ArchiveSpark is a framework / toolkit / library / API to facilitate efficient data processing, extraction as well as derivation for archival collections.
While originally developed for the use with web archives, which is still its main focus, ArchiveSpark can be used with any (archival) data collections through its modular architecture and customizable data specifications.
What can you do with it?
The main use case of ArchiveSpark is the efficient access to archival data with the goal to derive corpora by applying filters and tools in order to extract information from the original raw data, to be stored in a more accessible format, like JSON, while reflecting the data lineage of each derived value.
Examples of what you can do with it include: (see recipes for code examples)
- Selecting a subset of your data and extracting desired properties (e.g., title, entities, ...)
- Running a (temporal) data analysis on the filtered / extracted / derived data
- Generating hyperlink or knowledge graphs for downstream applications
- Processing archived webpages and extracting embedded resources
- Downloading remote WARC/CDX data from the Internet Archive's Wayback Machine
New in 3.0
- Namespace changed to
- Extensive overhaul to be based on Sparkling, Internet Archive's internal data processing library, which is now partially included under
- ArchiveSpark will evolve as Sparkling evolves and automatically benefit from new features and bugfixes.
- Streamlined with all unused / unnecessary / academic / experimental features being removed.
- Refactored / cleaned up / simplified ArchiveSpark's public APIs.
For more information and instructions, please read the docs:
The MIT License (MIT)
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