Big Data promises to deliver a better understanding of your campaigns, a more precise ability to target, and ultimately provide grander insights about your customers. But going after atomic-level data is daunting, so where do you start? Here are four steps to get you started to manipulate Big Data in a meaningful way
It’s just a meta-template on how to tackle an analysis. Then you’ll go through technicalities. Actually that will be the complex, but exciting part.
Original title and link: 4 Steps to Solve for Big Data Complex Analysis ( ©myNoSQL)
For those who read my blog and follow my research then you know I chose MongoDB as my backend database to store my PDFs […]
Why not standard SQL? Well, I wanted the data to be returned without having to parse a blob everytime (JSON/BSON), PDF files contain a lot of data that are often unique to themselves (document based storing) and Mongo also made it easy to handle dynamic content (no columns). […] I wanted to highlight an interesting way of collecting data and answering questions about my malware using Map/Reduce.
Initially I thought that using a seach engine would be a better approach. Then I realized that not everything can be expressed with a query. When complex filtering and grouping algorithms are needed, MapReduce is the solution.
Original title and link: Malware, MongoDB and Map/Reduce: A New Analyst Approach (NoSQL databases © myNoSQL)