“Printing Money”...
Organizations have made large investments in big data platforms, but many are struggling to realize business value. While most have anecdotal stories of insights that drive value, most still rely only upon storage cost savings when assessing platform benefits. At the same time, most organizations have treated machine learning and other cognitive technologies as “science […]
5 Reasons Apache Spa...
We are living in exponential times. Especially if we are talking about data. The world is moving fast and more data is generated every day. With all that data coming your way, you need the right tools to deal with the growing amounts of data. If you want to get any insights from all that […]
Next Generation Anal...
The Classic analytics traditionally supported by a data warehouse yields focus and insight by understanding organization’s past actions. One among many examples would include measuring the supply chain of materials into a product or service that’s brought to market, which is typical across all industries from telecom and financial services to pharmaceuticals and retail. Others […]
Better Compressed Pe...
Problem setting: persistence for big data Joblib is a powerful Python package for management of computation: parallel computing, caching, and primitives for out-of-core computing. It is handy when working on so called big data, that can consume more than the available RAM (several GB nowadays). In such situations, objects in the working space must be […]
Practical Tips on Ha...
Big Data is often shrouded in mystery and jargon. Brian will attempt to demystify the topic through a series of short vignettes on how to pragmatically deal with Big Data. Presentation Info: Dr. Brian will show us how to avoid Big Data problems in the first place, scaling code, divide-and-conquer methods, and near-term future trends […]
Big Data from Big Ci...
Naturally, a paper titled Big Data and Big Cities: The Promise and Limitations of Improved Measures of Urban Life piqued my interest. From the discussion, of “big data” four V’s, Glaeser et al. focused on large volumes of data. The paper discussed two types of big data: while researchers typically used aggregated data, such as […]
More Data ≠ Rich D...
In the past 10 years, the focus of data has been on amassing and storing: the more data collected, the better. But while we all became expert data gatherers, what we actually ended up with was a glut of data, a shred of the insights we expected to get, and a very expensive problem. Data […]
Big Data? Try Mixed ...
Big Data is here to stay and it is having a profound effect on businesses and societies. That having said; there are still so many organizations that have no clue about what Big Data is. Big Data means different things for different people, organizations and industries. While it is true that Big Data has different […]
Big Data: The Future...
Abstract: The availability of new data, be it from businesses, from scientific experiments, from social networks, or from the Internet of Things (IoT) are ushering in a new era of scientific enlightenment, social revolution and business Darwinism. These new eras will fundamentally reshape the way we work, live, and think, and will retrain us to […]
