What is a Graph Database?

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What is an Online Analysis Processing System?

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What is an OLAP CUBE?

OLAP simply translated into Online Analysis Processing , it is the process of viewing data in a multi-dimensional format  i.e.  slicing and dicing of data from various  multiple perspectives.

olap

It involves the deployment of both fact and dimensional tables. Many enterprise business use OLAP to view their performance against diverse variables such as sales by locations by a given quarter.

I  have personal experiences with the application of OLAP systems in various data warehousing projects in which i have undertaken in the past. It is feels pretty good to harness the rich capabilities that an OLAP system brings into the business . Some of the features of an OLAP includes hierarchies look ups,drill through of data, slicing and dicing of  data  etc.

Why you need an Online Analysis Processing  System

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Many contemporary dashboards  reports are built on  existing OLAP systems, Your ROI upon the adoption of an OLAP systems for your business  would be very huge (i.e if you don’t have one already)

Some OLAP vendors

OLAP VENDORS

 

Graph Theory For Information Management

Graph theory is a mathematical concept on the basis of pair wise relationship between nodes or vertices.okay put simply it basically two entities connected by a relationship,
graph theory has been around for a very long time, however graph theory for information management has gained a lot traction due to recent advances made in technology.

Graph database modeling
Graph database modeling showing the relationship between nodes

I personal stumbled upon graph database last year  after taking keen interest in NoSQL database platform. A quote by Emil Emfrem “graphs are eating the world”
Today graph database platforms are in demand due to it improved performance especially when used for building modern day  applications (onsite and cloud-based solutions).

  Top ranked Graph database platforms ,seen below

1.Neo4j

2.OrientDB

3.Titan

4. ArangoDB

 

5.Virtuoso

I have personally used graph database platform for some of my contemporary jobs and  i can conclude without an iota of doubt that it is indeed ready to give other  database platforms a run for it money.

 

Some Graph Databases Applications

  1. Master Data Management
  2. Fraud Detection Analysis
  3. Project Management
  4. Social Network Analysis
  5. Real Time Recommendations and many more.

 

5 Reasons Why You Need To Archive Your Data

1. Derivable Insights
Many big companies are deriving lots of valuable insights from archived datasets this  greatly allows them to execute and review their business strategy.
Today archived datasets equals valuable insights when processed with the right technology.

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2. Democratization of Software Technologies
Today a huge number  database management  software vendors exist in the market place, this wasn’t the  case some 30 years ago ,from Oracle, Microsoft Sql Server,MySql,Microsoft Acess,PostgresSql ,Teradata etc.
The list is nearly endless, presently opportunities towards archiving datasets for both small and enterprise business is very encouraging.

3. Your Rival Maybe Doing It (Archiving Too)
Yes certainly, it most likely your competition has in place a good information management strategy giving them a very crucial edge over you.

4. Improved Hardware Technologies
Decades ago we had constraints related to limited capacity of our storage devices. From cassettes to floppy disk drives, thumb drives, hard drive. Today improved technologies has made it possible for us to store gigabytes, terabytes, exabyte and petabyte of datasets (On site and off site provisions)

5. You could make a lot of Money from Archiving Data
Google is one of the most valuable companies today, yes we all know but do we know why that is??? , answer it due to the very fact that google archives lots of search queries by its users. Data is very a crucial  ingredient credited for many success stories. Numerous cases of big enterprise legally making acquisition of data warehousing companies others pay a huge amount of  to data warehousing companies for access to its datasets (troves)