Curriculum planned

Stage 11 · Cloud & Managed Data Platforms

Google BigQuery

A comprehensive Google BigQuery course covering GoogleSQL, datasets/tables/views, nested and repeated data, partitioning/clustering, ingestion/export, external and BigLake data, query plans and optimization, materialized views, BI Engine, reservations and workload management, scripting/procedures, geospatial, BigQuery ML, AI functions, embeddings and vector/hybrid search, continuous queries, graph analytics, security/governance, sharing, disaster recovery, APIs, observability, migration, and FinOps.

Browse planned curriculum Back to all courses

38planned chapters

190reserved lesson paths

Intermediate → Advancedlearning level

Plannedcourse state

Coverage baselineCurrent Google BigQuery managed-service baseline through mid-2026, covering GoogleSQL, serverless storage/compute, partitioning and clustering, nested/repeated data, BigLake and external data, ingestion and pipelines, continuous queries, materialized views, BI Engine, reservations/workload management, BigQuery ML and AI functions, autonomous embeddings, vector/hybrid search, BigQuery Graph/GQL awareness, governance tags, sharing, managed disaster recovery, observability, and cost engineering

Course brief

Learn BigQuery as a managed distributed analytical system whose primary engineering constraints are bytes scanned, slots, data layout, query stages, governance boundaries, and workload isolation—not server tuning—then use those signals to design reliable SQL, ML, streaming, and AI workloads.

A comprehensive Google BigQuery course covering GoogleSQL, datasets/tables/views, nested and repeated data, partitioning/clustering, ingestion/export, external and BigLake data, query plans and optimization, materialized views, BI Engine, reservations and workload management, scripting/procedures, geospatial, BigQuery ML, AI functions, embeddings and vector/hybrid search, continuous queries, graph analytics, security/governance, sharing, disaster recovery, APIs, observability, migration, and FinOps.

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

Write advanced GoogleSQL over structured, nested and repeated data using arrays, structs, windows, scripting, procedures, UDFs, and reproducible analytical patterns
Design partitioned/clustered tables, ingestion and external/BigLake patterns, materialized views, BI acceleration, continuous queries, and workload isolation around measurable query plans
Build BigQuery ML and modern AI/search workflows with vector indexes, autonomous embeddings, hybrid search and current AI functions while distinguishing GA from preview capabilities
Secure and govern BigQuery with IAM, authorized views/routines, row/column controls, governance tags, masking, audit logs, sharing and data-residency/DR considerations
Control cost and reliability through reservations, autoscaling/fluid scaling concepts, quotas, monitoring, slot/bytes analysis, migration tooling, CI/CD, and production runbooks

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

BigQuery Foundations: Serverless Analytical Architecture, Projects/Datasets/Tables, Regions, Free-Tier Awareness, and First GoogleSQL Lab

5 lessons

01

BigQuery Foundations: Serverless Analytical Architecture, Projects/Datasets/Tables, Regions, Free-Tier Awareness, and First GoogleSQL Lab: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter01/Lesson1.html

Planned02

BigQuery Foundations: Serverless Analytical Architecture, Projects/Datasets/Tables, Regions, Free-Tier Awareness, and First GoogleSQL Lab: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter01/Lesson2.html

Planned03

BigQuery Foundations: Serverless Analytical Architecture, Projects/Datasets/Tables, Regions, Free-Tier Awareness, and First GoogleSQL Lab: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter01/Lesson3.html

Planned04

BigQuery Foundations: Serverless Analytical Architecture, Projects/Datasets/Tables, Regions, Free-Tier Awareness, and First GoogleSQL Lab: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter01/Lesson4.html

Planned05

Checkpoint Lab — BigQuery Foundations: Serverless Analytical Architecture, Projects/Datasets/Tables, Regions, Free-Tier Awareness, and First GoogleSQL Lab: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter01/Lesson5.html

Planned

02

Chapter 2

GoogleSQL Fundamentals: SELECT, Filtering, Ordering, Expressions, Functions, NULL Semantics, Safe Functions, and Query Reproducibility

5 lessons

01

GoogleSQL Fundamentals: SELECT, Filtering, Ordering, Expressions, Functions, NULL Semantics, Safe Functions, and Query Reproducibility: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter02/Lesson1.html

Planned02

GoogleSQL Fundamentals: SELECT, Filtering, Ordering, Expressions, Functions, NULL Semantics, Safe Functions, and Query Reproducibility: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter02/Lesson2.html

Planned03

GoogleSQL Fundamentals: SELECT, Filtering, Ordering, Expressions, Functions, NULL Semantics, Safe Functions, and Query Reproducibility: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter02/Lesson3.html

Planned04

GoogleSQL Fundamentals: SELECT, Filtering, Ordering, Expressions, Functions, NULL Semantics, Safe Functions, and Query Reproducibility: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter02/Lesson4.html

Planned05

Checkpoint Lab — GoogleSQL Fundamentals: SELECT, Filtering, Ordering, Expressions, Functions, NULL Semantics, Safe Functions, and Query Reproducibility: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter02/Lesson5.html

Planned

03

Chapter 3

Data Types and Nested Data: ARRAY, STRUCT, JSON, GEOGRAPHY, RANGE/INTERVAL Concepts, Repeated Fields, UNNEST, and Denormalization Patterns

5 lessons

01

Data Types and Nested Data: ARRAY, STRUCT, JSON, GEOGRAPHY, RANGE/INTERVAL Concepts, Repeated Fields, UNNEST, and Denormalization Patterns: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter03/Lesson1.html

Planned02

Data Types and Nested Data: ARRAY, STRUCT, JSON, GEOGRAPHY, RANGE/INTERVAL Concepts, Repeated Fields, UNNEST, and Denormalization Patterns: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter03/Lesson2.html

Planned03

Data Types and Nested Data: ARRAY, STRUCT, JSON, GEOGRAPHY, RANGE/INTERVAL Concepts, Repeated Fields, UNNEST, and Denormalization Patterns: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter03/Lesson3.html

Planned04

Data Types and Nested Data: ARRAY, STRUCT, JSON, GEOGRAPHY, RANGE/INTERVAL Concepts, Repeated Fields, UNNEST, and Denormalization Patterns: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter03/Lesson4.html

Planned05

Checkpoint Lab — Data Types and Nested Data: ARRAY, STRUCT, JSON, GEOGRAPHY, RANGE/INTERVAL Concepts, Repeated Fields, UNNEST, and Denormalization Patterns: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter03/Lesson5.html

