Data Science 101
In part one of our History of BI series, we looked at the origins of BI, the way it developed over the 1960’s and 70’s, and the key technologies that emerged within these era’s. The article began by mapping the way data storage changed from hierarchical database management systems (DBMS), like
‘SQL is outdated’. ‘RDBMS can no longer meet businesses’ data management needs’. ‘New database technologies like NoSQL are the solution for today’s enterprises’. We hear statements like these alot, both inside and outside the database technologies industry. But are they accurate? Is SQL a thing of the past, and are
Business intelligence, or BI as it is commonly referred to, has gained considerable popularity over the past decade. Although the term was first introduced in 1865, and picked up momentum in the mid-late 1980’s, it was only up until the early 1990’s that the phrase entered public discourse. Now it
This article was first posted by Carla Gentry here. Carla is currently owner and data scientist at Analytical Solution.She has worked in the field of data science for over 15 years, and has worked for Fortune 100 and 500 companies such as Johnson & Johnson, Hershey, Kraft, Kellogg’s and Firestone.
Coined only 6 years ago by D.J. Patil and Jeff Hammerbacher, the title “Data Scientist” has grown to prominence in recent times. Indeed, data scientists are some of most sought after people on the planet. Companies like Google, Facebook, LinkedIn and Netflix have relied heavily on the expertise of data
SQL vs NoSQL Since both languages have equal number of proponents when deciding which language to use, Network World invited two influencers to share their views on SQL vs NoSQL. Ryan Betts, CTO of VoltDB, is a stark proponent of SQL and will therefore take the structured side. Bob Wiederhold,