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5 Reasons Apache Spark is the Swiss Army Knife of Big Data Analytics

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 Analytics: The Collision of Classic & Big Data Analytics

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 Persistence with Joblib

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 Handling Big Data

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 Cities

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 Data

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 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 Today

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 […]

Coping with the Data Chaos Post Data Landscape Disruption

Coping with the Data...

Abstract: Unstructured Data and the BigData platforms forces a disruptive change that left most enterprises in a major Data Chaos. The combination of economic drivers in enterprise computing, the need to leverage semi-structured and unstructured Data, and the emergence of the Internet of Things (IOT) is driving a dramatic shift in the Enterprise Data landscape. […]