Planned

04

Chapter 4

Joins and Analytical SQL: Join Strategies, Window Functions, QUALIFY, Grouping Sets, PIVOT/UNPIVOT, Approximation, and Advanced Aggregation

5 lessons

01

Joins and Analytical SQL: Join Strategies, Window Functions, QUALIFY, Grouping Sets, PIVOT/UNPIVOT, Approximation, and Advanced Aggregation: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter04/Lesson1.html

Planned02

Joins and Analytical SQL: Join Strategies, Window Functions, QUALIFY, Grouping Sets, PIVOT/UNPIVOT, Approximation, and Advanced Aggregation: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter04/Lesson2.html

Planned03

Joins and Analytical SQL: Join Strategies, Window Functions, QUALIFY, Grouping Sets, PIVOT/UNPIVOT, Approximation, and Advanced Aggregation: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter04/Lesson3.html

Planned04

Joins and Analytical SQL: Join Strategies, Window Functions, QUALIFY, Grouping Sets, PIVOT/UNPIVOT, Approximation, and Advanced Aggregation: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter04/Lesson4.html

Planned05

Checkpoint Lab — Joins and Analytical SQL: Join Strategies, Window Functions, QUALIFY, Grouping Sets, PIVOT/UNPIVOT, Approximation, and Advanced Aggregation: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter04/Lesson5.html

Planned

05

Chapter 5

Datasets, Tables, Views, and Materialized Views: Lifecycle, Metadata, Logical vs Physical Abstractions, Refresh Semantics, and Dependency Design

5 lessons

01

Datasets, Tables, Views, and Materialized Views: Lifecycle, Metadata, Logical vs Physical Abstractions, Refresh Semantics, and Dependency Design: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter05/Lesson1.html

Planned02

Datasets, Tables, Views, and Materialized Views: Lifecycle, Metadata, Logical vs Physical Abstractions, Refresh Semantics, and Dependency Design: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter05/Lesson2.html

Planned03

Datasets, Tables, Views, and Materialized Views: Lifecycle, Metadata, Logical vs Physical Abstractions, Refresh Semantics, and Dependency Design: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter05/Lesson3.html

Planned04

Datasets, Tables, Views, and Materialized Views: Lifecycle, Metadata, Logical vs Physical Abstractions, Refresh Semantics, and Dependency Design: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter05/Lesson4.html

Planned05

Checkpoint Lab — Datasets, Tables, Views, and Materialized Views: Lifecycle, Metadata, Logical vs Physical Abstractions, Refresh Semantics, and Dependency Design: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter05/Lesson5.html

Planned

06

Chapter 6

Partitioned Tables: Ingestion-Time/Time-Unit/Integer-Range Partitioning, Pruning, Require Filter, Expiration, and Partition Anti-Patterns

5 lessons

01

Partitioned Tables: Ingestion-Time/Time-Unit/Integer-Range Partitioning, Pruning, Require Filter, Expiration, and Partition Anti-Patterns: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter06/Lesson1.html

Planned02

Partitioned Tables: Ingestion-Time/Time-Unit/Integer-Range Partitioning, Pruning, Require Filter, Expiration, and Partition Anti-Patterns: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter06/Lesson2.html

Planned03

Partitioned Tables: Ingestion-Time/Time-Unit/Integer-Range Partitioning, Pruning, Require Filter, Expiration, and Partition Anti-Patterns: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter06/Lesson3.html

Planned04

Partitioned Tables: Ingestion-Time/Time-Unit/Integer-Range Partitioning, Pruning, Require Filter, Expiration, and Partition Anti-Patterns: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter06/Lesson4.html

Planned05

Checkpoint Lab — Partitioned Tables: Ingestion-Time/Time-Unit/Integer-Range Partitioning, Pruning, Require Filter, Expiration, and Partition Anti-Patterns: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter06/Lesson5.html

Planned

07

Chapter 7

Clustering: Cluster Keys, Block Pruning, Re-Clustering Behavior, Key Ordering, High Cardinality, Partition+Cluster Design, and Measurement

5 lessons

01

Clustering: Cluster Keys, Block Pruning, Re-Clustering Behavior, Key Ordering, High Cardinality, Partition+Cluster Design, and Measurement: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter07/Lesson1.html

Planned02

Clustering: Cluster Keys, Block Pruning, Re-Clustering Behavior, Key Ordering, High Cardinality, Partition+Cluster Design, and Measurement: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter07/Lesson2.html

Planned03

Clustering: Cluster Keys, Block Pruning, Re-Clustering Behavior, Key Ordering, High Cardinality, Partition+Cluster Design, and Measurement: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter07/Lesson3.html

Planned04

Clustering: Cluster Keys, Block Pruning, Re-Clustering Behavior, Key Ordering, High Cardinality, Partition+Cluster Design, and Measurement: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter07/Lesson4.html

Planned05

Checkpoint Lab — Clustering: Cluster Keys, Block Pruning, Re-Clustering Behavior, Key Ordering, High Cardinality, Partition+Cluster Design, and Measurement: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter07/Lesson5.html

Planned

08

Chapter 8

Storage Internals and Columnar Execution Mental Model: Distributed Storage, Column Pruning, Shuffle, Slots, Stages, and Why SQL Shape Matters

5 lessons

01

Storage Internals and Columnar Execution Mental Model: Distributed Storage, Column Pruning, Shuffle, Slots, Stages, and Why SQL Shape Matters: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter08/Lesson1.html

Planned02

Storage Internals and Columnar Execution Mental Model: Distributed Storage, Column Pruning, Shuffle, Slots, Stages, and Why SQL Shape Matters: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter08/Lesson2.html

Planned03

Storage Internals and Columnar Execution Mental Model: Distributed Storage, Column Pruning, Shuffle, Slots, Stages, and Why SQL Shape Matters: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter08/Lesson3.html

Planned04

Storage Internals and Columnar Execution Mental Model: Distributed Storage, Column Pruning, Shuffle, Slots, Stages, and Why SQL Shape Matters: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter08/Lesson4.html

Planned05

Checkpoint Lab — Storage Internals and Columnar Execution Mental Model: Distributed Storage, Column Pruning, Shuffle, Slots, Stages, and Why SQL Shape Matters: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter08/Lesson5.html

Planned

09

Chapter 9

Query Plans and Performance Diagnostics: Execution Graph, Stage Details, Shuffle, Skew, Spill, Slot Time, Repeated Work, and Query Insights

5 lessons

01

Query Plans and Performance Diagnostics: Execution Graph, Stage Details, Shuffle, Skew, Spill, Slot Time, Repeated Work, and Query Insights: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter09/Lesson1.html

