Data Cockpit
Data Cockpit is an interactive IPython widget built on top of the Dataplug framework. It enables scientists and engineers to:
- Upload and browse datasets in Amazon S3
- Explore curated public and Metaspace collections
- Benchmark performance to discover optimal batch sizes
- Partition a variety of scientific data types into chunks or batches
- Integrate seamlessly into Jupyter notebooks for elastic, parallel workloads
Why Data Cockpit?
Built on Dataplug’s Cloud-Aware Partitioning
Dataplug is a client-side Python framework for dynamic, zero-cost data slicing of unstructured scientific data stored in object stores like S3. It:
- Pre-processes data in a read-only fashion, building lightweight indexes decoupled from the raw objects
- Exploits S3 byte-range reads to parallelize high-bandwidth access across many workers
- Supports a plug-in interface for multiple domains:
- Generic: CSV, raw text
- Genomics: FASTA, FASTQ, VCF
- Geospatial: LiDAR, Cloud-Optimized Point Cloud (COPC), COG
- Metabolomics: ImzML
- Allows re-partitioning with different strategies without rewriting the original data
What Data Cockpit Adds
While Dataplug focuses on efficient data slicing, Data Cockpit provides an end-to-end Jupyter UI that:
- Uploads your local files directly into any S3 bucket
- Browses existing buckets or public datasets from the AWS Open Data Registry
- Runs benchmarks across a configurable range of batch sizes to find the fastest throughput
- Processes & partitions your data with one click, displaying progress and results entirely in-notebook
- Retrieves partitions via
get_data_slices(), which returns the DataPlug data slices (metadata) for downstream processing
Installation
pip install cloud-data-cockpit
Or install both Data Cockpit and geospatial extras together:
pip install cloud-data-cockpit[geospatial]
2 maintainers
Metadata
2 maintainers
Release files for cloud-data-cockpit 1.0.4
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