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https://github.com/stefen-taime/iceberg-dbt-trino-hive-modern-open-source-data-stack

To provide a deeper understanding of how the modern, open-source data stack consisting of Iceberg, dbt, Trino, and Hive operates within a music streaming platform, let’s delve into the detailed workflow and benefits of each component.
https://github.com/stefen-taime/iceberg-dbt-trino-hive-modern-open-source-data-stack

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To provide a deeper understanding of how the modern, open-source data stack consisting of Iceberg, dbt, Trino, and Hive operates within a music streaming platform, let’s delve into the detailed workflow and benefits of each component.

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# Iceberg + Dbt + Trino + Hive: Modern, Open-Source Data Stack



![](https://cdn-images-1.medium.com/max/3086/1*Hg-ZHsBd_yJ54legC570oA.png)



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.



## Run the Local Trino Server



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.



## Integration with Kafka for Data Streaming



To simulate real-time data streaming in a music event context, follow the instructions from the GitHub repository [Stefen-Taime/eventmusic](https://github.com/Stefen-Taime/eventmusic.git). 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.



### Preparing Kafka Connectors



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

   ```



### Configuring Connectors and Producing Data



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

   ```



## Run the Dbt Commands



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.  



## Get Superset



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](https://github.com/apache/superset/releases) for the latest version.

   ```

   export SUPERSET_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`