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In Defense of Relational Databases

As you may know, many big data technologies are defined as schema on read. What this means is that you can throw whatever you want on the disk and then, when you need the data, you tell the data store what that data means (e.g....

The Shifting Role of IT in Analytics

In the distant past (circa 2015), IT was responsible for providing data consumers with data, tools, and development expertise. Today, the landscape is shifting, especially in larger organizations. Nowadays, IT provides some of the data - the certified, clean data. Consuming departments fill the rest...

Predictive Analytics explained in two sentences

Predictive analytics is this super-complex field that only statisticians and data scientists can understand, right? Well, perhaps it takes some training to do it well but it only takes two sentences to understand what it's all about: In predictive analytics we determine the likelihood of something...

Dataspace to sponsor Southeast SAS Users Group 2017

  We are proud to announce that Dataspace is a corporate sponsor of the 25th Annual Southeast SAS Users Group (SESUG 2017) conference in Cary, NC from November 5th to 7th, 2017!   Kiran Venna, our featured conference speaker, will be presenting on the following topics during the academic...

kafka

Kafka: What Is it?

Number One in Dataspace’s Data Science Series: What does it mean?   If nothing else, the data science industry is good at coming up with new, unique, confusing names and terms.  ZooKeeper, MapReduce, Hadoop, Pig, Storm, Mahout MongoDB…the list keeps growing and it’s totally understandable if you...

Pitching data science budget

Pitching Your 2018 Data Science Budget

Last week we offered some suggestions on how to attack your data management initiatives in 2018 according to your organization’s level of data expertise.  This week we follow up with some tips on how to pitch those new data science technologies and initiatives to increase...