Planned02

Query Plans and Performance Diagnostics: Execution Graph, Stage Details, Shuffle, Skew, Spill, Slot Time, Repeated Work, and Query Insights: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter09/Lesson2.html

Planned03

Query Plans and Performance Diagnostics: Execution Graph, Stage Details, Shuffle, Skew, Spill, Slot Time, Repeated Work, and Query Insights: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter09/Lesson3.html

Planned04

Query Plans and Performance Diagnostics: Execution Graph, Stage Details, Shuffle, Skew, Spill, Slot Time, Repeated Work, and Query Insights: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter09/Lesson4.html

Planned05

Checkpoint Lab — Query Plans and Performance Diagnostics: Execution Graph, Stage Details, Shuffle, Skew, Spill, Slot Time, Repeated Work, and Query Insights: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter09/Lesson5.html

Planned

10

Chapter 10

Performance Engineering: Filter/Projection Pushdown, Join Reduction, Pre-Aggregation, Approximation, Materialization, Caching Awareness, and Benchmarking

5 lessons

01

Performance Engineering: Filter/Projection Pushdown, Join Reduction, Pre-Aggregation, Approximation, Materialization, Caching Awareness, and Benchmarking: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter10/Lesson1.html

Planned02

Performance Engineering: Filter/Projection Pushdown, Join Reduction, Pre-Aggregation, Approximation, Materialization, Caching Awareness, and Benchmarking: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter10/Lesson2.html

Planned03

Performance Engineering: Filter/Projection Pushdown, Join Reduction, Pre-Aggregation, Approximation, Materialization, Caching Awareness, and Benchmarking: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter10/Lesson3.html

Planned04

Performance Engineering: Filter/Projection Pushdown, Join Reduction, Pre-Aggregation, Approximation, Materialization, Caching Awareness, and Benchmarking: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter10/Lesson4.html

Planned05

Checkpoint Lab — Performance Engineering: Filter/Projection Pushdown, Join Reduction, Pre-Aggregation, Approximation, Materialization, Caching Awareness, and Benchmarking: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter10/Lesson5.html

Planned

11

Chapter 11

Loading Data: Batch Loads, CSV/JSON/Avro/Parquet/ORC, Schema Detection, Bad Records, Load Jobs, File Layout, and Idempotent Pipelines

5 lessons

01

Loading Data: Batch Loads, CSV/JSON/Avro/Parquet/ORC, Schema Detection, Bad Records, Load Jobs, File Layout, and Idempotent Pipelines: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter11/Lesson1.html

Planned02

Loading Data: Batch Loads, CSV/JSON/Avro/Parquet/ORC, Schema Detection, Bad Records, Load Jobs, File Layout, and Idempotent Pipelines: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter11/Lesson2.html

Planned03

Loading Data: Batch Loads, CSV/JSON/Avro/Parquet/ORC, Schema Detection, Bad Records, Load Jobs, File Layout, and Idempotent Pipelines: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter11/Lesson3.html

Planned04

Loading Data: Batch Loads, CSV/JSON/Avro/Parquet/ORC, Schema Detection, Bad Records, Load Jobs, File Layout, and Idempotent Pipelines: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter11/Lesson4.html

Planned05

Checkpoint Lab — Loading Data: Batch Loads, CSV/JSON/Avro/Parquet/ORC, Schema Detection, Bad Records, Load Jobs, File Layout, and Idempotent Pipelines: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter11/Lesson5.html

Planned

12

Chapter 12

Streaming and Storage Write API Concepts: Streaming Inserts, Write Streams, Exactly-Once Offsets, Committed/Pending Modes, Costs, and Recovery

5 lessons

01

Streaming and Storage Write API Concepts: Streaming Inserts, Write Streams, Exactly-Once Offsets, Committed/Pending Modes, Costs, and Recovery: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter12/Lesson1.html

Planned02

Streaming and Storage Write API Concepts: Streaming Inserts, Write Streams, Exactly-Once Offsets, Committed/Pending Modes, Costs, and Recovery: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter12/Lesson2.html

Planned03

Streaming and Storage Write API Concepts: Streaming Inserts, Write Streams, Exactly-Once Offsets, Committed/Pending Modes, Costs, and Recovery: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter12/Lesson3.html

Planned04

Streaming and Storage Write API Concepts: Streaming Inserts, Write Streams, Exactly-Once Offsets, Committed/Pending Modes, Costs, and Recovery: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter12/Lesson4.html

Planned05

Checkpoint Lab — Streaming and Storage Write API Concepts: Streaming Inserts, Write Streams, Exactly-Once Offsets, Committed/Pending Modes, Costs, and Recovery: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter12/Lesson5.html

Planned

13

Chapter 13

Data Transfer Service and Managed Ingestion: Scheduled Transfers, Connectors, Metadata Transfers, Credentials, Scheduling, Backfills, and Operational Limits

5 lessons

01

Data Transfer Service and Managed Ingestion: Scheduled Transfers, Connectors, Metadata Transfers, Credentials, Scheduling, Backfills, and Operational Limits: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter13/Lesson1.html

Planned02

Data Transfer Service and Managed Ingestion: Scheduled Transfers, Connectors, Metadata Transfers, Credentials, Scheduling, Backfills, and Operational Limits: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter13/Lesson2.html

Planned03

Data Transfer Service and Managed Ingestion: Scheduled Transfers, Connectors, Metadata Transfers, Credentials, Scheduling, Backfills, and Operational Limits: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter13/Lesson3.html

Planned04

Data Transfer Service and Managed Ingestion: Scheduled Transfers, Connectors, Metadata Transfers, Credentials, Scheduling, Backfills, and Operational Limits: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter13/Lesson4.html

Planned05

Checkpoint Lab — Data Transfer Service and Managed Ingestion: Scheduled Transfers, Connectors, Metadata Transfers, Credentials, Scheduling, Backfills, and Operational Limits: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter13/Lesson5.html

Planned

14

Chapter 14

External Tables and BigLake: Cloud Storage, Object Tables, Iceberg/Lakehouse Concepts, Metadata Caching, Cross-Cloud Patterns, and Pushdown Boundaries

5 lessons

01

External Tables and BigLake: Cloud Storage, Object Tables, Iceberg/Lakehouse Concepts, Metadata Caching, Cross-Cloud Patterns, and Pushdown Boundaries: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter14/Lesson1.html

Planned02

External Tables and BigLake: Cloud Storage, Object Tables, Iceberg/Lakehouse Concepts, Metadata Caching, Cross-Cloud Patterns, and Pushdown Boundaries: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter14/Lesson2.html

