning: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter11/Lesson2.html

Planned03

MERGE Deep Dive: Matched/Not-Matched Clauses, Upserts, Deduplication, CDC Inputs, Ambiguous Matches, and Performance Tuning: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter11/Lesson3.html

Planned04

MERGE Deep Dive: Matched/Not-Matched Clauses, Upserts, Deduplication, CDC Inputs, Ambiguous Matches, and Performance Tuning: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter11/Lesson4.html

Planned05

Checkpoint Lab — MERGE Deep Dive: Matched/Not-Matched Clauses, Upserts, Deduplication, CDC Inputs, Ambiguous Matches, and Performance Tuning: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter11/Lesson5.html

Planned

Chapter 12

Deletion Vectors: Row-Level Delete Representation, Reader Semantics, Rewrite Avoidance, Compaction/Purge Behavior, Compatibility, and Operational Tradeoffs

5 lessons

Deletion Vectors: Row-Level Delete Representation, Reader Semantics, Rewrite Avoidance, Compaction/Purge Behavior, Compatibility, and Operational Tradeoffs: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter12/Lesson1.html

Planned02

Deletion Vectors: Row-Level Delete Representation, Reader Semantics, Rewrite Avoidance, Compaction/Purge Behavior, Compatibility, and Operational Tradeoffs: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter12/Lesson2.html

Planned03

Deletion Vectors: Row-Level Delete Representation, Reader Semantics, Rewrite Avoidance, Compaction/Purge Behavior, Compatibility, and Operational Tradeoffs: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter12/Lesson3.html

Planned04

Deletion Vectors: Row-Level Delete Representation, Reader Semantics, Rewrite Avoidance, Compaction/Purge Behavior, Compatibility, and Operational Tradeoffs: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter12/Lesson4.html

Planned05

Checkpoint Lab — Deletion Vectors: Row-Level Delete Representation, Reader Semantics, Rewrite Avoidance, Compaction/Purge Behavior, Compatibility, and Operational Tradeoffs: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter12/Lesson5.html

Planned

Chapter 13

Change Data Feed: Enablement, Change Types, Versions/Timestamps, Batch and Streaming Reads, Retention, CDC Consumers, and Replay Boundaries

5 lessons

Change Data Feed: Enablement, Change Types, Versions/Timestamps, Batch and Streaming Reads, Retention, CDC Consumers, and Replay Boundaries: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter13/Lesson1.html

Planned02

Change Data Feed: Enablement, Change Types, Versions/Timestamps, Batch and Streaming Reads, Retention, CDC Consumers, and Replay Boundaries: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter13/Lesson2.html

Planned03

Change Data Feed: Enablement, Change Types, Versions/Timestamps, Batch and Streaming Reads, Retention, CDC Consumers, and Replay Boundaries: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter13/Lesson3.html

Planned04

Change Data Feed: Enablement, Change Types, Versions/Timestamps, Batch and Streaming Reads, Retention, CDC Consumers, and Replay Boundaries: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter13/Lesson4.html

Planned05

Checkpoint Lab — Change Data Feed: Enablement, Change Types, Versions/Timestamps, Batch and Streaming Reads, Retention, CDC Consumers, and Replay Boundaries: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter13/Lesson5.html

Planned

Chapter 14

Time Travel and Restore: VERSION/TIMESTAMP Reads, History, Restore Semantics, Reproducible Analytics, Retention Dependencies, and Audit Use Cases

5 lessons

Time Travel and Restore: VERSION/TIMESTAMP Reads, History, Restore Semantics, Reproducible Analytics, Retention Dependencies, and Audit Use Cases: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter14/Lesson1.html

Planned02

Time Travel and Restore: VERSION/TIMESTAMP Reads, History, Restore Semantics, Reproducible Analytics, Retention Dependencies, and Audit Use Cases: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter14/Lesson2.html

