The repository showcases a demo of integrating Iceberg, Dbt, Trino, and Hive, forming a modern and open-source data stack suitable for various analytical needs. This guide provides a structured approach to setting up and utilizing this stack effectively, ensuring a seamless workflow from data ingestion to analysis.

Before diving into the specifics of data transformation and analysis with Dbt, it's essential to have the Trino server up and running. Trino serves as a distributed SQL query engine that allows you to query your data across different sources seamlessly. Here's how to start the Trino server locally using Docker:

cd docker
docker-compose up --build -d

This command navigates to the Docker directory within your project and initiates the Docker Compose process, which builds and starts the containers defined in your docker-compose.yml file in detached mode.

To simulate real-time data streaming in a music event context, follow the instructions from the GitHub repository Stefen-Taime/eventmusic. This repository contains scripts and configurations necessary for producing messages to Kafka, which acts as the backbone for real-time data handling in this stack.

After setting up the Docker containers and running the local Trino server, proceed with the Kafka connectors setup:

1. Set Permissions for install_connectors.sh: This script installs the necessary Kafka connectors for integrating with PostgreSQL and MongoDB. Adjust the file permissions to make it executable.

   chmod +x install_connectors.sh
   

2. Execute install_connectors.sh: Run the script to install the Kafka connectors.

   ./install_connectors.sh
   

With the connectors installed:

1. Set Permissions for postConnect.sh: This script configures the connectors. Modify the permissions to ensure executability.

   chmod +x postConnect.sh
   

2. Execute postConnect.sh: Run the script to configure the connectors and initiate data streaming.

   ./postConnect.sh
   

With the Trino server running, the next step is to execute the necessary Dbt commands to manage your data transformations:

dbt deps
dbt run

dbt deps fetches the project's dependencies, ensuring that all required packages and modules are available. dbt run then executes the transformations defined in your dbt project, building your data models according to the specifications in your dbt files.

To get started with Apache Superset, follow these steps to pull and run the Superset Docker image. Ensure you have Docker installed and running on your machine.

1. Set Superset Version: Set the SUPERSET_VERSION environment variable with the latest Superset version. Check the Apache Superset releases for the latest version.

   export SUPERSET_VERSION=<latest_version>
   

2. Pull Superset Image: Pull the Superset image from Docker Hub.

   docker pull apache/superset:$SUPERSET_VERSION
   

3. Start Superset: Note that Superset requires a user-specified value of SECRET_KEY or SUPERSET_SECRET_KEY as an environment variable to start.

   docker run -d -p 3000:8088 \
              -e "SUPERSET_SECRET_KEY=$(openssl rand -base64 42)" \
              -e "TALISMAN_ENABLED=False" \
              --name superset apache/superset:$SUPERSET_VERSION
   

4. Create an Account: Create an admin account in Superset.

   docker exec -it superset superset fab create-admin \
               --username admin \
               --firstname Admin \
               --lastname Admin \
               --email admin@localhost \
               --password admin
   

5. Configure Superset: Configure the database and load example data.

   docker exec -it superset superset db upgrade && \
          docker exec -it superset superset load_examples && \
          docker exec -it superset superset init
   

6. Start Using Superset: After configuration, access Superset at http://localhost:8080 with the default credentials:

   • Username: admin
   • Password: admin