dbt research project proposal sample

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A sample project to attempt to highlight most of the features of dbt in one fairly simple repo. It uses common dbt samples projects and adds in some additional useful features.

TheDataFoundryAU/dbt_sample_project

Folders and files, repository files navigation, the data foundry - sample dbt project.

This project is based on the standard jaffle shop model for dbt, including additional other models to replicate a more real world situation.

The purpose of this project is to show how to structure DBT projects as there are a number of ways, and they can conflict with each other if specific parts are not made explicit.

Table of Contents

Materialisation, documentation macros, key areas included.

The standard location for a profile file is ~/.dbt/profiles.yml but if you are running multiple projects (eg. different clients) you might find it easier to keep multiple profile.yml files

The default dbt run command will look in the standard location, so if using multiple profiles use the following command

You can run multiple projects on a dbt profile, or you can build them all into one. If you have multiple projects you will have a copy of this folder structure for each project. DBT looks for a file called dbt_project.yml to define the details of the project.

dbt Doco - Tags

Tags can be used to separate out parts of a model so that it can be run in parts. This might be for dev purposes, different parts needing different run schedules (eg daily and hourly sections) or any other reason you need.

Tags need to be defined in 3 places, the project level, the model level (folder) or within the individual model. This project has some example tags within the base dbt_project.yml configured.

To run dbt for a tagged subset use the following code (assuming using a local profile)

dbt Doco - Tests

Tests are important. Write tests.

Tests can be run against columns or tables. DBT contains only 4 built in tests, but can be expanded as needed with custom tests.

Tests can be run using the dbt test command.

All of the models have tests in them, but custom tests (using dbt_utils) are being used in the mrr model. A custom model of date_format_check is set to run on jaffles.order.order_date. Note that this test may return a SQL error, as the order field is already a date column and there seems to be a bug in Snowflake where running this function on a date breaks things.

Targets (maybe better thought of as stages) allow for you to use your dbt project in different configurations as defined in your profiles.yml file. The sample profile has two targets to show how this might be used.

Targets will use what is defined in the target: key in the profile and can be overridden as needed to run in a different target. Probably the most common use case is leaving as dev , then setting a target or test or prod at runtime as needed. To use a specific target at runtime use the command below

DBT can include additional packages to serve a number of functions. For more info look here and here

dbt-codegen is included in packages as an example, but packages can also be from a git repo too.

To install the dependancies run the following command:

Example usage:

DBT can automatically generate documentation of the environment. This contains a bunch of useful info like the columns, tests being run, the SQL and so on.

dbt also generates lineage graphs as part of the docs. This can be really helpful in debugging when you have a lot of models and dependancies.

To generate the docs run the command below:

To view the docs use this:

Data quality, data standards, consistency, who wants to do all that?! You should!

Thankfully dbt makes that much easier when using this. In the models/jaffles/schema.yml file on the status column you'll see how its used. This, populates into the docs above and makes for some nice easy docs that are referenced in a single place. Notice in the customer_id column you can even include images in the doco if there is value.

Jaffle Shop

Testing dbt project: jaffle_shop.

jaffle_shop is a fictional ecommerce store. This dbt project transforms raw data from an app database into a customers and orders model ready for analytics.

What is this repo?

What this repo is :

• A self-contained playground dbt project, useful for testing out scripts, and communicating some of the core dbt concepts.

What this repo is not :

• A tutorial — check out the Getting Started Tutorial for that. Notably, this repo contains some anti-patterns to make it self-contained, namely the use of seeds instead of sources.
• our standard file naming patterns (which make more sense on larger projects, rather than this five-model project)
• a pull request flow
• CI/CD integrations
• A demonstration of using dbt for a high-complex project, or a demo of advanced features (e.g. macros, packages, hooks, operations) — we're just trying to keep things simple here!

What's in this repo?

This repo contains seeds that includes some (fake) raw data from a fictional app.

The raw data consists of customers, orders, and payments, with the following entity-relationship diagram:

Running this project

To get up and running with this project:

Install dbt using these instructions .

Clone this repository.

Change into the jaffle_shop directory from the command line:

Set up a profile called jaffle_shop to connect to a data warehouse by following these instructions . If you have access to a data warehouse, you can use those credentials – we recommend setting your target schema to be a new schema (dbt will create the schema for you, as long as you have the right privileges). If you don't have access to an existing data warehouse, you can also setup a local postgres database and connect to it in your profile.

Ensure your profile is setup correctly from the command line:

• Load the CSVs with the demo data set. This materializes the CSVs as tables in your target schema. Note that a typical dbt project does not require this step since dbt assumes your raw data is already in your warehouse.
• Run the models:

NOTE: If this steps fails, it might mean that you need to make small changes to the SQL in the models folder to adjust for the flavor of SQL of your target database. Definitely consider this if you are using a community-contributed adapter.

