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November 4, 2009

Simply About Regular Expressions for Data Integration

Filed under: Data Integration, Data Migration, ETL — Olga Belokurskaya @ 3:53 am

The use of regular expressions in data integration has become quite a common thing. There’s no surprise, for regular expressions are quite powerful tools that can help shorten and ease the process. Working with text strings, regular expressions help find and transform text, based on special rules or patterns.

Regular expressions comprise different constants and operators which define multiple strings and multiple operations over strings. How that is used in data integration? Different ways, but the principle is simple: RegEx (regular expressions in short) ease the work with data when out of big amount of you’ve got to find some values that fit some feature and do something with them (transform, migrate, change, whatever).

An example. Quite simple one. Actually, it may explain how RegEx are used practically. Imagine we’ve got to integrate data from one source to another. Let it be, for example, Salesforce integration with some accounting system, and let the data be some contact information. Imagine that, for example, in Salesforce first name and last name are entered in one field, while the accounting system has separate fields for them. Using regular expressions we may find and choose the first name in Salesforce “Name” field as something that stands before the blank space and integrate it to accounting system’s“first name” field. The same thing with the last name which may be defined as some text standing after the blank space in the “Name” field at Salesforce. Okay, it’s done. Everybody’s happy, no manual work, life’s great. Yes, the example is more than simple. And a lot more things may be done using RegEx to examine or parse some text or parts of texts that match specific rules.

There is, however a thought, that RegEx may work slow when it comes to dealing with large amounts of data. But still, they ease data integration a lot.

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