https://github.com/zahidgalea/dbt-hudi-lake
Delta Lake with Hudi and DBT as ELT tool
https://github.com/zahidgalea/dbt-hudi-lake
Last synced: 3 months ago
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Delta Lake with Hudi and DBT as ELT tool
- Host: GitHub
- URL: https://github.com/zahidgalea/dbt-hudi-lake
- Owner: ZahidGalea
- Created: 2024-02-08T22:45:49.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-03T02:12:11.000Z (over 1 year ago)
- Last Synced: 2024-03-03T03:21:47.549Z (over 1 year ago)
- Language: Python
- Homepage: https://www.linkedin.com/in/zahidgalea
- Size: 19.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Delta / DBT / Elementary
---
---
### Checkmarks
* [x] Base docker compose configuration
* [x] PostgreSQL with Hive metastore configuration
* [x] Apache Hudi with Spark Thrift Server configuration
* [x] DBT with connection to Spark Thrift Server and test model
* [ ] Elementary integration
* [ ] Run Book documentation
## Introduction
This document outlines the Proof of Concept (PoC) implemented to demonstrate the integration and functioning of Apache Hudi, DBT (Data Build Tool), and Elementary within a data processing environment. The aim is to assess the efficiency, scalability, and data quality management achievable by leveraging these technologies together.
## Technology Description
* **DBT (Data Build Tool)**: A data transformation tool that enables data teams to model and transform data in their data warehouse. It uses SQL for transformations and integrates with version control systems to maintain a change history and facilitate collaboration.
* **Apache Hudi**: A storage layer that brings ACID transactions, scalability, and reliability to data storage systems in a data lake format. It supports concurrent read and write operations, ensuring data integrity.
* **Spark Thrift Server**: Provides a JDBC/ODBC interface that allows external applications to connect to Spark SQL using standard protocols. It facilitates interoperability and access to data processed by Spark.
* **Hive Metastore in PostgreSQL**: The Hive Metastore stores table and partition metadata in a relational data store, in this case, PostgreSQL. This enables efficient and scalable management of metadata, crucial for complex data operations.
## How to replicate.
1. Clone this repo.
2. Build the images.
```bash
docker-compose build --no-cache
```
3. Run the containers.
```bash
docker-compose up
```