• Test the output of the models:
• Generate documentation for the project:
• View the documentation for the project:

What is a jaffle?

A jaffle is a toasted sandwich with crimped, sealed edges. Invented in Bondi in 1949, the humble jaffle is an Australian classic. The sealed edges allow jaffle-eaters to enjoy liquid fillings inside the sandwich, which reach temperatures close to the core of the earth during cooking. Often consumed at home after a night out, the most classic filling is tinned spaghetti, while my personal favourite is leftover beef stew with melted cheese.

For more information on dbt:

• Read the introduction to dbt .
• Read the dbt viewpoint .
• Join the dbt community .
• Shell 100.0%

Are there any example dbt projects?

• Quickstart Tutorial: You can build your own example dbt project in the quickstart guide
• Jaffle Shop: A demonstration project (closely related to the tutorial) for a fictional ecommerce store ( source code )
• MRR Playbook: A demonstration project that models subscription revenue ( source code , docs )
• Attribution Playbook: A demonstration project that models marketing attribution ( source code , docs )
• GitLab: Gitlab's internal dbt project is open source and is a great example of how to use dbt at scale ( source code , docs )
• dummy-dbt: A containerized dbt project that populates the Sakila database in Postgres and populates dbt seeds, models, snapshots, and tests. The project can be used for testing and experimentation purposes ( source code )
• Google Analytics 4: A demonstration project that transforms the Google Analytics 4 BigQuery exports to various models ( source code , docs )

If you have an example project to add to this list, suggest an edit by clicking Edit this page below.

• GOVERNMENT OF INDIA
• MINISTRY OF SCIENCE & TECHNOLOGY

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Notification for submission of proposal

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• Biotechnology Industry Research Assistance Council

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• BioAngels Program
• Fund of Funds - AcE
• Quest for Assistive Technologies
• Banana Fortification
• Establishing preclinical models for Drug discovery
• DBT BIRAC AMR Mission
• CEFIPRA-BPI France
• DBT-BMGF-BIRAC
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• LEHS (WISH)
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• RAPID USAID-TB Diagnostics Programme
• UKTI (UK Trade & Investment)
• Wellcome Trust
• TATA Trust-Social Alpha
• CSR:Join Hands with BIRAC
• IP & Technology Management Cell
• Call for Proposal

• Call For Proposal

BIRAC Announces 22nd Call for Proposals under the Biotechnology Ignition Grant BIG Scheme : Last Date -15th February 2023 | Call Last date extended to 16th February 2023

Introduction.

Biotechnology Industry Research Assistance Council (BIRAC), a Section 8 ‘ Not-for-Profit Company’  of Government of India, has been set up as DBT’s interface agency, to serve as a single window for emerging biotech companies.

The scheme enables technology innovators and entrepreneurs to pursue a promising technology idea, and establish and validate proof of concept  (POC) for the idea.

Click here to view Guidelines   

Click here to register for BIG Scheme

KEY FEATURES OF THE CALL

• Supports high level innovation in the Biotech sector and is not meant for basic research projects.
• Grant-in-aid up-to INR 50 lakhs for a maximum period of 18 months
• Implementation and Mentoring through BIRAC’s BIG Partners

WHO CAN APPLY?

BIRAC invites proposals from Biotech Start-ups and Individual Entrepreneurs with innovative idea having potential for commercialization. Eligible entities include:

• Start up age allowed for a registered company/ LLP incorporated under BIG w.e.f 1st Aug 2021: 5 years
• Scientists, Faculty, Research Scholars, Graduates in any discipline incubating/ intend to incubate at a BioIncubator 

Eligibility criteria for startups under BIG has been revised. 

HOW TO APPLY?

BIG scheme allows only online submission of proposals. For proposal submission please register on BIRAC website under “BIG User”. User registration is open round the clock. For further details on the scheme, eligibility criteria, FAQs and registration log on to  www.birac.nic.in .

Note: Prior recipients of BIG grant are ineligible

For any queries, please contact:

Email:  [email protected] ;  [email protected]

ANNOUNCEMENT DATE | LAST SUBMISSION DATE

Announcement Date: 01-01-2023 Last Submission Date: 16-02-2023 05:30 PM

Annual Reports

Dbt-birac mou, tenders  , vacancy , loan non-repayment, supported projects  .

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This is the official Website of Biotechnology Industry Research Assistance Council (BIRAC) , a Public Sector Enterprise, set up by Department of Biotechnology (DBT), Government of India © 2024

Last Updated: 03 May, 2024