Course (Part 376)

Course: dbt on Databricks

The course opens with a clear introduction that orients learners to objectives, structure, and expected outcomes, emphasizing a blend of theory and practice. It outlines modules that range from dbt Cloud setup and resource configuration to conceptual foundations like models, tests, seeds, and analyses. Practical chapters cover technical skills such as Jinja templating, macro creation, and package organization, alongside dbt-specific topics like materializations, incremental builds, and job management. Testing is treated as a central theme, with sections on generic versus singular tests, advanced testing methods, and strategies for ensuring data quality. The curriculum also addresses collaborative development workflows, including branching, merging to main, and best practices for code review and conflict resolution. Deployment strategies such as blue-green, canary, and rolling deployments are introduced with preparatory and rollback procedures. Throughout the course, reproducibility and consistency are highlighted via the use of seeds and well-documented workflows. Learners are encouraged to use dbt Core and dbt Cloud features—models, documentation, scheduling, and job monitoring—to build robust analytics pipelines. Emphasis is placed on continuous improvement: monitoring performance, refining materializations, and iterating on models and tests. The pedagogical approach favors interaction, peer discussion, and hands-on labs to reinforce concepts. Resources such as readings, videos, and quizzes are provided to support self-assessment and progress tracking. By the end, students are expected to be competent in transforming data reliably, automating workflows, and contributing to production-ready analytics systems. Congratulations and next-step recommendations close the course, inviting learners to apply their skills in real projects and continue their data engineering practice.

Lesson

Topic

Course overview: dbt Cloud & Core, models, tests, seeds, Jinja, materializations, CI/CD, deployment, and best practices.

Learning Outcomes

Understand the overall course structure and objectives.
Set up dbt Cloud projects and connect data warehouses.
Design and materialize models using dbt Core and dbt Cloud.
Apply generic and singular tests to ensure data quality.
Use Jinja macros, functions, and packages for templating.
Implement deployment strategies and merge workflows with best practices.

Conceptual Explanation

This lesson synthesizes foundational concepts and practical tasks across the course. Learners begin with an introduction to goals, structure, and resources, then progress through technical chapters: dbt Cloud setup, model creation and materialization, testing paradigms (generic vs singular), the role of seeds for reproducibility, templating with Jinja (functions, macros, packages), and operational topics such as merging changes, CI/CD, and deployment strategies. Emphasis is placed on reproducibility, collaboration, monitoring, and iterative improvement. Each module pairs theoretical explanation with hands-on activities—SQL models, tests, and CI job definitions—so learners can apply concepts to realistic pipelines. Monitoring and documentation are highlighted to sustain production readiness and team transparency.

Bilingual (EN → বাংলা)

English	বাংলা
Introduction	পরিচিতি
dbt Cloud	dbt ক্লাউড
Model	মডেল
Materialization	অবকাঠামোগত সংরক্ষণ (Materialization)
Seed (reproducibility)	সিড (পুনরুত্পাদনযোগ্যতা)
Test (generic/singular)	পরীক্ষা (সার্বজনীন/বিশেষ)
Deployment	প্রকাশ (ডিপ্লয়মেন্ট)

Example sentence translation:

EN: "Set a seed to ensure reproducible splits for training and testing."
বাংলা: "ট্রেনিং এবং টেস্টিংয়ের জন্য পুনরুত্পাদনযোগ্য ভাগ নিশ্চিত করতে একটি সিড সেট করুন।"

Important Notes

Document materialization choices and the rationale for performance and cost trade-offs.
Use seeds to make experiments reproducible across team members and CI runs.
Prefer incremental materializations for large datasets to save compute.
Integrate tests into CI pipelines to catch regressions early.
Adopt branching and frequent merges to reduce conflict complexity.
Monitor jobs, logs, and performance metrics proactively after deployment.

Exercises

Type	Questions	Answer
True / False	dbt Cloud automatically runs models without any configuration. (T/F) Ephemeral models are stored permanently in the database. (T/F) Setting a seed helps reproducibility. (T/F)	Answer False — you must configure projects, connections, and schedules. False — ephemeral models exist only during a run and are not stored. True — seeds initialize random generators for reproducible results.
Fill in the Blanks	Use ______________ materialization to persist results as a physical table. Jinja delimiters for expressions are ______________. CI stands for ______________.	Answer table {{ '{{' }} and {{ '}}' }} (i.e., double curly braces for expressions) Continuous Integration
MCQ	Which materialization is best for frequent small updates? A. Table B. View C. Incremental D. Ephemeral Which deployment minimizes downtime by switching traffic between environments? A. Rolling B. Blue-Green C. Recreate D. Canary	Answer C — Incremental is optimized for processing only new/changed records. B — Blue-Green switches traffic between two environments to minimize downtime.
Matching	Match the item on the left with its purpose on the right: Seed Macro Materialization: View	Answer Seed — ensures reproducible randomness. Macro — reusable template code in Jinja. Materialization: View — provides dynamic, always-current results without storing data.
Descriptive	Explain why testing in CI is preferable to manual testing before merging to main.	Answer Automated CI tests run consistently on every change, catching regressions early and ensuring the main branch remains stable. They remove human error, scale across many commits, and provide a repeatable audit trail for test outcomes, which is essential for collaborative teams and production pipelines.
Creative	Design a short checklist for a safe dbt model deployment to production (5 items).	Answer Run full test suite (dbt test) and resolve failures. Validate incremental logic on a staging dataset. Confirm materialization choice and target schema permissions. Schedule a canary run, monitor metrics and logs. Have a documented rollback plan and backup of previous table(s).

Activities

Hands-on lab: Create a dbt Cloud project, connect a sample warehouse, and run your first model.
Write three dbt tests (not_null, unique, relationships) for a chosen model and run dbt test.
Implement a Jinja macro that standardizes timestamp formatting across models.

Vocabulary

Materialization — how a dbt model is persisted (table, view, incremental, ephemeral).
Seed — initial random generator value for reproducibility.
Macro — reusable Jinja template snippet.
CI/CD — Continuous Integration / Continuous Deployment pipeline.

Homework

Document a simple dbt project: list models, materializations, and tests with rationale (one page).
Translate two key concept explanations into Bengali and submit both versions.

Practice Task

Build an incremental model that ingests data from a seed table, applies one test, and is configured to run on a 1-hour schedule in dbt Cloud. Provide screenshots or job logs showing a successful run and the test passing.

End of lesson: Review the Important Notes and complete Activities before the next session.