With the continued growth of machine learning and artificial intelligence, there is a constant need for reports, dashboards, and other ways of visualizing data. Whether you are downloading CSV files or connecting to a complex…
In this post I will discuss some important points of TensorFlow; namely what is it, how it works, and how easily it can be used in production environments with Keras and eager processing. Enterprises that…
We’ve seen how what has traditionally been thought of as “big data” has evolved to encompass techniques and technologies that can benefit any company and prepare them for the future. What could an example project look like?
When you start down your cloud journey, all of the unanswered questions can be overwhelming. “How do I choose the right vendor? Do I just do a “lift and shift” on IaaS (Infrastructure as a Service), or is it better to implement a PaaS (Platform as a Service) option?
What exactly is a Modern Data Architecture, and, more importantly, do you need one? What makes a Modern Data Architecture different from existing reporting and analytic environments?
In this article we will discuss the importance of not filtering out data just because it doesn’t look like any other data we have, and how to quickly leverage multiple sources to derive business insights.
Eating the Elephant - Velocity So far in this series we have introduced the “3 V’s” of Big Data: Volume, Velocity, and Variety, focusing on Volume. Recall that in the introduction we encouraged you to…
Utilizing Apache tools Spark, Livy, and Zeppelin, together, can be very powerful in helping to tackle machine learning and data science.
In this article we will discuss one of the 3 V's of Big Data: Volume.
Jeremiah Evans reviews the 3 V's of Big Data and how you can tackle them one step at a time.