https://github.com/turakulov/datalakehouse
A project to create a Data Lake House
https://github.com/turakulov/datalakehouse
airflow datavault dbt docker dwh etl iceberg kafka s3 spark trino
Last synced: 3 months ago
JSON representation
A project to create a Data Lake House
- Host: GitHub
- URL: https://github.com/turakulov/datalakehouse
- Owner: Turakulov
- Created: 2025-01-18T10:56:12.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-23T21:38:53.000Z (over 1 year ago)
- Last Synced: 2025-03-23T22:32:19.706Z (over 1 year ago)
- Topics: airflow, datavault, dbt, docker, dwh, etl, iceberg, kafka, s3, spark, trino
- Language: Python
- Homepage:
- Size: 151 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🏗️ Data Lakehouse with Modern Technologies
## 📌 Overview
This project demonstrates how to build a **Data Lakehouse** using modern open-source technologies.
It integrates **Kafka**, **Spark Streaming**, **Apache Iceberg**, **Apache Nessie**, **Trino**, and **DBT** to create an efficient data pipeline.
🔹 **Streaming Layer:** Spark Streaming / Flink processes real-time Kafka events.
🔹 **Batch Layer:** Apache Spark and AirByte handle batch ingestion into an **S3 Minio** data lake.
🔹 **Metadata Layer:** Apache Nessie is used as the Iceberg catalog, backed by PostgreSQL.
🔹 **Transformation Layer:** Trino and DBT transform and store data in **Vertica** for analytics.
🔹 **Orchestration:** Apache Airflow schedules and manages all ETL/ELT workflows.
🔹 **Synthetic Data:** Generated with `sdv` Python library from **SalesDB_v1** dataset.
🔹 **Visualization:** Dashboards built in **Tableau, Superset, or Power BI**.
## 🚀 Tech Stack
| Category | Technology |
|------------------|------------|
| **Streaming** | Kafka, Spark Streaming, Flink |
| **Storage** | S3 Minio (Apache Iceberg) |
| **Metadata** | Apache Nessie, PostgreSQL |
| **ETL/ELT** | Airflow, DBT, Apache Spark |
| **Query Engine** | Trino, Vertica |
| **Orchestration**| Apache Airflow |
| **Dashboarding** | Tableau, Superset, Power BI |
---
## 🎯 Architecture
### 1️⃣ **Data Ingestion**
- Streaming events flow from **Kafka** into **Spark Streaming / Flink**.
- Batch data is ingested via **AirByte** into **S3 Minio (Iceberg format)**.
### 2️⃣ **Storage & Metadata Management**
- Raw (`raw`) and Operational (`ods`) data are stored in **S3 Minio (Apache Iceberg)**.
- Apache Nessie acts as the metadata catalog, tracking schema versions and changes.
### 3️⃣ **Transformation & Querying**
- **DBT + Trino** handle transformations.
- Processed marts (`marts`) are stored in **Vertica** for BI consumption.
### 4️⃣ **Orchestration & Visualization**
- **Apache Airflow** schedules all ETL/ELT jobs.
- Dashboards are created using **Tableau, Superset, or Power BI**.
---
## 📸 Screenshots
🔹 **Kafka UI** (Monitor real-time events)
🔹 **S3 Minio Browser** (View stored Iceberg tables)
🔹 **Apache Nessie UI** (Track schema changes)
🔹 **Airflow DAGs** (Monitor ETL workflows)
🔹 **DBT Lineage Graph** (View transformation dependencies)
🔹 **BI Dashboards** (Analytics & insights)
---
## 🏗️ **Setup & Deployment**
This project is fully containerized using **Docker Compose**.
### 🔧 **Prerequisites**
- Docker & Docker Compose
- Python (for data generation)
### 📥 **Clone Repository**
```bash
git clone https://github.com/Turakulov/datalakehouse.git
cd datalakehouse
```
### ▶️ **Start the Environment**
```bash
docker-compose build
docker-compose up -d
```
### 🔎 **Verify Services**
- **Minio Console:** [http://localhost:9001](http://localhost:9001)
- **Trino UI:** [http://localhost:8083](http://localhost:8083)
- **Kafka UI:** [http://localhost:9999](http://localhost:9999)
- **Airflow UI:** [http://localhost:8090](http://localhost:8090)
- **Vertica (SQL Access):** `jdbc:vertica://localhost:5433/db`
---
## 🛠️ **Project Structure**
```bash
📂 datalakehouse
├── 📂 airflow/ # Apache Airflow DAGs
├── 📂 spark/ # Spark Streaming jobs
├── 📂 kafka/ # Kafka configurations
├── 📂 trino/ # Trino catalogs
├── 📂 minio/ # Minio storage setup
├── 📂 nessie/ # Apache Nessie metadata
├── 📂 postgres/ # Postgres storage setup for Apache Iceberg metadata
├── 📂 vertica/ # Vertica storage setup for datamarts and OLAP queries
├── 📜 docker-compose.yaml # Docker environment
└── 📜 README.md # Project documentation
```
## 📌 **Contributing**
🔹 Fork the repo & create a feature branch.
🔹 Submit a pull request with detailed changes.