Large-scale data extraction, structuring and matching using Python and Spark

Motivation - Matching data collections with the aim to augment and integrate the information for any available data point that lies in two or more of these collections, is a problem that nowadays arises often. Notable examples of such data points are scientific publications for which metadata and data are kept in various repositories, and users’ profiles, whose metadata and data exist in several social networks or platforms.

In our case, collections were as follows: (1) A large dump of compressed data files on s3 containing archives in the form of zips, tars, bzips and gzips, which were expected to contain published papers in the form of xmls and pdfs, amongst other files, and (2) A large store of xmls in the form of xmls, some of which are to be matched to Collection 1.

Problem Statement - The problems, then, are: (1) How to best unzip the compressed archives and extract the relevant files? (2) How to extract meta-information from the xml or pdf files? (3) How to match the meta-information from the two different collections? And all of these must be done in a big-data environment.

Presentation –

The presentation will describe the solution process and the use of python and Spark in the large-scale unzipping and extraction of files from archives, and how metadata was then extracted from the files to perform the matches on.