Previously on the following Azure Databricks - Data Pipeline article I present how to build and end to end data pipeline using Azure Databricks and Data Factory. Now I'm doing a similar implementation using an open-source stack leveraging some of the best existing solutions:

• Airbyte: data extraction - http://localhost:56174/
• Dbt: data transformation
• Airflow: task orchestration - http://localhost:8085/
• PostgreSQL: data storage
• Openmetadata: data catalog - http://localhost:8585/
• Minio: object storage - http://localhost:9090/

We will be working with transactional data referred to loan transactions and customers from GeekBankPE (a famous bank around the world).

You have two requirements from different areas of the bank.

• The Marketing area needs to have updated customer data to be able to contact them and make offers.
• The Finance area requires to have daily loan transactions complemented with customer drivers to be able to analyze them and improve the revenue.

To fulfill with the request, we are going to perform incremental loads and also using techniques like upsert.

• Docker Desktop
• Docker compose
• Windows 10 or higher (This project hasn't been tested on MacOS or Linux)
• Python 3.6 or higher
• Recommended 8GB RAM or higher only for docker containers.

• username: airflow
• password: airflow

Minio acces and secret keys along with url_endpoint are used to log in and for connections.

• minio_access_key: minio_admin
• minio_secret_key: minio_password
• url_endpoint: http://host.docker.internal:9000
• port: 9000

• username: dwh
• password: dwh
• external_host: local_host
• external_port: 5455
• internal_host: postgres_dwh
• interal_port: 5432 If you connect to your database through Mysql or DBeaver, you need to use the external host and port.

Enter a valid email when trying to log in.

• For other configurations:
• internal_host: host.docker.internal
• internal_host_and_port: http://host.docker.internal:8000
• user: airbyte
• password: password

• username: admin
• password: admin

In the below youtube video, I show you how to set up everything.

• Open Source Data Stack

1. Open your terminal.
2. Navigate to the root of the os-data-stack repository
3. Run docker compose up --build to initialize the containers. If you want to run it in the background, add -d argument.
4. Perform Airflow configurations (Section below)
5. Run the Airflow DAG called upload_data_to_s3 for uploading csv files into minio s3 bucket.
6. Perform Airbyte configurations (Section below)
7. Run the Airflow DAG called dbt_model to create the tables in the bronze, silver, and gold schemas.
8. Perform Openmetadata configurations. (Section below)
9. Play around all the technologies.

1. Open Airflow
2. Go to connections
3. Create the Minio S3 connection with the below configuration: If you test this connection it will fail, just ignore it.
   • Connection Type: Amazon Web Services
   • Connection Id: aws_default
   • Extra: {"aws_access_key_id": "minio_admin", "aws_secret_access_key": "minio_password", "endpoint_url": "http://host.docker.internal:9000"}
4. Create the Postgres connection
   • Connection Type: Postgres
   • Connection Id: postgres_default
   • Host: postgres_dwh
   • Schema: dwh
   • Login: dwh
   • Password: dwh
   • Port: 5432
5. Create the Airbyte connection (Optional, in case you want to use the stack for your own development)
   • Connection Type: Airbyte
   • Connection Id: airbyte_default
   • Host: host.docker.internal
   • Username: airbyte
   • Password: password
   • Port: 8000

1. Open Airbyte, enter an email and select Get started
2. Select sources (left sidebar) , in the search bar write S3 and select it
3. Create the S3 connection for customer data
   • Source_name: S3_customer_information_cdc
   • Output_stream_name: daily_customer_information_cdc
   • Pattern_of_files_to_replicate: customer/*.csv
   • Bucket: raw
   • Aws_access_key_id: minio_admin
   • Aws_secret_access_key: minio_password
   • Path_prefix: customer/
   • Endpoint: http://host.docker.internal:9000
   • Scroll until the end and select set up source
4. Create the S3 connection for customer driver data
   • Source_name: S3_daily_customer_drivers
   • Output_stream_name: daily_customer_drivers
   • Pattern_of_files_to_replicate: customerDrivers/*.csv
   • Bucket: raw
   • Aws_access_key_id: minio_admin
   • Aws_secret_access_key: minio_password
   • Path_prefix: customerDrivers/
   • Endpoint: http://host.docker.internal:9000
   • Scroll until the end and select set up source
5. Create the S3 connection for loan transactions data
   • Source_name: S3_daily_loan_transactions
   • Output_stream_name: daily_loan_transactions
   • Pattern_of_files_to_replicate: transactions/*.csv
   • Bucket: raw
   • Aws_access_key_id: minio_admin
   • Aws_secret_access_key: minio_password
   • Path_prefix: transactions/
   • Endpoint: http://host.docker.internal:9000
   • Scroll until the end and select set up source
6. Select Destinations (left sidebar), search for Postgres and select it.
7. Create the Postgres connection as destination
   • Destination_name: Postgres_DWH
   • Host: localhost
   • Port: 5455
   • Db_name: dwh
   • Default_Schema: airbyte
   • user: dwh
   • password: dwh
   • Scroll until the end and select set up destination
8. Select Connections (left sidebar)
   • Select the S3_customer_information_cdc source
   • Select Use existing destination
   • In the destination tab select Postgres_DWH and select Use existing destination
   • In the new screen view, change the Replication frequency to Manual
   • Sync mode should be Full refresh overwrite (2023-05-01 - Incremental sync mode isn't working, data gets duplicated when using it, maybe because the Postgres connector is in Alpha)
   • Select set up connection
   • Repeat the same process for S3_daily_customer_drivers and S3_daily_loan_transactions sources.
9. After setting up the connections, select Connections (left side bar) and click on Sync now for your 3 connections.
10. Wait until the syncronization finished.