Planned03

External Tables and BigLake: Cloud Storage, Object Tables, Iceberg/Lakehouse Concepts, Metadata Caching, Cross-Cloud Patterns, and Pushdown Boundaries: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter14/Lesson3.html

Planned04

External Tables and BigLake: Cloud Storage, Object Tables, Iceberg/Lakehouse Concepts, Metadata Caching, Cross-Cloud Patterns, and Pushdown Boundaries: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter14/Lesson4.html

Planned05

Checkpoint Lab — External Tables and BigLake: Cloud Storage, Object Tables, Iceberg/Lakehouse Concepts, Metadata Caching, Cross-Cloud Patterns, and Pushdown Boundaries: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter14/Lesson5.html

Planned

15

Chapter 15

BigQuery Pipelines and Data Preparation: Managed SQL/Data Preparation Workflows, Scheduling, Trigger-Based Concepts, Dependencies, and Governance

5 lessons

01

BigQuery Pipelines and Data Preparation: Managed SQL/Data Preparation Workflows, Scheduling, Trigger-Based Concepts, Dependencies, and Governance: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter15/Lesson1.html

Planned02

BigQuery Pipelines and Data Preparation: Managed SQL/Data Preparation Workflows, Scheduling, Trigger-Based Concepts, Dependencies, and Governance: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter15/Lesson2.html

Planned03

BigQuery Pipelines and Data Preparation: Managed SQL/Data Preparation Workflows, Scheduling, Trigger-Based Concepts, Dependencies, and Governance: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter15/Lesson3.html

Planned04

BigQuery Pipelines and Data Preparation: Managed SQL/Data Preparation Workflows, Scheduling, Trigger-Based Concepts, Dependencies, and Governance: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter15/Lesson4.html

Planned05

Checkpoint Lab — BigQuery Pipelines and Data Preparation: Managed SQL/Data Preparation Workflows, Scheduling, Trigger-Based Concepts, Dependencies, and Governance: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter15/Lesson5.html

Planned

16

Chapter 16

Continuous Queries: Streaming SQL, Stateful Operations, Aggregations, External Sinks, Delivery Semantics, Checkpoints, and When Continuous Queries Fit

5 lessons

01

Continuous Queries: Streaming SQL, Stateful Operations, Aggregations, External Sinks, Delivery Semantics, Checkpoints, and When Continuous Queries Fit: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter16/Lesson1.html

Planned02

Continuous Queries: Streaming SQL, Stateful Operations, Aggregations, External Sinks, Delivery Semantics, Checkpoints, and When Continuous Queries Fit: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter16/Lesson2.html

Planned03

Continuous Queries: Streaming SQL, Stateful Operations, Aggregations, External Sinks, Delivery Semantics, Checkpoints, and When Continuous Queries Fit: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter16/Lesson3.html

Planned04

Continuous Queries: Streaming SQL, Stateful Operations, Aggregations, External Sinks, Delivery Semantics, Checkpoints, and When Continuous Queries Fit: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter16/Lesson4.html

Planned05

Checkpoint Lab — Continuous Queries: Streaming SQL, Stateful Operations, Aggregations, External Sinks, Delivery Semantics, Checkpoints, and When Continuous Queries Fit: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter16/Lesson5.html

Planned

17

Chapter 17

Scripting and Stored Procedures: Variables, Control Flow, Transactions, Temporary Objects, Dynamic SQL, Error Handling, and Administrative Automation

5 lessons

01

Scripting and Stored Procedures: Variables, Control Flow, Transactions, Temporary Objects, Dynamic SQL, Error Handling, and Administrative Automation: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter17/Lesson1.html

Planned02

Scripting and Stored Procedures: Variables, Control Flow, Transactions, Temporary Objects, Dynamic SQL, Error Handling, and Administrative Automation: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter17/Lesson2.html

Planned03

Scripting and Stored Procedures: Variables, Control Flow, Transactions, Temporary Objects, Dynamic SQL, Error Handling, and Administrative Automation: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter17/Lesson3.html

Planned04

Scripting and Stored Procedures: Variables, Control Flow, Transactions, Temporary Objects, Dynamic SQL, Error Handling, and Administrative Automation: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter17/Lesson4.html

Planned05

Checkpoint Lab — Scripting and Stored Procedures: Variables, Control Flow, Transactions, Temporary Objects, Dynamic SQL, Error Handling, and Administrative Automation: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter17/Lesson5.html

Planned

18

Chapter 18

UDFs and Remote Functions: SQL/JavaScript/Python UDF Concepts, Remote Functions, Cloud Run Integration, Libraries, Determinism, Security, and Cost

5 lessons

01

UDFs and Remote Functions: SQL/JavaScript/Python UDF Concepts, Remote Functions, Cloud Run Integration, Libraries, Determinism, Security, and Cost: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter18/Lesson1.html

Planned02

UDFs and Remote Functions: SQL/JavaScript/Python UDF Concepts, Remote Functions, Cloud Run Integration, Libraries, Determinism, Security, and Cost: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter18/Lesson2.html

Planned03

UDFs and Remote Functions: SQL/JavaScript/Python UDF Concepts, Remote Functions, Cloud Run Integration, Libraries, Determinism, Security, and Cost: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter18/Lesson3.html

Planned04

UDFs and Remote Functions: SQL/JavaScript/Python UDF Concepts, Remote Functions, Cloud Run Integration, Libraries, Determinism, Security, and Cost: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter18/Lesson4.html

Planned05

Checkpoint Lab — UDFs and Remote Functions: SQL/JavaScript/Python UDF Concepts, Remote Functions, Cloud Run Integration, Libraries, Determinism, Security, and Cost: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter18/Lesson5.html

Planned

19

Chapter 19

Transactions and DML: INSERT/UPDATE/DELETE/MERGE, Multi-Statement Transactions, Mutation Cost, Concurrency, Quotas, and CDC-Style Loads

5 lessons

01

Transactions and DML: INSERT/UPDATE/DELETE/MERGE, Multi-Statement Transactions, Mutation Cost, Concurrency, Quotas, and CDC-Style Loads: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter19/Lesson1.html

Planned02

Transactions and DML: INSERT/UPDATE/DELETE/MERGE, Multi-Statement Transactions, Mutation Cost, Concurrency, Quotas, and CDC-Style Loads: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter19/Lesson2.html

Planned03

Transactions and DML: INSERT/UPDATE/DELETE/MERGE, Multi-Statement Transactions, Mutation Cost, Concurrency, Quotas, and CDC-Style Loads: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter19/Lesson3.html

