Context-aware stream computing helps you become more responsive to emerging opportunities. By using innovative technologies to understand the context of data and analyze data in real time, you can put data to work.
Melissa Hilbert, a Director at Gartner Research, will be speaking in the SPM track at IBM Vision 2017 about some of the key questions and challenges lying ahead for business leaders regarding sales performance management and incentive compensation. ibm.com/vision
In the past, the relationship between the different models that might be used in defining a data warehouse was a very linear one. There may have been different model artifacts used as the team responsible for developing the data warehouse progressed through the usually waterfall-type set of activities, starting with high level business definitions, progressing to use of logical models and eventually ending up with a set of physically deployed schemas that are intended to address the initial business needs. The arrival of the data lake has significantly changed the approaches required for the creation of models.
For today’s data scientists and data engineers, the data lake is a concept that is both intriguing and often misunderstood. While there are many good resources about data lakes on ibm.com and other websites, there is also a lot of hype and spin. As a result, it can be difficult to get a clear understanding of the challenges, opportunities and methods that can help companies build data lakes that deliver real business advantage.
Although NoSQL database technology has been around for a long time (before SQL actually), not until the advent of Web 2.0, when companies such as Google and Amazon began using the technology, did NoSQL’s popularity really take off. Market Research Media forecasts NoSQL Market to be $3.4 Billion by 2020. This is being driven by a compound annual growth rate (CAGR) of 21% in the period 2015-2020.
Dwaine Snow is a Global Big Data and Data Science Technical Sales Manager at IBM. He has worked for IBM for more than 20 years, focusing on relational databases, data warehousing, and the new world of big data analytics. He has written eight books and numerous articles on database management, and has presented at conferences around the world.