<img align="right" style="padding:35px" src="notebooks/images/SCP_med.png" width="160">
# Zeek Analysis Tools (ZAT)
 [](http://codecov.io/github/SuperCowPowers/zat?branch=master) [](https://pypi.python.org/pypi/zat) [](https://choosealicense.com/licenses/apache-2.0)
The ZAT Python package supports the processing and analysis of Zeek data
with Pandas, scikit-learn, Kafka, and Spark
### Install
```
pip install zat
pip install zat[pyspark] (includes pyspark library)
pip install zat[all] (include pyarrow, yara-python, and tldextract)
```
### Getting Started
- [Examples of Using ZAT](https://supercowpowers.github.io/zat/examples.html)
### Installing on Raspberry Pi!
- [Raspberry Pi Instructions](https://supercowpowers.github.io/zat/raspberry_pi.html)
### Recent Improvements
- Faster/Smaller Pandas Dataframes for large log files: [Large Dataframes](https://supercowpowers.github.io/zat/large_dataframes.html)
- Better Panda Dataframe to Matrix (ndarray) support: [Dataframe To Matrix](https://supercowpowers.github.io/zat/dataframe_to_matrix.html)
- Scalable conversion from Zeek logs to Parquet: [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Parquet.ipynb)
- Vastly improved Spark Dataframe Class: [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Spark.ipynb)
- Updated/improved Notebooks: [Analysis Notebooks](#analysis-notebooks)
- Zeek JSON to DataFrame class: [Zeek JSON to DataFrame Example](https://github.com/SuperCowPowers/zat/blob/main/examples/zeek_json_to_pandas.py)
### Video Presentation
- [Data Analysis and Machine Learning with Zeek](https://www.youtube.com/watch?v=pG5lU9CLnIU)
### Why ZAT?
Zeek already has a flexible, powerful scripting language why should I use
ZAT?
**Offloading:** Running complex tasks like statistics, state machines,
machine learning, etc.. should be offloaded from Zeek so that Zeek can
focus on the efficient processing of high volume network traffic.
**Data Analysis:** We have a large set of support classes that help
bridge from raw Zeek data to packages like Pandas, scikit-learn, Kafka, and
Spark. We also have example notebooks that show step-by-step how to get
from here to there.
### Analysis Notebooks
- [Zeek to Scikit-Learn](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Scikit_Learn.ipynb)
- [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Parquet.ipynb)
- [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Spark.ipynb)
- [Spark Clustering](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Spark_Clustering.ipynb)
- [Zeek to Kafka](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Kafka.ipynb)
- [Zeek to Kafka to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Kafka_to_Spark.ipynb)
- [Clustering: Picking K (or not)](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Clustering_Picking_K.ipynb)
- [Anomaly Detection Exploration](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Anomaly_Detection.ipynb)
- [Risky Domains Stats and Deployment](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Risky_Domains.ipynb)
- [Zeek to Matplotlib](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Plot.ipynb)
<img align="right" style="padding: 10px" src="notebooks/images/SCP_med.png" width="120">
### Documentation
<https://supercowpowers.github.io/zat/>
#### Running the Tests
```
pip install pytest coverage pytest-cov
pytest zat
```
### About SuperCowPowers
The company was formed so that its developers could follow their passion for Python, streaming data pipelines and having fun with data analysis. We also think cows are cool and should be superheros or at least carry around rayguns and burner phones. <a href="https://www.supercowpowers.com" target="_blank">Visit SuperCowPowers</a>
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"description": "<img align=\"right\" style=\"padding:35px\" src=\"notebooks/images/SCP_med.png\" width=\"160\">\n\n# Zeek Analysis Tools (ZAT) \n [](http://codecov.io/github/SuperCowPowers/zat?branch=master) [](https://pypi.python.org/pypi/zat) [](https://choosealicense.com/licenses/apache-2.0)\n\nThe ZAT Python package supports the processing and analysis of Zeek data\nwith Pandas, scikit-learn, Kafka, and Spark\n\n### Install\n```\npip install zat\npip install zat[pyspark] (includes pyspark library)\npip install zat[all] (include pyarrow, yara-python, and tldextract)\n```\n\n### Getting Started\n- [Examples of Using ZAT](https://supercowpowers.github.io/zat/examples.html)\n\n### Installing on Raspberry Pi!\n- [Raspberry Pi Instructions](https://supercowpowers.github.io/zat/raspberry_pi.html)\n\n### Recent Improvements\n- Faster/Smaller Pandas Dataframes for large log files: [Large Dataframes](https://supercowpowers.github.io/zat/large_dataframes.html)\n- Better Panda Dataframe to Matrix (ndarray) support: [Dataframe To Matrix](https://supercowpowers.github.io/zat/dataframe_to_matrix.html)\n- Scalable conversion from Zeek logs to Parquet: [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Parquet.ipynb)\n- Vastly improved Spark Dataframe Class: [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Spark.ipynb)\n- Updated/improved Notebooks: [Analysis Notebooks](#analysis-notebooks)\n- Zeek JSON to DataFrame class: [Zeek JSON to DataFrame Example](https://github.com/SuperCowPowers/zat/blob/main/examples/zeek_json_to_pandas.py)\n\n### Video Presentation\n- [Data Analysis and Machine Learning with Zeek](https://www.youtube.com/watch?v=pG5lU9CLnIU)\n\n### Why ZAT?\nZeek already has a flexible, powerful scripting language why should I use\nZAT?\n\n**Offloading:** Running complex tasks like statistics, state machines,\nmachine learning, etc.. should be offloaded from Zeek so that Zeek can\nfocus on the efficient processing of high volume network traffic.\n\n**Data Analysis:** We have a large set of support classes that help\nbridge from raw Zeek data to packages like Pandas, scikit-learn, Kafka, and\nSpark. We also have example notebooks that show step-by-step how to get\nfrom here to there.\n\n### Analysis Notebooks\n\n- [Zeek to Scikit-Learn](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Scikit_Learn.ipynb)\n- [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Parquet.ipynb)\n- [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Spark.ipynb)\n- [Spark Clustering](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Spark_Clustering.ipynb)\n- [Zeek to Kafka](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Kafka.ipynb)\n- [Zeek to Kafka to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Kafka_to_Spark.ipynb)\n- [Clustering: Picking K (or not)](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Clustering_Picking_K.ipynb)\n- [Anomaly Detection Exploration](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Anomaly_Detection.ipynb)\n- [Risky Domains Stats and Deployment](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Risky_Domains.ipynb)\n- [Zeek to Matplotlib](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/main/notebooks/Zeek_to_Plot.ipynb)\n\n<img align=\"right\" style=\"padding: 10px\" src=\"notebooks/images/SCP_med.png\" width=\"120\">\n\n### Documentation\n<https://supercowpowers.github.io/zat/>\n\n#### Running the Tests\n```\npip install pytest coverage pytest-cov\npytest zat\n```\n\n### About SuperCowPowers\nThe company was formed so that its developers could follow their passion for Python, streaming data pipelines and having fun with data analysis. We also think cows are cool and should be superheros or at least carry around rayguns and burner phones. <a href=\"https://www.supercowpowers.com\" target=\"_blank\">Visit SuperCowPowers</a>\n",
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