Planned04

Transactions and DML: INSERT/UPDATE/DELETE/MERGE, Multi-Statement Transactions, Mutation Cost, Concurrency, Quotas, and CDC-Style Loads: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter19/Lesson4.html

Planned05

Checkpoint Lab — Transactions and DML: INSERT/UPDATE/DELETE/MERGE, Multi-Statement Transactions, Mutation Cost, Concurrency, Quotas, and CDC-Style Loads: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter19/Lesson5.html

Planned

20

Chapter 20

Change History and Time Travel: Historical Reads, Table Snapshots/Clones Concepts, Recovery, Retention, Auditing, and Reproducible Analytics

5 lessons

01

Change History and Time Travel: Historical Reads, Table Snapshots/Clones Concepts, Recovery, Retention, Auditing, and Reproducible Analytics: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter20/Lesson1.html

Planned02

Change History and Time Travel: Historical Reads, Table Snapshots/Clones Concepts, Recovery, Retention, Auditing, and Reproducible Analytics: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter20/Lesson2.html

Planned03

Change History and Time Travel: Historical Reads, Table Snapshots/Clones Concepts, Recovery, Retention, Auditing, and Reproducible Analytics: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter20/Lesson3.html

Planned04

Change History and Time Travel: Historical Reads, Table Snapshots/Clones Concepts, Recovery, Retention, Auditing, and Reproducible Analytics: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter20/Lesson4.html

Planned05

Checkpoint Lab — Change History and Time Travel: Historical Reads, Table Snapshots/Clones Concepts, Recovery, Retention, Auditing, and Reproducible Analytics: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter20/Lesson5.html

Planned

21

Chapter 21

Geospatial Analytics: GEOGRAPHY Types, Spatial Joins, Functions, Indexing Expectations, Visualization, and Performance Considerations

5 lessons

01

Geospatial Analytics: GEOGRAPHY Types, Spatial Joins, Functions, Indexing Expectations, Visualization, and Performance Considerations: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter21/Lesson1.html

Planned02

Geospatial Analytics: GEOGRAPHY Types, Spatial Joins, Functions, Indexing Expectations, Visualization, and Performance Considerations: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter21/Lesson2.html

Planned03

Geospatial Analytics: GEOGRAPHY Types, Spatial Joins, Functions, Indexing Expectations, Visualization, and Performance Considerations: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter21/Lesson3.html

Planned04

Geospatial Analytics: GEOGRAPHY Types, Spatial Joins, Functions, Indexing Expectations, Visualization, and Performance Considerations: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter21/Lesson4.html

Planned05

Checkpoint Lab — Geospatial Analytics: GEOGRAPHY Types, Spatial Joins, Functions, Indexing Expectations, Visualization, and Performance Considerations: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter21/Lesson5.html

Planned

22

Chapter 22

BigQuery ML Foundations: CREATE MODEL, Training/Evaluation/Predict, Linear/Logistic/Tree/Clustering/Forecasting Concepts, Feature Engineering, and Cost

5 lessons

01

BigQuery ML Foundations: CREATE MODEL, Training/Evaluation/Predict, Linear/Logistic/Tree/Clustering/Forecasting Concepts, Feature Engineering, and Cost: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter22/Lesson1.html

Planned02

BigQuery ML Foundations: CREATE MODEL, Training/Evaluation/Predict, Linear/Logistic/Tree/Clustering/Forecasting Concepts, Feature Engineering, and Cost: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter22/Lesson2.html

Planned03

BigQuery ML Foundations: CREATE MODEL, Training/Evaluation/Predict, Linear/Logistic/Tree/Clustering/Forecasting Concepts, Feature Engineering, and Cost: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter22/Lesson3.html

Planned04

BigQuery ML Foundations: CREATE MODEL, Training/Evaluation/Predict, Linear/Logistic/Tree/Clustering/Forecasting Concepts, Feature Engineering, and Cost: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter22/Lesson4.html

Planned05

Checkpoint Lab — BigQuery ML Foundations: CREATE MODEL, Training/Evaluation/Predict, Linear/Logistic/Tree/Clustering/Forecasting Concepts, Feature Engineering, and Cost: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter22/Lesson5.html

Planned

23

Chapter 23

Advanced BigQuery ML and Time Series: TimesFM/Forecasting Concepts, Anomaly Detection, Contribution/Driver Analysis, Model Registry Awareness, and Evaluation

5 lessons

01

Advanced BigQuery ML and Time Series: TimesFM/Forecasting Concepts, Anomaly Detection, Contribution/Driver Analysis, Model Registry Awareness, and Evaluation: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter23/Lesson1.html

Planned02

Advanced BigQuery ML and Time Series: TimesFM/Forecasting Concepts, Anomaly Detection, Contribution/Driver Analysis, Model Registry Awareness, and Evaluation: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter23/Lesson2.html

Planned03

Advanced BigQuery ML and Time Series: TimesFM/Forecasting Concepts, Anomaly Detection, Contribution/Driver Analysis, Model Registry Awareness, and Evaluation: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter23/Lesson3.html

Planned04

Advanced BigQuery ML and Time Series: TimesFM/Forecasting Concepts, Anomaly Detection, Contribution/Driver Analysis, Model Registry Awareness, and Evaluation: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter23/Lesson4.html

Planned05

Checkpoint Lab — Advanced BigQuery ML and Time Series: TimesFM/Forecasting Concepts, Anomaly Detection, Contribution/Driver Analysis, Model Registry Awareness, and Evaluation: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter23/Lesson5.html

Planned

24

Chapter 24

AI Functions and Generative Workflows: AI.GENERATE/CLASSIFY/SCORE/AGG/Similarity Concepts, Remote/Managed Models, Prompt Safety, Cost, and Governance

5 lessons

01

AI Functions and Generative Workflows: AI.GENERATE/CLASSIFY/SCORE/AGG/Similarity Concepts, Remote/Managed Models, Prompt Safety, Cost, and Governance: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter24/Lesson1.html

Planned02

AI Functions and Generative Workflows: AI.GENERATE/CLASSIFY/SCORE/AGG/Similarity Concepts, Remote/Managed Models, Prompt Safety, Cost, and Governance: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter24/Lesson2.html

Planned03

AI Functions and Generative Workflows: AI.GENERATE/CLASSIFY/SCORE/AGG/Similarity Concepts, Remote/Managed Models, Prompt Safety, Cost, and Governance: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter24/Lesson3.html

