There are two main characteristics that data needs to fullfill: there needs to be transformable data and filterable data. In this tutorial, I will describe both.
If data is transformed, it can be changed to a different format or layout. This could as well mean the format change from binary to e.g. Json or XML as well as a totally new representation. If someone wants to look at a specific dataset (which, for instance, could be filtered) not all data might be interesting.
Let’s assume that a manager wants to filter for all Customers younger than 18 in a specific district. The manager is probably not interested in the names of the customer but rather in the sum of customers. Instead returning a huge list of Names with addresses and alike, a number is returned.
Or the online marketing department wants to target all customers with specific criteria such as age, the address might not be relevant, but Names and E-Mail are. Transformability is also a necessary characteristic if data has to be exported to another database, e.g. for analytics.
This is a key characteristic to Datasets. Analytics software use Filtering frequently and it is absolutely necessary since most analytics simply don’t run on all data but rather on selected Data. Filtered Data is often represented with the “Select … Where”-Clauses in Databases.
Most of what filtering of data is good for was already discussed with “Transformability”, however we would still go into detail with that. If we analyze data, it is often necessary to work on specific datasets.
Imagine a Google Search Query, where you search for “Big Data”. All Data within Google’s index gets filtered for exactly these Words and a consolidated List is returned. If the online marketing department mentioned in “Transformability” wants a list of customers in a specific area, this List is also filtered based on the Zip Code or other geographical data. Hence it is an important characteristic for Data to support Filtering.
I hope you enjoyed the first part of this tutorial about big data technology. This tutorial is part of the Big Data Tutorial. Make sure to read the entire tutorials.