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flat files, flat land
plateau is a Python library to manage (create, read, update, delete) large
amounts of tabular data in a blob store. It stores data as datasets, which
it presents as pandas DataFrames to the user. Datasets are a collection of
files with the same schema that reside in a blob store. plateau uses a metadata
definition to handle these datasets efficiently. For distributed access and
manipulation of datasets plateau offers a Dask interface.
Storing data distributed over multiple files in a blob store (S3, ABS, GCS, etc.) allows for a fast, cost-efficient and highly scalable data infrastructure. A downside of storing data solely in an object store is that the storages themselves give little to no guarantees beyond the consistency of a single file. In particular, they cannot guarantee the consistency of your dataset. If we demand a consistent state of our dataset at all times, we need to track the state of the dataset. plateau frees us from having to do this manually.
plateau.io module provides building blocks to create and modify these
datasets in data pipelines. plateau handles I/O, tracks dataset partitions
and selects subsets of data transparently.
Installers for the latest released version are availabe at the Python package index and on conda-forge.
# Install with pip pip install plateau
# Install with conda/mamba, optionally add conda-forge as a source # conda config --add channels conda-forge mamba install plateau
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