Planned04

AI Functions and Generative Workflows: AI.GENERATE/CLASSIFY/SCORE/AGG/Similarity Concepts, Remote/Managed Models, Prompt Safety, Cost, and Governance: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter24/Lesson4.html

Planned05

Checkpoint Lab — AI Functions and Generative Workflows: AI.GENERATE/CLASSIFY/SCORE/AGG/Similarity Concepts, Remote/Managed Models, Prompt Safety, Cost, and Governance: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter24/Lesson5.html

Planned

25

Chapter 25

Embeddings and Vector Search: VECTOR_SEARCH, Vector Indexes, Autonomous Embedding Generation, Similarity Metrics, Refresh, Recall/Latency, and Evaluation

5 lessons

01

Embeddings and Vector Search: VECTOR_SEARCH, Vector Indexes, Autonomous Embedding Generation, Similarity Metrics, Refresh, Recall/Latency, and Evaluation: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter25/Lesson1.html

Planned02

Embeddings and Vector Search: VECTOR_SEARCH, Vector Indexes, Autonomous Embedding Generation, Similarity Metrics, Refresh, Recall/Latency, and Evaluation: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter25/Lesson2.html

Planned03

Embeddings and Vector Search: VECTOR_SEARCH, Vector Indexes, Autonomous Embedding Generation, Similarity Metrics, Refresh, Recall/Latency, and Evaluation: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter25/Lesson3.html

Planned04

Embeddings and Vector Search: VECTOR_SEARCH, Vector Indexes, Autonomous Embedding Generation, Similarity Metrics, Refresh, Recall/Latency, and Evaluation: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter25/Lesson4.html

Planned05

Checkpoint Lab — Embeddings and Vector Search: VECTOR_SEARCH, Vector Indexes, Autonomous Embedding Generation, Similarity Metrics, Refresh, Recall/Latency, and Evaluation: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter25/Lesson5.html

Planned

26

Chapter 26

Hybrid Search and AI.SEARCH: Semantic + Lexical Retrieval, Keyword-Augmented Vector Indexes, Autonomous Embeddings, Ranking, RAG Retrieval, and Preview Boundaries

5 lessons

01

Hybrid Search and AI.SEARCH: Semantic + Lexical Retrieval, Keyword-Augmented Vector Indexes, Autonomous Embeddings, Ranking, RAG Retrieval, and Preview Boundaries: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter26/Lesson1.html

Planned02

Hybrid Search and AI.SEARCH: Semantic + Lexical Retrieval, Keyword-Augmented Vector Indexes, Autonomous Embeddings, Ranking, RAG Retrieval, and Preview Boundaries: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter26/Lesson2.html

Planned03

Hybrid Search and AI.SEARCH: Semantic + Lexical Retrieval, Keyword-Augmented Vector Indexes, Autonomous Embeddings, Ranking, RAG Retrieval, and Preview Boundaries: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter26/Lesson3.html

Planned04

Hybrid Search and AI.SEARCH: Semantic + Lexical Retrieval, Keyword-Augmented Vector Indexes, Autonomous Embeddings, Ranking, RAG Retrieval, and Preview Boundaries: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter26/Lesson4.html

Planned05

Checkpoint Lab — Hybrid Search and AI.SEARCH: Semantic + Lexical Retrieval, Keyword-Augmented Vector Indexes, Autonomous Embeddings, Ranking, RAG Retrieval, and Preview Boundaries: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter26/Lesson5.html

Planned

27

Chapter 27

BigQuery Graph and GQL Awareness: Property Graph Modeling over Tables, Graph Query Language, Paths/Patterns, Relationship Analytics, and Preview Evaluation

5 lessons

01

BigQuery Graph and GQL Awareness: Property Graph Modeling over Tables, Graph Query Language, Paths/Patterns, Relationship Analytics, and Preview Evaluation: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter27/Lesson1.html

Planned02

BigQuery Graph and GQL Awareness: Property Graph Modeling over Tables, Graph Query Language, Paths/Patterns, Relationship Analytics, and Preview Evaluation: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter27/Lesson2.html

Planned03

BigQuery Graph and GQL Awareness: Property Graph Modeling over Tables, Graph Query Language, Paths/Patterns, Relationship Analytics, and Preview Evaluation: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter27/Lesson3.html

Planned04

BigQuery Graph and GQL Awareness: Property Graph Modeling over Tables, Graph Query Language, Paths/Patterns, Relationship Analytics, and Preview Evaluation: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter27/Lesson4.html

Planned05

Checkpoint Lab — BigQuery Graph and GQL Awareness: Property Graph Modeling over Tables, Graph Query Language, Paths/Patterns, Relationship Analytics, and Preview Evaluation: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter27/Lesson5.html

Planned

28

Chapter 28

BI Engine and Interactive Analytics: Acceleration Concepts, Reservations, Dashboard Workloads, Connected Sheets, Cache/Capacity Behavior, and BI Design

5 lessons

01

BI Engine and Interactive Analytics: Acceleration Concepts, Reservations, Dashboard Workloads, Connected Sheets, Cache/Capacity Behavior, and BI Design: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter28/Lesson1.html

Planned02

BI Engine and Interactive Analytics: Acceleration Concepts, Reservations, Dashboard Workloads, Connected Sheets, Cache/Capacity Behavior, and BI Design: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter28/Lesson2.html

Planned03

BI Engine and Interactive Analytics: Acceleration Concepts, Reservations, Dashboard Workloads, Connected Sheets, Cache/Capacity Behavior, and BI Design: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter28/Lesson3.html

Planned04

BI Engine and Interactive Analytics: Acceleration Concepts, Reservations, Dashboard Workloads, Connected Sheets, Cache/Capacity Behavior, and BI Design: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter28/Lesson4.html

Planned05

Checkpoint Lab — BI Engine and Interactive Analytics: Acceleration Concepts, Reservations, Dashboard Workloads, Connected Sheets, Cache/Capacity Behavior, and BI Design: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter28/Lesson5.html

Planned

29

Chapter 29

Reservations and Workload Management: Editions/Capacity Concepts, Assignments, Autoscaling, Fluid Scaling, Identity-Based Routing, Isolation, and Chargeback

5 lessons

01

Reservations and Workload Management: Editions/Capacity Concepts, Assignments, Autoscaling, Fluid Scaling, Identity-Based Routing, Isolation, and Chargeback: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter29/Lesson1.html

Planned02

Reservations and Workload Management: Editions/Capacity Concepts, Assignments, Autoscaling, Fluid Scaling, Identity-Based Routing, Isolation, and Chargeback: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter29/Lesson2.html

