kartothek - manage tabular data in object stores¶
|Date:||Apr 02, 2020|
Kartothek 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. Kartothek uses a
metadata definition to handle these datasets efficiently. For distributed
access and manipulation of datasets, Kartothek 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. Kartothek frees us from having to do this manually.
kartothek.io module provides building blocks to create and modify
these datasets in data pipelines. Kartothek handles I/O, tracks dataset
partitions and selects subsets of data transparently.
What is a (real) Kartothek?¶
A Kartothek (or more modern: Zettelkasten/Katalogkasten) is a tool to organize (high-level) information extracted from a source of information.