Planned03

Time Travel and Restore: VERSION/TIMESTAMP Reads, History, Restore Semantics, Reproducible Analytics, Retention Dependencies, and Audit Use Cases: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter14/Lesson3.html

Planned04

Time Travel and Restore: VERSION/TIMESTAMP Reads, History, Restore Semantics, Reproducible Analytics, Retention Dependencies, and Audit Use Cases: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter14/Lesson4.html

Planned05

Checkpoint Lab — Time Travel and Restore: VERSION/TIMESTAMP Reads, History, Restore Semantics, Reproducible Analytics, Retention Dependencies, and Audit Use Cases: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter14/Lesson5.html

Planned

Chapter 15

Constraints, Generated Columns, Identity Columns, and Defaults: Data Quality Rules, Write Semantics, Concurrency Implications, and Modeling Patterns

5 lessons

Constraints, Generated Columns, Identity Columns, and Defaults: Data Quality Rules, Write Semantics, Concurrency Implications, and Modeling Patterns: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter15/Lesson1.html

Planned02

Constraints, Generated Columns, Identity Columns, and Defaults: Data Quality Rules, Write Semantics, Concurrency Implications, and Modeling Patterns: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter15/Lesson2.html

Planned03

Constraints, Generated Columns, Identity Columns, and Defaults: Data Quality Rules, Write Semantics, Concurrency Implications, and Modeling Patterns: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter15/Lesson3.html

Planned04

Constraints, Generated Columns, Identity Columns, and Defaults: Data Quality Rules, Write Semantics, Concurrency Implications, and Modeling Patterns: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter15/Lesson4.html

Planned05

Checkpoint Lab — Constraints, Generated Columns, Identity Columns, and Defaults: Data Quality Rules, Write Semantics, Concurrency Implications, and Modeling Patterns: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter15/Lesson5.html

Planned

Chapter 16

Partitioning and Data Skipping: Partition Columns, File Statistics, Predicate Pruning, Cardinality, Partition Evolution Constraints, and Layout Tradeoffs

5 lessons

Partitioning and Data Skipping: Partition Columns, File Statistics, Predicate Pruning, Cardinality, Partition Evolution Constraints, and Layout Tradeoffs: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter16/Lesson1.html

Planned02

Partitioning and Data Skipping: Partition Columns, File Statistics, Predicate Pruning, Cardinality, Partition Evolution Constraints, and Layout Tradeoffs: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter16/Lesson2.html

Planned03

Partitioning and Data Skipping: Partition Columns, File Statistics, Predicate Pruning, Cardinality, Partition Evolution Constraints, and Layout Tradeoffs: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter16/Lesson3.html

Planned04

Partitioning and Data Skipping: Partition Columns, File Statistics, Predicate Pruning, Cardinality, Partition Evolution Constraints, and Layout Tradeoffs: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter16/Lesson4.html

Planned05

Checkpoint Lab — Partitioning and Data Skipping: Partition Columns, File Statistics, Predicate Pruning, Cardinality, Partition Evolution Constraints, and Layout Tradeoffs: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter16/Lesson5.html

Planned

Chapter 17

Liquid Clustering: Clustering Keys, Incremental Reorganization, Evolving Layout, Compatibility, Key Selection, and Comparison with Static Partitioning

5 lessons

Liquid Clustering: Clustering Keys, Incremental Reorganization, Evolving Layout, Compatibility, Key Selection, and Comparison with Static Partitioning: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter17/Lesson1.html

Planned02

Liquid Clustering: Clustering Keys, Incremental Reorganization, Evolving Layout, Compatibility, Key Selection, and Comparison with Static Partitioning: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter17/Lesson2.html

Planned03

Liquid Clustering: Clustering Keys, Incremental Reorganization, Evolving Layout, Compatibility, Key Selection, and Comparison with Static Partitioning: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter17/Lesson3.html

Planned04

Liquid Clustering: Clustering Keys, Incremental Reorganization, Evolving Layout, Compatibility, Key Selection, and Comparison with Static Partitioning: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter17/Lesson4.html

