Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
- data-drift
- jupyter-notebook
- pandas-dataframe
- machine-learning
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Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
- data-science
- python
- dag
- data-engineering
Easy pipelines for pandas DataFrames.
Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.
Benchmarks for Low Latency (Streaming) solutions including Apache Storm, Apache Spark, Apache Flink, ...
- benchmarks
- low-latency
- streaming
Docker container for Kafka - Spark Streaming - Cassandra
Big Data essentials: Hadoop, MapReduce, Spark. Explore tutorials and demos in Jupyter notebooks—most are self-contained and live, ready to run with a click.
Various examples of notebooks for working with web archives with the Archives Unleashed Toolkit, and derivatives generated by the Archives Unleashed Toolkit.
- notebooks
- pyspark-notebook
- juypter-notebook
- spark
GTFS Data Pipeline for TfNSW Bus Datasets
- gtfs
- gtfs-static
- gtfs-realtime
- tfnsw