Data Warehousing & BigQuery is a structured, practical course covering BigQuery, Snowflake, dbt, Data Warehouse, ETL. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
DatabasesTool stack
Google BigQuerySnowflake
Technology marks for Data Warehousing & BigQuery, sourced from the CC0-licensed Simple Icons project.
Use BigQuery appropriately in a realistic, bounded task.
Use Snowflake appropriately in a realistic, bounded task.
Use dbt appropriately in a realistic, bounded task.
Use Data Warehouse appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
BigQuery: Data modelling
Apply BigQuery through entities, relationships, constraints.
Lesson
Key terms
Revision question
Entities with BigQuery
entities, BigQuery, databases
In Data Warehousing & BigQuery, which evidence best supports a entities result produced with BigQuery?
Relationships with Snowflake
relationships, Snowflake, databases
In Data Warehousing & BigQuery, which evidence best supports a relationships result produced with Snowflake?
Constraints with dbt
constraints, dbt, databases
In Data Warehousing & BigQuery, which evidence best supports a constraints result produced with dbt?
Instructional figureNested structure
Entities: from context to evidence
Entities connects business rule and records to a valid data state in Data Warehousing & BigQuery.
1Business rule and records
Define the purpose, intended user and BigQuery constraints.
frames
2Entities
Derive entities from business rules and define stable identifiers.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Entities is credible only when the result can be traced back to its purpose, inputs and constraints. Entities is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 2
Snowflake: Querying
Apply Snowflake through selection, joins, aggregation.
Lesson
Key terms
Revision question
Selection with Data Warehouse
selection, Data Warehouse, databases
In Data Warehousing & BigQuery, which evidence best supports a selection result produced with Data Warehouse?
Joins with ETL
joins, ETL, databases
In Data Warehousing & BigQuery, which evidence best supports a joins result produced with ETL?
Aggregation with BigQuery
aggregation, BigQuery, databases
In Data Warehousing & BigQuery, which evidence best supports a aggregation result produced with BigQuery?
Instructional figureContinuous cycle
Selection: from context to evidence
Selection connects business rule and records to a valid data state in Data Warehousing & BigQuery.
1Business rule and records
Define the purpose, intended user and Data Warehouse constraints.
frames
2Selection
Translate conditions into predicates and test boundaries.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Selection is credible only when the result can be traced back to its purpose, inputs and constraints. Selection is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 3
dbt: Reliability
Apply dbt through transactions, indexes, backups.
Lesson
Key terms
Revision question
Transactions with Snowflake
transactions, Snowflake, databases
In Data Warehousing & BigQuery, which evidence best supports a transactions result produced with Snowflake?
Indexes with dbt
indexes, dbt, databases
In Data Warehousing & BigQuery, which evidence best supports a indexes result produced with dbt?
Backups with Data Warehouse
backups, Data Warehouse, databases
In Data Warehousing & BigQuery, which evidence best supports a backups result produced with Data Warehouse?
Instructional figureProcess flow
Transactions: from context to evidence
Transactions connects business rule and records to a valid data state in Data Warehousing & BigQuery.
1Business rule and records
Define the purpose, intended user and Snowflake constraints.
frames
2Transactions
Choose boundaries that preserve invariants under failure.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Transactions is credible only when the result can be traced back to its purpose, inputs and constraints. Transactions is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 4
Data Warehouse: Production design
Apply Data Warehouse through security, performance, operations.
Lesson
Key terms
Revision question
Security with ETL
security, ETL, databases
In Data Warehousing & BigQuery, which evidence best supports a security result produced with ETL?
Performance with BigQuery
performance, BigQuery, databases
In Data Warehousing & BigQuery, which evidence best supports a performance result produced with BigQuery?
Operations with Snowflake
operations, Snowflake, databases
In Data Warehousing & BigQuery, which evidence best supports a operations result produced with Snowflake?
Instructional figureContinuous cycle
Security: from context to evidence
Security connects business rule and records to a valid data state in Data Warehousing & BigQuery.
1Business rule and records
Define the purpose, intended user and ETL constraints.
frames
2Security
Grant roles by task and test prohibited operations.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Security is credible only when the result can be traced back to its purpose, inputs and constraints. Security is the decision layer between the starting context and evidence that the result is fit for purpose.
Course practical outcome
Produce a reviewable Data Warehousing & BigQuery project using BigQuery, Snowflake, dbt.
Expected output: A working Data Warehousing & BigQuery artefact plus an evidence-based self-review.
Tools: A suitable BigQuery environment, A plain-text decision log, Test data or realistic sample material
Production steps
Define the intended user, outcome and constraints.
Create the smallest complete result using BigQuery.
Test one normal case, one boundary case and one failure response.
Revise the work from the evidence and preserve before-and-after results.
Prepare a concise handover containing method, limitations and next step.
Success criteria
The output matches the stated outcome.
Inputs and decisions are reproducible.
Boundary and failure evidence is included.
Limitations and responsibility considerations are explicit.
Self-review
Can another learner repeat the method?
Did I test a difficult case?
Did I avoid unsupported claims?
Is the next action proportionate to the remaining risk?
Next step: Choose one weakness found during review and improve it before extending the BigQuery scope.
Glossary
BigQuery
A core concept or tool used in Data Warehousing & BigQuery; its exact meaning is established in the relevant lesson.
Snowflake
A core concept or tool used in Data Warehousing & BigQuery; its exact meaning is established in the relevant lesson.
dbt
A core concept or tool used in Data Warehousing & BigQuery; its exact meaning is established in the relevant lesson.
Data Warehouse
A core concept or tool used in Data Warehousing & BigQuery; its exact meaning is established in the relevant lesson.
ETL
A core concept or tool used in Data Warehousing & BigQuery; its exact meaning is established in the relevant lesson.
References
PostgreSQL Tutorial - PostgreSQL Global Development Group (accessed 2026-08-21)
SQL Language - PostgreSQL Global Development Group (accessed 2026-08-21)
This guide is generated from DigiLearn course material. Product versions, regulations and professional standards can change; consult the linked authoritative source before applying version-sensitive guidance.