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streaming_data_processing

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Data Streaming with Debezium, Kafka, Spark Streaming, Delta Lake, and MinIO

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// repository documentation

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# Data Streaming with Debezium, Kafka, Spark Streaming, Delta Lake, and MinIO

## Overview

- Produce persistent data to **PostgreSQL**.
- Identify and track changes to data in a database using **Debezium** Connector.
- Read the streaming data from **Kafka topic** using **PySpark (Spark Streaming)**.
- Convert to **Delta Lake** format and write the streaming data to **MinIO (AWS Object Storage)**.

- Use **Trino** to query data and display in **DBeaver**.

## System Architecture

<p align="center">
    System Architecture
</p>

## Prerequisites

Before running this script, ensure you have the following installed:

- Python 3.10
- Docker
- Debezium (Debezium UI)
- PostgreSQL
- Confluent Containers (Zookeeper, Kafka, Schema Registry, Connect, Control Center)
- Apache Spark
- Delta Lake
- MinIO, Trino, DBeaver

## Getting Started

1.  **Clone the repository**:

```bash
    git clone https://github.com/trannhatnguyen2/streaming_data_processing
    ```

2.  **Start our data streaming infrastructure**

```bash
    docker compose -f docker-compose.yaml -f storage-docker-compose.yaml up -d
    ```

This command will download the necessary Docker images, create containers, and start the services in detached mode.

3.  **Setup environment**

```bash
    conda create -n streaming python==3.10
    y
    conda activate streaming
    pip install -r requirements.txt
    ```

Activate your conda environment and install required packages

4.  **Access the Services**

- Postgres is accessible on the default port `5432`.
    - Debezium UI is accessible at `http://localhost:8080`.
    - Kafka Control Center is accessible at `http://localhost:9021`.
    - MinIO is accessible at `http://localhost:9001`.

## How-to Guide

1.  **Create Connector Postgres to Debezium**

Firstly, modifying your config in configs/orders-cdc.json

```bash
    cd debezium/
    bash run.sh register_connector configs/orders-cdc.json
    ```

2.  **Create an empty table in PostgreSQL and insert new record to the table**

```bash
    python utils/create_table.py
    python utils/insert_table.py
    ```

<!-- **Note:** Print records as json format in terminal

```bash
    python streaming/json_consume_message.py
    ``` -->

<!-- 3.  **Produce data to Kafka topic**

```bash
    cd data_ingestion/kafka_producer
    python produce_json.py
    ``` -->

3.  **Read and store data to MinIO**

```bash
    python spark_streaming/orders_delta_spark_to_minio.py
    ```

Read data in `Kafka Topic`then push them to `MinIO` with `delta lake` format

## Read data streaming in MinIO

### Create data schema

After putting your files to ` MinIO`, please execute `trino` container by the following command:

```bash
docker exec -ti datalake-trino bash
```

When you are already inside the `trino` container, typing `trino` to in an interactive mode

After that, run the following command to register a new schema for our data:

```sql

CREATE SCHEMA IF NOT EXISTS lakehouse.orders
    WITH (location = 's3://lakehouse/');

CREATE TABLE IF NOT EXISTS lakehouse.orders.orders (
    event_timestamp TIMESTAMP(3) WITH TIME ZONE,
    order_date VARCHAR,
    order_time VARCHAR,
    order_number VARCHAR,
    order_line_number TINYINT,
    customer_name VARCHAR,
    product_name VARCHAR,
    store VARCHAR,
    promotion VARCHAR,
    order_quantity TINYINT,
    unit_price DOUBLE,
    unit_cost DOUBLE,
    unit_discount DOUBLE,
    sales_amount DOUBLE
    ) WITH (
    location = 's3://lakehouse/orders'
    );

```

## Query with DBeaver

1. Install `DBeaver` as in the following [guide](https://dbeaver.io/download/)
2. Connect to our database (type `trino`) using the following information (empty `password`):
   ![DBeaver Trino](https://raw.githubusercontent.com/trannhatnguyen2/streaming_data_processing/main/imgs/trino.png)

---

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