Key-Object – A New Paradigm in Search?

@machinelearnbot

Summary: The premise of this new Key Object architecture is that search is broken, at least as it applies to complex merchandise like computers, printers, and cameras. An innovative and workable solution is described. The question remains, is the pain sufficient to justify a switch? As we are all fond of saying, innovation follows pain points. Are we missing something in our uber-critical search capabilities that needs to be resolved?


Key-Object – A New Paradigm in Search?

@machinelearnbot

As we are all fond of saying, innovation follows pain points. Are we missing something in our uber-critical search capabilities that needs to be resolved? A colleague recently pointed me to a slim volume "Structured Search for Big Data" by Mikhail Gilula (published by Elsevier and available on Amazon) that argues that not only are our search tools deficient but that a complete revamp of the underlying key-word NoSQL DB structure is what's required. Use Google, Amazon, or any of the other life-critical search tools we've become so reliant upon and you are using key-word search on NoSQL. The pain that Gilula identifies is the length of time it takes the consumer to research and select complex merchandise for best deals resulting from the imprecision of the search results.


On RDBMS, NoSQL and NewSQL databases. Interview with John Ryan

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"The single most important lesson I've learned is to keep it simple. I find designers sometimes deliver over-complex, generic solutions that could (in theory) do anything, but in reality are remarkably difficult to operate, and often misunderstood."–John I have interviewed John Ryan, Data Warehouse Solution Architect (Director) at UBS. You are an experienced Data Warehouse architect, designer and developer. What are the main lessons you have learned in your career?


Debunking the 68 Most Common Myths About Big Data – Part 2

@machinelearnbot

If you caught Part 1 of this article you know that we set out to catalogue all the common misconceptions and myths about Big Data. In all we identified 68! Eliminating some overlap but trying to retain the nuances of different ways to explain the same myth we were able to group these into 14 major categories (myths or misconceptions). The Most Important Thing About Big Data is Its Size. The More Data the Better – Data is Inherently Valuable. Big Data is for Big Companies Only – It Doesn't Apply to Me.


To SQL or not To SQL: that is the question!

@machinelearnbot

This article is based upon our upcoming book Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data, www.pdbmbook.com See also our corresponding YouTube channel with free video lectures. Relational database systems (RDBMS) pay a lot of attention to data consistency and compliance with a formal database schema. New data or modifications to existing data are not accepted unless they satisfy constraints represented in this schema in terms of data types, referential integrity etc. The way in which RDBMS coordinate their transactions guarantees that the entire database is consistent at all times, the well-known ACID properties: atomicity, consistency, isolation and durability.