Product Information

Data is created constantly, and at an ever-increasing rate. Mobile phones, social media, imaging technologies to determine a medical diagnosis-all these and more create new data, and that must be stored somewhere for some purpose. Devices and sensors automatically generate diagnostic information that needs to be stored and processed in real time. Merely keeping up with this huge influx of data is difficult, but substantially more challenging is analyzing vast amounts of it, especially when it does not conform to traditional notions of data structure, to identify meaningful patterns and extract useful information. These challenges of the data deluge present the opportunity to transform business, government, science, and everyday life.
“Big Data” is data whose scale, diversity and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from.

Although the volume of Big Data tends to attract the most attention, generally the variety and velocity of the data provide a more apt definition of Big Data. (Big Data is sometimes described as having 4 Vs: volume, variety, veracity and velocity.)

Due to its size or structure, Big Data cannot be efficiently analyzed using only traditional databases or methods. Big Data problems require new tools and technologies to store, manage, and realize business benefits.

The players in this field are data analysts, data engineers and data scientists :

An effective data analyst will take the guesswork out of business decisions and help the entire organization thrive. The data analyst must be an effective bridge between different teams by analyzing new data, combining different reports, and translating the outcomes. In turn, this is what allows the organization to maintain an accurate pulse check on its growth.
The data scientist will uncover hidden insights by leveraging both supervised (e.g. classification, regression) and unsupervised learning (e.g. clustering, neural networks, anomaly detection) methods toward their machine learning models. They are essentially training mathematical models that will allow them to better identify patterns and derive accurate predictions.

Data engineers establish the foundation that the data analysts and scientists build upon. Data engineers are responsible for constructing data pipelines and often have to use complex tools and techniques to handle data at scale. Unlike the previous two career paths, data engineering leans a lot more toward a software development skill set. At larger organizations, data engineers can have different focuses such as leveraging data tools, maintaining databases, and creating and managing data pipelines. Whatever the focus may be, a good data engineer allows a data scientist or analyst to focus on solving analytical problems, rather than having to move data from source to source. The data engineer’s mindset is often more focused on building and optimization