Planned03

Reservations and Workload Management: Editions/Capacity Concepts, Assignments, Autoscaling, Fluid Scaling, Identity-Based Routing, Isolation, and Chargeback: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter29/Lesson3.html

Planned04

Reservations and Workload Management: Editions/Capacity Concepts, Assignments, Autoscaling, Fluid Scaling, Identity-Based Routing, Isolation, and Chargeback: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter29/Lesson4.html

Planned05

Checkpoint Lab — Reservations and Workload Management: Editions/Capacity Concepts, Assignments, Autoscaling, Fluid Scaling, Identity-Based Routing, Isolation, and Chargeback: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter29/Lesson5.html

Planned

30

Chapter 30

On-Demand vs Capacity Cost Engineering: Bytes Processed, Slot Consumption, Storage Classes, Editions Concepts, Quotas, Budgets, Labels, and FinOps Dashboards

5 lessons

01

On-Demand vs Capacity Cost Engineering: Bytes Processed, Slot Consumption, Storage Classes, Editions Concepts, Quotas, Budgets, Labels, and FinOps Dashboards: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter30/Lesson1.html

Planned02

On-Demand vs Capacity Cost Engineering: Bytes Processed, Slot Consumption, Storage Classes, Editions Concepts, Quotas, Budgets, Labels, and FinOps Dashboards: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter30/Lesson2.html

Planned03

On-Demand vs Capacity Cost Engineering: Bytes Processed, Slot Consumption, Storage Classes, Editions Concepts, Quotas, Budgets, Labels, and FinOps Dashboards: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter30/Lesson3.html

Planned04

On-Demand vs Capacity Cost Engineering: Bytes Processed, Slot Consumption, Storage Classes, Editions Concepts, Quotas, Budgets, Labels, and FinOps Dashboards: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter30/Lesson4.html

Planned05

Checkpoint Lab — On-Demand vs Capacity Cost Engineering: Bytes Processed, Slot Consumption, Storage Classes, Editions Concepts, Quotas, Budgets, Labels, and FinOps Dashboards: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter30/Lesson5.html

Planned

31

Chapter 31

Security Fundamentals: IAM, Dataset/Table Permissions, Service Accounts, Authorized Views/Routines, VPC Service Controls Awareness, and Least Privilege

5 lessons

01

Security Fundamentals: IAM, Dataset/Table Permissions, Service Accounts, Authorized Views/Routines, VPC Service Controls Awareness, and Least Privilege: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter31/Lesson1.html

Planned02

Security Fundamentals: IAM, Dataset/Table Permissions, Service Accounts, Authorized Views/Routines, VPC Service Controls Awareness, and Least Privilege: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter31/Lesson2.html

Planned03

Security Fundamentals: IAM, Dataset/Table Permissions, Service Accounts, Authorized Views/Routines, VPC Service Controls Awareness, and Least Privilege: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter31/Lesson3.html

Planned04

Security Fundamentals: IAM, Dataset/Table Permissions, Service Accounts, Authorized Views/Routines, VPC Service Controls Awareness, and Least Privilege: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter31/Lesson4.html

Planned05

Checkpoint Lab — Security Fundamentals: IAM, Dataset/Table Permissions, Service Accounts, Authorized Views/Routines, VPC Service Controls Awareness, and Least Privilege: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter31/Lesson5.html

Planned

32

Chapter 32

Column/Row Security, Masking, and Governance Tags: Policy Tags, Data Policies, Row Access Policies, Governance Tags, Dynamic Masking, and Testing Access

5 lessons

01

Column/Row Security, Masking, and Governance Tags: Policy Tags, Data Policies, Row Access Policies, Governance Tags, Dynamic Masking, and Testing Access: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter32/Lesson1.html

Planned02

Column/Row Security, Masking, and Governance Tags: Policy Tags, Data Policies, Row Access Policies, Governance Tags, Dynamic Masking, and Testing Access: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter32/Lesson2.html

Planned03

Column/Row Security, Masking, and Governance Tags: Policy Tags, Data Policies, Row Access Policies, Governance Tags, Dynamic Masking, and Testing Access: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter32/Lesson3.html

Planned04

Column/Row Security, Masking, and Governance Tags: Policy Tags, Data Policies, Row Access Policies, Governance Tags, Dynamic Masking, and Testing Access: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter32/Lesson4.html

Planned05

Checkpoint Lab — Column/Row Security, Masking, and Governance Tags: Policy Tags, Data Policies, Row Access Policies, Governance Tags, Dynamic Masking, and Testing Access: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter32/Lesson5.html

Planned

33

Chapter 33

Metadata, Lineage, and Knowledge Catalog: INFORMATION_SCHEMA, Labels/Tags, Lineage, Glossaries, Search/Discovery, Ownership, and Governance Workflows

5 lessons

01

Metadata, Lineage, and Knowledge Catalog: INFORMATION_SCHEMA, Labels/Tags, Lineage, Glossaries, Search/Discovery, Ownership, and Governance Workflows: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter33/Lesson1.html

Planned02

Metadata, Lineage, and Knowledge Catalog: INFORMATION_SCHEMA, Labels/Tags, Lineage, Glossaries, Search/Discovery, Ownership, and Governance Workflows: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter33/Lesson2.html

Planned03

Metadata, Lineage, and Knowledge Catalog: INFORMATION_SCHEMA, Labels/Tags, Lineage, Glossaries, Search/Discovery, Ownership, and Governance Workflows: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter33/Lesson3.html

Planned04

Metadata, Lineage, and Knowledge Catalog: INFORMATION_SCHEMA, Labels/Tags, Lineage, Glossaries, Search/Discovery, Ownership, and Governance Workflows: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter33/Lesson4.html

Planned05

Checkpoint Lab — Metadata, Lineage, and Knowledge Catalog: INFORMATION_SCHEMA, Labels/Tags, Lineage, Glossaries, Search/Discovery, Ownership, and Governance Workflows: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter33/Lesson5.html

Planned

34

Chapter 34

Data Sharing: Authorized Sharing, Analytics Hub/BigQuery Sharing Concepts, Listings, Clean-Room Awareness, Cross-Project Access, and Consumer Governance

5 lessons

01

Data Sharing: Authorized Sharing, Analytics Hub/BigQuery Sharing Concepts, Listings, Clean-Room Awareness, Cross-Project Access, and Consumer Governance: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter34/Lesson1.html

Planned02

Data Sharing: Authorized Sharing, Analytics Hub/BigQuery Sharing Concepts, Listings, Clean-Room Awareness, Cross-Project Access, and Consumer Governance: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter34/Lesson2.html

