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
Building a data lake is one of the stepping stones towards data monetization use cases and many other advance revenue generating and competitive edge use cases. What are the building blocks of a “cognitive trusted data lake” enabled by machine learning and data science?
Quite often, we see that the need for data security and governance makes some organizations hesitant about migrating to the cloud. This is perfectly understandable given the types of data gathered and used by businesses today, the regulations they must adhere to on both a local and global level,
In many cases the data lake can be defined as a super set of repositories of data that includes the traditional data warehouse, complete with traditional relational technology. One significant example of the different components in this broader data lake, is in terms of different approaches to the
When the data lake is deployed as an infrastructure to be exploited by different users in various departments with their own needs, their own different requirements and often their own dialects in terms of a business language, then a universal translator can become very useful. Especially with the
There is so much talk about data as a new natural resource. The amount of data organizations and citizens across the globe produce, is authored in many systems and consumed by various organizations and users in different formats. This begs the following questions: Who owns this data? And why it is
Internet of Things data, devices and technologies are evolving into a core platform that is expected to impact business flexibility and more. Take a look at some key comprehensive best practices for Internet of Things–enabled application development that can put speed and agility into your business
Technology is great on its own, but unless it’s doing something for your business and creating real value for the organization, it’s not really serving a point. IBM’s Kevin McIntyre talks about how the DataFirst Method helps the data professional stay focused on data.