1. Open openmetadata, enter your credentials
   • username: admin
   • password: admin
2. Select Services in the left sidebar.
3. Select Add New Service in the right top corner.
4. Create the Postgres database service.
   • Select Database Services.
   • Select the Postgres connector and select Next
   • Enter postgres_con as service name and select Next
   • Fill out the fields for the connection:
     • Username: dwh
     • Password: dwh
     • Host_and_Port: postgres_dwh
     • Database: dwh
   • Test your connection and select save
5. Select your connection.
6. Select the tab called Ingestions
7. Create a metadata ingestion by selecting the Add ingestion purple button on the right Mandatory
   • Enable Use FQN For Filtering
   • Enable Include Views
   • Select Next
   • Schedule interval, select None, which means Manual.
   • Finally select Add & Deploy and then View Service
   • Run the metadata ingestion.
8. Create a DBT Ingestion Mandatory (For our example)
   • dbt_Configuration_Source: S3_Config_Source
   • AWS_Access_Key_ID: minio_admin
   • AWS_Secret_Access_Key: minio_password
   • AWS_Region: us-east-1
   • Endpoint_URL: http://host.docker.internal:9000
   • dbt_Bucket_Name: dbt
   • dbt_Object_Prefix: dwh
   • Select Next, choose a Manual schedule interval.
   • Run the DBT ingestion
9. Create Lineage Ingestion Optional
10. Create a Profiler Ingestion Optional
    • Recommended to filter schema and target only: bronze,silver,gold
    • Recommended to play with the value of Profile Sample Type
11. Create a Usage Ingestion Optional

1. Select Services in the left sidebar.
2. Select Add New Service in the right top corner.
3. Select Pipeline Services from the drop down list.
4. Select Airflow and then Next
5. Enter a name for your service connection and select Next
6. Enter the below configuration:
   • Host_and_Port: http://localhost:8085/
   • Metadata_Database_Connection: PostgresConnection
   • Username: airflow
   • Password: airflow
   • Host_and_Port: host.docker.internal:5432
   • Database: airflow
7. Test your connection and save.
8. Navigate to your airflow service connection and create a metadata ingestion.
9. Run the metadata ingestion.

1. Select Services in the left sidebar.
2. Select Add New Service in the right top corner.
3. Select Pipeline Services from the drop down list.
4. Select Airbyte and then Next
5. Enter a name for your service connection and select Next
6. Enter the below configuration:
   • Host_and_Port: http://host.docker.internal:8000
   • Metadata_Database_Connection: PostgresConnection
   • Username: airbyte
   • Password: password
7. Test your connection and save.
8. Navigate to your airflow service connection and create a metadata ingestion.
9. Run the metadata ingestion.

In case you want to do some dbt actions

1. Navigate to the root of the dbt directory
2. Install a python virtual environment by running:
   • Windows: python -m venv env
3. Activate your environment by running:
   • Windows: env/scripts/activate
4. Run the below command to install dbt:
   • Windows: pip install dbt-postgres==1.4.6
5. Navigate to the dwh project by running:
   • cd dwh
6. Now you are able to run dbt commands, follow example below:

• dbt build --profiles-dir "../" --target prod_localhost --vars '{ target_date: 2022-09-12 }' --select gold
• dbt test --profiles-dir "../" --target prod_localhost --vars '{ target_date: 2022-09-12 }' --select gold Note: Some of the tasks will be marked as ERROR when running a DBT command because data is already loaded.