Planned03

Data Sharing: Authorized Sharing, Analytics Hub/BigQuery Sharing Concepts, Listings, Clean-Room Awareness, Cross-Project Access, and Consumer Governance: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter34/Lesson3.html

Planned04

Data Sharing: Authorized Sharing, Analytics Hub/BigQuery Sharing Concepts, Listings, Clean-Room Awareness, Cross-Project Access, and Consumer Governance: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter34/Lesson4.html

Planned05

Checkpoint Lab — Data Sharing: Authorized Sharing, Analytics Hub/BigQuery Sharing Concepts, Listings, Clean-Room Awareness, Cross-Project Access, and Consumer Governance: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter34/Lesson5.html

Planned

35

Chapter 35

Replication and Disaster Recovery: Dataset Replication, Cross-Region Managed DR Concepts, Failover, Monitoring Latency/Egress, RPO/RTO, and Recovery Tests

5 lessons

01

Replication and Disaster Recovery: Dataset Replication, Cross-Region Managed DR Concepts, Failover, Monitoring Latency/Egress, RPO/RTO, and Recovery Tests: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter35/Lesson1.html

Planned02

Replication and Disaster Recovery: Dataset Replication, Cross-Region Managed DR Concepts, Failover, Monitoring Latency/Egress, RPO/RTO, and Recovery Tests: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter35/Lesson2.html

Planned03

Replication and Disaster Recovery: Dataset Replication, Cross-Region Managed DR Concepts, Failover, Monitoring Latency/Egress, RPO/RTO, and Recovery Tests: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter35/Lesson3.html

Planned04

Replication and Disaster Recovery: Dataset Replication, Cross-Region Managed DR Concepts, Failover, Monitoring Latency/Egress, RPO/RTO, and Recovery Tests: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter35/Lesson4.html

Planned05

Checkpoint Lab — Replication and Disaster Recovery: Dataset Replication, Cross-Region Managed DR Concepts, Failover, Monitoring Latency/Egress, RPO/RTO, and Recovery Tests: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter35/Lesson5.html

Planned

36

Chapter 36

APIs, Clients, JDBC/ODBC, and Automation: bq CLI, REST, Python/Java Clients, Open-Source JDBC, Jobs, Retries, Idempotency, and Infrastructure-as-Code

5 lessons

01

APIs, Clients, JDBC/ODBC, and Automation: bq CLI, REST, Python/Java Clients, Open-Source JDBC, Jobs, Retries, Idempotency, and Infrastructure-as-Code: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter36/Lesson1.html

Planned02

APIs, Clients, JDBC/ODBC, and Automation: bq CLI, REST, Python/Java Clients, Open-Source JDBC, Jobs, Retries, Idempotency, and Infrastructure-as-Code: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter36/Lesson2.html

Planned03

APIs, Clients, JDBC/ODBC, and Automation: bq CLI, REST, Python/Java Clients, Open-Source JDBC, Jobs, Retries, Idempotency, and Infrastructure-as-Code: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter36/Lesson3.html

Planned04

APIs, Clients, JDBC/ODBC, and Automation: bq CLI, REST, Python/Java Clients, Open-Source JDBC, Jobs, Retries, Idempotency, and Infrastructure-as-Code: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter36/Lesson4.html

Planned05

Checkpoint Lab — APIs, Clients, JDBC/ODBC, and Automation: bq CLI, REST, Python/Java Clients, Open-Source JDBC, Jobs, Retries, Idempotency, and Infrastructure-as-Code: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter36/Lesson5.html

Planned

37

Chapter 37

Migration and Modernization: SQL Translation, Migration Service, Teradata/Snowflake/Oracle/Hadoop Patterns, Validation, Dual-Run, and Cost Regression Gates

5 lessons

01

Migration and Modernization: SQL Translation, Migration Service, Teradata/Snowflake/Oracle/Hadoop Patterns, Validation, Dual-Run, and Cost Regression Gates: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter37/Lesson1.html

Planned02

Migration and Modernization: SQL Translation, Migration Service, Teradata/Snowflake/Oracle/Hadoop Patterns, Validation, Dual-Run, and Cost Regression Gates: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter37/Lesson2.html

Planned03

Migration and Modernization: SQL Translation, Migration Service, Teradata/Snowflake/Oracle/Hadoop Patterns, Validation, Dual-Run, and Cost Regression Gates: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter37/Lesson3.html

Planned04

Migration and Modernization: SQL Translation, Migration Service, Teradata/Snowflake/Oracle/Hadoop Patterns, Validation, Dual-Run, and Cost Regression Gates: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter37/Lesson4.html

Planned05

Checkpoint Lab — Migration and Modernization: SQL Translation, Migration Service, Teradata/Snowflake/Oracle/Hadoop Patterns, Validation, Dual-Run, and Cost Regression Gates: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter37/Lesson5.html

Planned

38

Chapter 38

Production Capstone: Ingest, Model, Partition, Cluster, Stream, Search, Govern, Optimize, Share, Replicate, Observe, and Cost-Control BigQuery

5 lessons

01

Production Capstone: Ingest, Model, Partition, Cluster, Stream, Search, Govern, Optimize, Share, Replicate, Observe, and Cost-Control BigQuery: Concepts, Terminology, Architecture, and Mental ModelPlanned lesson · reserved path Chapter38/Lesson1.html

Planned02

Production Capstone: Ingest, Model, Partition, Cluster, Stream, Search, Govern, Optimize, Share, Replicate, Observe, and Cost-Control BigQuery: Guided Hands-On Workflow, Commands, APIs, and ConfigurationPlanned lesson · reserved path Chapter38/Lesson2.html

Planned03

Production Capstone: Ingest, Model, Partition, Cluster, Stream, Search, Govern, Optimize, Share, Replicate, Observe, and Cost-Control BigQuery: Design Choices, Scaling Behavior, Compatibility, and TradeoffsPlanned lesson · reserved path Chapter38/Lesson3.html

Planned04

Production Capstone: Ingest, Model, Partition, Cluster, Stream, Search, Govern, Optimize, Share, Replicate, Observe, and Cost-Control BigQuery: Failure Modes, Diagnostics, Security, Reliability, and PerformancePlanned lesson · reserved path Chapter38/Lesson4.html

Planned05

Checkpoint Lab — Production Capstone: Ingest, Model, Partition, Cluster, Stream, Search, Govern, Optimize, Share, Replicate, Observe, and Cost-Control BigQuery: Build, Test, Measure, Troubleshoot, and Explain the ResultPlanned lesson · reserved path Chapter38/Lesson5.html

Planned