Probabilistic Record Linking In Spark

Tom Lous builds a solution to link similar companies together by address:

Recently a colleague asked me to help her with a data problem, that seemed very straightforward at a glance.
She had purchased a small set of data from the chamber of commerce (Kamer van Koophandel: KvK) that contained roughly 50k small sized companies (5–20FTE), which can be hard to find online.
She noticed that many of those companies share the same address, which makes sense, because a lot of those companies tend to cluster in business complexes.

Read on for the solution.  Like many data problems, it turns out to be a lot more complicated than you’d think at first glance.

Related Posts

Understanding A Spark Streaming Workflow

Himanshu Gupta continues a series on structured streaming using Spark Streaming: Here we can clearly see that if new data is pushed to the source, Spark will run the “incremental” query that combines the previous running counts with the new data to compute updated counts. The “Input Table” here is the lines DataFrame which acts as a […]

Read More

Calculating TF-IDF Using Apache Spark

Arseniy Tashoyan shows us how to calculate Term Frequency-Inverse Document Frequency using Apache Spark: TF-IDF is used in a large variety of applications. Typical use cases include: Document search. Document tagging. Text preprocessing and feature vector engineering for Machine Learning algorithms. There is a vast number of resources on the web explaining the concept itself […]

Read More

Categories

April 2017
MTWTFSS
« Mar May »
 12
3456789
10111213141516
17181920212223
24252627282930