Planned05

Checkpoint Lab — Liquid Clustering: Clustering Keys, Incremental Reorganization, Evolving Layout, Compatibility, Key Selection, and Comparison with Static Partitioning: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter17/Lesson5.html

Planned

Chapter 18

Compaction and Optimization: Small Files, OPTIMIZE-Like Patterns, Bin Packing, Z-Ordering Awareness, Auto-Compaction Concepts, and Rewrite Economics

5 lessons

Compaction and Optimization: Small Files, OPTIMIZE-Like Patterns, Bin Packing, Z-Ordering Awareness, Auto-Compaction Concepts, and Rewrite Economics: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter18/Lesson1.html

Planned02

Compaction and Optimization: Small Files, OPTIMIZE-Like Patterns, Bin Packing, Z-Ordering Awareness, Auto-Compaction Concepts, and Rewrite Economics: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter18/Lesson2.html

Planned03

Compaction and Optimization: Small Files, OPTIMIZE-Like Patterns, Bin Packing, Z-Ordering Awareness, Auto-Compaction Concepts, and Rewrite Economics: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter18/Lesson3.html

Planned04

Compaction and Optimization: Small Files, OPTIMIZE-Like Patterns, Bin Packing, Z-Ordering Awareness, Auto-Compaction Concepts, and Rewrite Economics: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter18/Lesson4.html

Planned05

Checkpoint Lab — Compaction and Optimization: Small Files, OPTIMIZE-Like Patterns, Bin Packing, Z-Ordering Awareness, Auto-Compaction Concepts, and Rewrite Economics: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter18/Lesson5.html

Planned

Chapter 19

VACUUM and Retention Safety: Tombstones, Deleted File Retention, Concurrent Readers, Streaming Lag, Time Travel, Dry Runs, and Data-Loss Prevention

5 lessons

VACUUM and Retention Safety: Tombstones, Deleted File Retention, Concurrent Readers, Streaming Lag, Time Travel, Dry Runs, and Data-Loss Prevention: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter19/Lesson1.html

Planned02

VACUUM and Retention Safety: Tombstones, Deleted File Retention, Concurrent Readers, Streaming Lag, Time Travel, Dry Runs, and Data-Loss Prevention: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter19/Lesson2.html

Planned03

VACUUM and Retention Safety: Tombstones, Deleted File Retention, Concurrent Readers, Streaming Lag, Time Travel, Dry Runs, and Data-Loss Prevention: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter19/Lesson3.html

Planned04

VACUUM and Retention Safety: Tombstones, Deleted File Retention, Concurrent Readers, Streaming Lag, Time Travel, Dry Runs, and Data-Loss Prevention: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter19/Lesson4.html

Planned05

Checkpoint Lab — VACUUM and Retention Safety: Tombstones, Deleted File Retention, Concurrent Readers, Streaming Lag, Time Travel, Dry Runs, and Data-Loss Prevention: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter19/Lesson5.html

Planned

Chapter 20

Structured Streaming Reads: Source Offsets, Snapshot vs Incremental Processing, Schema Changes, Rate Controls, CDF Alternatives, and Recovery

5 lessons

Structured Streaming Reads: Source Offsets, Snapshot vs Incremental Processing, Schema Changes, Rate Controls, CDF Alternatives, and Recovery: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter20/Lesson1.html

Planned02

Structured Streaming Reads: Source Offsets, Snapshot vs Incremental Processing, Schema Changes, Rate Controls, CDF Alternatives, and Recovery: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter20/Lesson2.html

Planned03

Structured Streaming Reads: Source Offsets, Snapshot vs Incremental Processing, Schema Changes, Rate Controls, CDF Alternatives, and Recovery: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter20/Lesson3.html

Planned04

Structured Streaming Reads: Source Offsets, Snapshot vs Incremental Processing, Schema Changes, Rate Controls, CDF Alternatives, and Recovery: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter20/Lesson4.html

