Data Analytics


What is Big Data?

Big data is large data sets that are complicated to handle that ordinary software cannot deal with the complexity and the amount of data that are being exchanged at a constant rate.  In order to handle these data sets, there are software and relational databases that hold these massive amounts of data in servers.

The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.

There’s always challenges though, but that’s a whole other article by itself.

But how can you actually use this?

I know “big data” has been a hot topic these days, so the opportunities are endless when it comes to learning about the concepts of big data and data analytics.  You can use this to increase revenue growth, see what customers are buying, cut costs, and explore other products with the information you already have.

Statistical Data Usage from Technology

  • uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising.[83]
  • handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers. The core technology that keeps Amazon running is Linux-based and as of 2005 they had the world’s three largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB.[84]
  • Facebook handles 50 billion photos from its user base.[85]
  • Google was handling roughly 100 billion searches per month as of August 2012.[86]
  • Oracle NoSQL Database has been tested to past the 1M ops/sec mark with 8 shards and proceeded to hit 1.2M ops/sec with 10 shards.


The Best Part is…

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What I want to do?

Since my passion is to teach people the benefits of data analytics, I want to make people see that the sky is the limit when it comes to exploring data.  I like to think of myself as a reporting analyst that uses the advantages to organize your data, cleaning your data, utilizing your data and extracting the gems out of your data.  I say, “gems” because you never know what will be really useful to you until you find that sparkling diamond in the rough.

Final thoughts

Always keep learning and keep advancing.  Stay tuned for more awesome articles.  Thanks!