Interview enhancements

Dockerfile

@@ -8,6 +8,7 @@ WORKDIR /usr/src/dbt/dbt_project
 # Install the dbt Postgres adapter. This step will also install dbt-core
 RUN pip install --upgrade pip
 RUN pip install dbt-postgres==1.3.1
+RUN pip install pytz
 
 # Install dbt dependencies (as specified in packages.yml file)
 # Build seeds, models and snapshots (and run tests wherever applicable)

README.md

@@ -8,34 +8,21 @@ The `docker-compose.yml` file consists of two services:
 that are used to build the data models defined in the example project into a target Postgres database.
 
 ## `postgres` service and the Sakila Database
-This is an instance of a Postgres database initialised with Sakila database (and thus we are using the 
-`frantiseks/postgres-sakila` image which is available on Docker Hub). 
+This is an instance of a Postgres database initialised with Sakila database (and thus we are using the
+`frantiseks/postgres-sakila` image which is available on Docker Hub).
 
-The database models a DVD rental store and contains several normalised tables that correspond to films, payments, 
+The database models a DVD rental store and contains several normalised tables that correspond to films, payments,
 customers and other entities.
 
-Sakila Database was developed by Mike Hillyer, who used to be a member of the AB documentation team at MySQL. For more 
-information regarding Sakila Database you can refer to the 
-[official MySQL documentation](https://dev.mysql.com/doc/sakila/en/sakila-introduction.html). 
+Sakila Database was developed by Mike Hillyer, who used to be a member of the AB documentation team at MySQL. For more
+information regarding Sakila Database you can refer to the
+[official MySQL documentation](https://dev.mysql.com/doc/sakila/en/sakila-introduction.html).
+![Sakila DB](https://www.jooq.org/img/sakila.png)
+
 
 ## `dbt` service
 This service is built out of the `Dockerfile` and is responsible for creating dbt seeds, models and snapshots
-on `postgres` service. The example dbt project contains seeds, models (staging, intermediate and mart) as well as 
-snapshots. 
-
-Note that this is a dummy project, meaning that some entities (including aggregations) might not make too much sense
-from a business perspective. For example, even though the Sakila database contains the `customer` table already, we
-construct another table called `customer_base` that corresponds to a dbt seed, and is loaded form an external 
-`csv` file.
-
-Additionally, the models created may not be the perfect examples of what it should be considered as an intermediate or 
-mart model. In general if you are interested in gaining a deeper understanding of these terms I would encourage you to 
-read the following articles:
-- [Staging vs Intermediate vs Mart models in dbt](https://towardsdatascience.com/staging-intermediate-mart-models-dbt-2a759ecc1db1)
-- [How to structure your dbt project and data models](https://towardsdatascience.com/dbt-models-structure-c31c8977b5fc)
-
-Feel free to add, modify or remove models while cloning or forking the project in order to serve the purpose you 
-intend to use it for. 
+snapshots.
 
 
 ## Running the dummy dbt project
@@ -45,7 +32,7 @@ First, let's build the services defined in our `docker-compose.yml` file:
 docker-compose build
 ```
 
-and now let's run the services so that the dbt models are created in our target Postgres database: 
+and now let's run the services so that the dbt models are created in our target Postgres database:
 
 ```commandline
 docker-compose up
@@ -56,13 +43,13 @@ This will spin up two containers namely `dbt` (out of the `dbt-dummy` image) and
 
 Notes:
 - For development purposes, both containers will remain up and running
-- If you would like to end the `dbt` container, feel free to remove the `&& sleep infinity` in `CMD` command of the 
+- If you would like to end the `dbt` container, feel free to remove the `&& sleep infinity` in `CMD` command of the
 `Dockerfile`
 
 
 ### Building additional or modified data models
-Once the containers are up and running, you can still make any modifications in the existing dbt project 
-and re-run any command to serve the purpose of the modifications. 
+Once the containers are up and running, you can still make any modifications in the existing dbt project
+and re-run any command to serve the purpose of the modifications.
 