Planned05

Checkpoint Lab — Structured Streaming Reads: Source Offsets, Snapshot vs Incremental Processing, Schema Changes, Rate Controls, CDF Alternatives, and Recovery: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter20/Lesson5.html

Planned

Chapter 21

Structured Streaming Writes: Exactly-Once Boundaries, Transaction IDs, Checkpoints, Output Modes, MERGE in foreachBatch, Idempotency, and Replay

5 lessons

Structured Streaming Writes: Exactly-Once Boundaries, Transaction IDs, Checkpoints, Output Modes, MERGE in foreachBatch, Idempotency, and Replay: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter21/Lesson1.html

Planned02

Structured Streaming Writes: Exactly-Once Boundaries, Transaction IDs, Checkpoints, Output Modes, MERGE in foreachBatch, Idempotency, and Replay: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter21/Lesson2.html

Planned03

Structured Streaming Writes: Exactly-Once Boundaries, Transaction IDs, Checkpoints, Output Modes, MERGE in foreachBatch, Idempotency, and Replay: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter21/Lesson3.html

Planned04

Structured Streaming Writes: Exactly-Once Boundaries, Transaction IDs, Checkpoints, Output Modes, MERGE in foreachBatch, Idempotency, and Replay: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter21/Lesson4.html

Planned05

Checkpoint Lab — Structured Streaming Writes: Exactly-Once Boundaries, Transaction IDs, Checkpoints, Output Modes, MERGE in foreachBatch, Idempotency, and Replay: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter21/Lesson5.html

Planned

Chapter 22

Spark 4 Integration: SQL/DataFrame APIs, Session Extensions, Catalogs, DML, Table Properties, Query Planning, and Version Compatibility

5 lessons

Spark 4 Integration: SQL/DataFrame APIs, Session Extensions, Catalogs, DML, Table Properties, Query Planning, and Version Compatibility: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter22/Lesson1.html

Planned02

Spark 4 Integration: SQL/DataFrame APIs, Session Extensions, Catalogs, DML, Table Properties, Query Planning, and Version Compatibility: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter22/Lesson2.html

Planned03

Spark 4 Integration: SQL/DataFrame APIs, Session Extensions, Catalogs, DML, Table Properties, Query Planning, and Version Compatibility: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter22/Lesson3.html

Planned04

Spark 4 Integration: SQL/DataFrame APIs, Session Extensions, Catalogs, DML, Table Properties, Query Planning, and Version Compatibility: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter22/Lesson4.html

Planned05

Checkpoint Lab — Spark 4 Integration: SQL/DataFrame APIs, Session Extensions, Catalogs, DML, Table Properties, Query Planning, and Version Compatibility: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter22/Lesson5.html

Planned

Chapter 23

Delta Connect: Spark Connect Architecture, Client/Server Separation, Delta 4 Preview Support, Feature Gaps, Packaging, and Why Preview Is Not Production

5 lessons

Delta Connect: Spark Connect Architecture, Client/Server Separation, Delta 4 Preview Support, Feature Gaps, Packaging, and Why Preview Is Not Production: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter23/Lesson1.html

Planned02

Delta Connect: Spark Connect Architecture, Client/Server Separation, Delta 4 Preview Support, Feature Gaps, Packaging, and Why Preview Is Not Production: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter23/Lesson2.html

Planned03

Delta Connect: Spark Connect Architecture, Client/Server Separation, Delta 4 Preview Support, Feature Gaps, Packaging, and Why Preview Is Not Production: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter23/Lesson3.html

Planned04

Delta Connect: Spark Connect Architecture, Client/Server Separation, Delta 4 Preview Support, Feature Gaps, Packaging, and Why Preview Is Not Production: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter23/Lesson4.html

Planned05

Checkpoint Lab — Delta Connect: Spark Connect Architecture, Client/Server Separation, Delta 4 Preview Support, Feature Gaps, Packaging, and Why Preview Is Not Production: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter23/Lesson5.html

