While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.
Whenever you ask a successful company why they wrote their own large distributed system, or put a lot of work into gluing multiple systems together, the reason almost always boils down to something like, “Well, XYZ did nearly everything we needed, except…”. There are a couple of stellar write-ups from
DataHero, one of the leading providers of self-service cloud BI, has expanded its integration with HubSpot, launching the first connector that enables sales and marketing professionals to easily generate data visualizations and analytics dashboards from HubSpot CRM. HubSpot CRM is a flexible, intuitive solution for managing prospects and sales pipeline.
The concept of data ingestion has existed for quite some time, but remains a challenge for many enterprises trying to get necessary data in and out of various dependent systems. The challenge becomes even more unique when looking to ingest data in and out of Hadoop. Hadoop ingestion requires processing
Mainframe users are getting a major upgrade thanks to an open source tool from Syncsort that links the IBM z Systems with big name big data processor, Apache Spark. In the days of cloud computing, few people even remember the mainframe. Likely, no one in your neighborhood knows what they are, and
Emerging technological breakthroughs enter our consciousness promising a bright future, but more often than not they follow Gartner’s well plotted hype cycle – from the peak of inflated expectation through the trough of disillusionment, before mainstream adoption materialises. Some technologies, however, take on a new meaning after a prolonged maturity
Want to find the next great data science guru? Look for flexibility. As demand for data scientists far outstrips the pool of qualified applicants, employers must look elsewhere – to individuals with no experience but high potential. The question: what defines “potential”? Now, an emerging body of work is helping
In 1913, the Ford Motor Company was at the forefront of car manufacture. Designing the reasonably-priced Model T to appeal to the masses and employing division of labour & moblised assembly lines in the factory made Ford the largest automobile factory in the world at that time. In 2007, the
Ferris is a full stack data scientist at LinkedIn who enjoys building products at the forefront of intelligent technology. He understands that the next generation won’t be concerned with how to use technology to do things, but will expect technology to do and adapt for them. As a data scientist,
Win a full license for Tableau software in their visualization challenge! Tableau did not come from modest beginnings. It was the result of three well-prepared Stanford men. It all began when founder Chris Stolte was working on his PhD. With his background in database programming, he saw there were major
With SQL now invading the NoSQL camp, (see here), how should an organization choose between a traditional SQL database, a NoSQL data store, or NewSQL database? 2015 Turing Award winner Mike Stonebraker said it best: “one size does not fit all”. The idea that a single database product can satisfy