 In order to build your data models, you first need to access the container.
 
@@ -76,41 +63,16 @@ Then enter the running container:
 docker exec -it <container-id> /bin/bash
 ```
 
-And finally:
-
-```commandline
-# Install dbt deps
-dbt deps
-
-# Build seeds
-dbt seeds --profiles-dir profiles
-
-# Build data models
-dbt run --profiles-dir profiles
-
-# Build snapshots
-dbt snapshot --profiles-dir profiles
-
-# Run tests
-dbt test --profiles-dir profiles
-```
-
-Alternatively, you can run everything in just a single command:
-
-```commandline
-dbt build --profiles-dir profiles
-```
-
 ### Querying seeds, models and snapshots on Postgres
-In order to query and verify the seeds, models and snapshots created in the dummy dbt project, simply follow the 
-steps below. 
+In order to query and verify the seeds, models and snapshots created in the dummy dbt project, simply follow the
+steps below.
 
 Find the container id of the postgres service (`postgres`):
 ```commandline
-docker ps 
+docker ps
 ```
 
-Then run 
+Then run
 ```commandline
 docker exec -t <container-id> /bin/bash
 ```
@@ -119,22 +81,3 @@ We will then use `psql`, a terminal-based interface for PostgreSQL that allows u
 ```commandline
 psql -U postgres
 ```
-
-Now you can query the tables constructed form the seeds, models and snapshots defined in the dbt project:
-```sql
--- Query seed tables
-SELECT * FROM customer_base;
-
--- Query staging views
-SELECT * FROM stg_payment;
-
--- Query intermediate views
-SELECT * FROM int_customers_per_store;
-SELECT * FROM int_revenue_by_date;
-
--- Query mart tables
-SELECT * FROM cumulative_revenue;
-
--- Query snapshot tables
-SELECT * FROM int_stock_balances_daily_grouped_by_day_snapshot;
-```

dbt_project/.gitignore

@@ -2,3 +2,4 @@
 target/
 dbt_packages/
 logs/
+.user.yml

dbt_project/dbt_project.yml

@@ -1,38 +1,17 @@
-
-# Name your project! Project names should contain only lowercase characters
-# and underscores. A good package name should reflect your organization's
-# name or the intended use of these models
 name: 'test_dbt_project'
 version: '1.0.0'
 config-version: 2
 
-# This setting configures which "profile" dbt uses for this project.
 profile: 'test_profile'
 
-# These configurations specify where dbt should look for different types of files.
-# The `model-paths` config, for example, states that models in this project can be
-# found in the "models/" directory. You probably won't need to change these!
 model-paths: ["models"]
 analysis-paths: ["analyses"]
 test-paths: ["tests"]
 seed-paths: ["seeds"]
 macro-paths: ["macros"]
 snapshot-paths: ["snapshots"]
 
-target-path: "target"  # directory which will store compiled SQL files
-clean-targets:         # directories to be removed by `dbt clean`
+target-path: "target"
+clean-targets:
   - "target"
   - "dbt_packages"
-
-
-# Configuring models
-# Full documentation: https://docs.getdbt.com/docs/configuring-models
-
-# In this example config, we tell dbt to build all models in the example/ directory
-# as tables. These settings can be overridden in the individual model files
-# using the `{{ config(...) }}` macro.
-#models:
-#  test_dbt_project:
-#    # Config indicated by + and applies to all files under models/example/
-#    example:
-#      +materialized: view

dbt_project/models/schema.yml

@@ -0,0 +1,11 @@
+version: 2
+
+models:
+  - name: my_model_name
+    description: >-
+      Description of the model.
+
+    columns:
+      - name: column_name
+        description: >-
+          Description of the column.

dbt_project/packages.yml

@@ -1,3 +1,3 @@
 packages:
   - package: dbt-labs/dbt_utils
-    version: 1.0.0
+    version: 1.1.1

dbt_project/profiles/.user.yml

@@ -0,0 +1 @@
+id: eb8bacf2-ac6f-4e63-a184-7e6a351e42a3