Planned

Chapter 24

Interoperability and UniForm Concepts: Iceberg-Compatible Metadata, Cross-Engine Reads, Protocol Boundaries, Generated Metadata, and Consistency Expectations

5 lessons

Interoperability and UniForm Concepts: Iceberg-Compatible Metadata, Cross-Engine Reads, Protocol Boundaries, Generated Metadata, and Consistency Expectations: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter24/Lesson1.html

Planned02

Interoperability and UniForm Concepts: Iceberg-Compatible Metadata, Cross-Engine Reads, Protocol Boundaries, Generated Metadata, and Consistency Expectations: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter24/Lesson2.html

Planned03

Interoperability and UniForm Concepts: Iceberg-Compatible Metadata, Cross-Engine Reads, Protocol Boundaries, Generated Metadata, and Consistency Expectations: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter24/Lesson3.html

Planned04

Interoperability and UniForm Concepts: Iceberg-Compatible Metadata, Cross-Engine Reads, Protocol Boundaries, Generated Metadata, and Consistency Expectations: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter24/Lesson4.html

Planned05

Checkpoint Lab — Interoperability and UniForm Concepts: Iceberg-Compatible Metadata, Cross-Engine Reads, Protocol Boundaries, Generated Metadata, and Consistency Expectations: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter24/Lesson5.html

Planned

Chapter 25

Object Storage and Cloud Semantics: S3/GCS/Azure Concepts, Log-Store Coordination, Credentials, Multipart I/O, Listing, Encryption, and Region Design

5 lessons

Object Storage and Cloud Semantics: S3/GCS/Azure Concepts, Log-Store Coordination, Credentials, Multipart I/O, Listing, Encryption, and Region Design: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter25/Lesson1.html

Planned02

Object Storage and Cloud Semantics: S3/GCS/Azure Concepts, Log-Store Coordination, Credentials, Multipart I/O, Listing, Encryption, and Region Design: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter25/Lesson2.html

Planned03

Object Storage and Cloud Semantics: S3/GCS/Azure Concepts, Log-Store Coordination, Credentials, Multipart I/O, Listing, Encryption, and Region Design: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter25/Lesson3.html

Planned04

Object Storage and Cloud Semantics: S3/GCS/Azure Concepts, Log-Store Coordination, Credentials, Multipart I/O, Listing, Encryption, and Region Design: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter25/Lesson4.html

Planned05

Checkpoint Lab — Object Storage and Cloud Semantics: S3/GCS/Azure Concepts, Log-Store Coordination, Credentials, Multipart I/O, Listing, Encryption, and Region Design: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter25/Lesson5.html

Planned

Chapter 26

Performance Engineering: File Size, Clustering, Stats, Join/Filter Patterns, Metadata Cost, DML Rewrite Amplification, Caching, and Benchmark Methodology

5 lessons

Performance Engineering: File Size, Clustering, Stats, Join/Filter Patterns, Metadata Cost, DML Rewrite Amplification, Caching, and Benchmark Methodology: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter26/Lesson1.html

Planned02

Performance Engineering: File Size, Clustering, Stats, Join/Filter Patterns, Metadata Cost, DML Rewrite Amplification, Caching, and Benchmark Methodology: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter26/Lesson2.html

Planned03

Performance Engineering: File Size, Clustering, Stats, Join/Filter Patterns, Metadata Cost, DML Rewrite Amplification, Caching, and Benchmark Methodology: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter26/Lesson3.html

Planned04

Performance Engineering: File Size, Clustering, Stats, Join/Filter Patterns, Metadata Cost, DML Rewrite Amplification, Caching, and Benchmark Methodology: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter26/Lesson4.html

Planned05

Checkpoint Lab — Performance Engineering: File Size, Clustering, Stats, Join/Filter Patterns, Metadata Cost, DML Rewrite Amplification, Caching, and Benchmark Methodology: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter26/Lesson5.html

