Build a Practical Data Modeling and Warehousing Example in Data Engineering Fundamentals

Define the build target

For Data Modeling and Warehousing, design two related tables or models, add constraints, and create a migration that can be applied to a test database. Build the boundary case using this implementation lens: Use data contracts, formats, storage layouts, batch/stream pipelines, transformations, warehouse models, orchestration state, quality checks, lineage/telemetry, security, and cost signals.

Keep the Data Modeling and Warehousing build centered on these technical constraints: Primary keys. Foreign keys and cardinality. Apply them through this path lens: Use data contracts, formats, storage layouts, batch/stream pipelines, transformations, warehouse models, orchestration state, quality checks, lineage/telemetry, security, and cost signals. Use data contracts, formats, storage layouts, batch/stream pipelines, transformations, warehouse models, orchestration state, quality checks, lineage/telemetry, security, and cost signals.

Implement the core behavior

Implement Data Modeling and Warehousing around the module artifact—a working data modeling and warehousing example with an explicit success and failure check—and keep the implementation specific to this path context: Use data contracts, formats, storage layouts, batch/stream pipelines, transformations, warehouse models, orchestration state, quality checks, lineage/telemetry, security, and cost signals.

CREATE TABLE customers (
  id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
  email text NOT NULL UNIQUE
);

CREATE TABLE orders (
  id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
  customer_id bigint NOT NULL REFERENCES customers(id),
  total numeric(10,2) NOT NULL CHECK (total >= 0)
);

psql -f schema.sql

The database rejects duplicate customer emails, missing customers, and negative order totals.

practice/\n├── README.md\n├── data-modeling-and-warehousing-build.sql\n└── evidence/\n    └── expected-result.txt

Run the complete path

Run one realistic Data Modeling and Warehousing case end to end and record the required evidence: the relevant output, test, log, query result, or rendered state for Data Modeling and Warehousing. Interpret the result through this path context: Use data contracts, formats, storage layouts, batch/stream pipelines, transformations, warehouse models, orchestration state, quality checks, lineage/telemetry, security, and cost signals.

Change one meaningful condition

Modify one condition central to Data Modeling and Warehousing using this path context: Use data contracts, formats, storage layouts, batch/stream pipelines, transformations, warehouse models, orchestration state, quality checks, lineage/telemetry, security, and cost signals. Predict the new result before rerunning the same workflow.

Verify the artifact

Your deliverable is a working data modeling and warehousing example with an explicit success and failure check.

Common mistakes to avoid

• Relationship cardinality wrong.
• Constraint missing from database.
• Destructive migration without backfill.
• Duplicate data violates new constraint.