Planned

Chapter 27

Security and Governance: Storage IAM, Catalog/Metastore Authorization, Encryption, Secrets, Audit Logs, Row/Column Policy Integration, and Least Privilege

5 lessons

Security and Governance: Storage IAM, Catalog/Metastore Authorization, Encryption, Secrets, Audit Logs, Row/Column Policy Integration, and Least Privilege: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter27/Lesson1.html

Planned02

Security and Governance: Storage IAM, Catalog/Metastore Authorization, Encryption, Secrets, Audit Logs, Row/Column Policy Integration, and Least Privilege: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter27/Lesson2.html

Planned03

Security and Governance: Storage IAM, Catalog/Metastore Authorization, Encryption, Secrets, Audit Logs, Row/Column Policy Integration, and Least Privilege: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter27/Lesson3.html

Planned04

Security and Governance: Storage IAM, Catalog/Metastore Authorization, Encryption, Secrets, Audit Logs, Row/Column Policy Integration, and Least Privilege: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter27/Lesson4.html

Planned05

Checkpoint Lab — Security and Governance: Storage IAM, Catalog/Metastore Authorization, Encryption, Secrets, Audit Logs, Row/Column Policy Integration, and Least Privilege: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter27/Lesson5.html

Planned

Chapter 28

Observability and Troubleshooting: DESCRIBE HISTORY/DETAIL, Log Inspection, Commit Conflicts, Schema Errors, Missing Files, Slow Reads, and Corruption Triage

5 lessons

Observability and Troubleshooting: DESCRIBE HISTORY/DETAIL, Log Inspection, Commit Conflicts, Schema Errors, Missing Files, Slow Reads, and Corruption Triage: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter28/Lesson1.html

Planned02

Observability and Troubleshooting: DESCRIBE HISTORY/DETAIL, Log Inspection, Commit Conflicts, Schema Errors, Missing Files, Slow Reads, and Corruption Triage: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter28/Lesson2.html

Planned03

Observability and Troubleshooting: DESCRIBE HISTORY/DETAIL, Log Inspection, Commit Conflicts, Schema Errors, Missing Files, Slow Reads, and Corruption Triage: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter28/Lesson3.html

Planned04

Observability and Troubleshooting: DESCRIBE HISTORY/DETAIL, Log Inspection, Commit Conflicts, Schema Errors, Missing Files, Slow Reads, and Corruption Triage: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter28/Lesson4.html

Planned05

Checkpoint Lab — Observability and Troubleshooting: DESCRIBE HISTORY/DETAIL, Log Inspection, Commit Conflicts, Schema Errors, Missing Files, Slow Reads, and Corruption Triage: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter28/Lesson5.html

Planned

Chapter 29

Migration and Upgrade Planning: Parquet-to-Delta Conversion, Protocol/Table Feature Upgrades, Spark Compatibility, Rollback Limits, and Regression Gates

5 lessons

Migration and Upgrade Planning: Parquet-to-Delta Conversion, Protocol/Table Feature Upgrades, Spark Compatibility, Rollback Limits, and Regression Gates: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter29/Lesson1.html

Planned02

Migration and Upgrade Planning: Parquet-to-Delta Conversion, Protocol/Table Feature Upgrades, Spark Compatibility, Rollback Limits, and Regression Gates: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter29/Lesson2.html

Planned03

Migration and Upgrade Planning: Parquet-to-Delta Conversion, Protocol/Table Feature Upgrades, Spark Compatibility, Rollback Limits, and Regression Gates: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter29/Lesson3.html

Planned04

Migration and Upgrade Planning: Parquet-to-Delta Conversion, Protocol/Table Feature Upgrades, Spark Compatibility, Rollback Limits, and Regression Gates: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter29/Lesson4.html

Planned05

Checkpoint Lab — Migration and Upgrade Planning: Parquet-to-Delta Conversion, Protocol/Table Feature Upgrades, Spark Compatibility, Rollback Limits, and Regression Gates: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter29/Lesson5.html

Planned

Chapter 30

Backup, Replication, and Disaster Recovery: Object Versioning, Cross-Region Copies, Catalog/Metastore Recovery, Log Consistency, Restore Testing, and RPO/RTO

5 lessons

Backup, Replication, and Disaster Recovery: Object Versioning, Cross-Region Copies, Catalog/Metastore Recovery, Log Consistency, Restore Testing, and RPO/RTO: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter30/Lesson1.html

Planned02

Backup, Replication, and Disaster Recovery: Object Versioning, Cross-Region Copies, Catalog/Metastore Recovery, Log Consistency, Restore Testing, and RPO/RTO: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter30/Lesson2.html

Planned03

Backup, Replication, and Disaster Recovery: Object Versioning, Cross-Region Copies, Catalog/Metastore Recovery, Log Consistency, Restore Testing, and RPO/RTO: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter30/Lesson3.html

Planned04

Backup, Replication, and Disaster Recovery: Object Versioning, Cross-Region Copies, Catalog/Metastore Recovery, Log Consistency, Restore Testing, and RPO/RTO: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter30/Lesson4.html

Planned05

Checkpoint Lab — Backup, Replication, and Disaster Recovery: Object Versioning, Cross-Region Copies, Catalog/Metastore Recovery, Log Consistency, Restore Testing, and RPO/RTO: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter30/Lesson5.html

Planned

Chapter 31

Architecture Patterns: Medallion Layers, CDC, Streaming Tables, Backfills, Reprocessing, Multi-Tenant Lakehouses, and Cost-Aware Data Products

5 lessons

Architecture Patterns: Medallion Layers, CDC, Streaming Tables, Backfills, Reprocessing, Multi-Tenant Lakehouses, and Cost-Aware Data Products: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter31/Lesson1.html

Planned02

Architecture Patterns: Medallion Layers, CDC, Streaming Tables, Backfills, Reprocessing, Multi-Tenant Lakehouses, and Cost-Aware Data Products: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter31/Lesson2.html

Planned03

Architecture Patterns: Medallion Layers, CDC, Streaming Tables, Backfills, Reprocessing, Multi-Tenant Lakehouses, and Cost-Aware Data Products: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter31/Lesson3.html

Planned04

Architecture Patterns: Medallion Layers, CDC, Streaming Tables, Backfills, Reprocessing, Multi-Tenant Lakehouses, and Cost-Aware Data Products: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter31/Lesson4.html

Planned05

Checkpoint Lab — Architecture Patterns: Medallion Layers, CDC, Streaming Tables, Backfills, Reprocessing, Multi-Tenant Lakehouses, and Cost-Aware Data Products: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter31/Lesson5.html

Planned

Chapter 32

Production Capstone: Build, Stream, Merge, Cluster, Optimize, Secure, Observe, Break, Recover, and Upgrade a Delta Lake 4.0 Platform

5 lessons

Production Capstone: Build, Stream, Merge, Cluster, Optimize, Secure, Observe, Break, Recover, and Upgrade a Delta Lake 4.0 Platform: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter32/Lesson1.html

Planned02

Production Capstone: Build, Stream, Merge, Cluster, Optimize, Secure, Observe, Break, Recover, and Upgrade a Delta Lake 4.0 Platform: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter32/Lesson2.html

Planned03

Production Capstone: Build, Stream, Merge, Cluster, Optimize, Secure, Observe, Break, Recover, and Upgrade a Delta Lake 4.0 Platform: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter32/Lesson3.html

Planned04

Production Capstone: Build, Stream, Merge, Cluster, Optimize, Secure, Observe, Break, Recover, and Upgrade a Delta Lake 4.0 Platform: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter32/Lesson4.html

Planned05

Checkpoint Lab — Production Capstone: Build, Stream, Merge, Cluster, Optimize, Secure, Observe, Break, Recover, and Upgrade a Delta Lake 4.0 Platform: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter32/Lesson5.html

Planned