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Alan Nugent
Alan Nugent has extensive experience in cloud-based big data solutions.
Articles & Books From Alan Nugent
Cheat Sheet / Updated 02-09-2022
To stay competitive today, companies must find practical ways to deal with big data — that is, to learn new ways to capture and analyze growing amounts of information about customers, products, and services.Data is becoming increasingly complex in structured and unstructured ways. New sources of data come from machines, such as sensors; social business sites; and website interaction, such as click-stream data.
Article / Updated 03-26-2016
The best way to understand the economics of big data is to look at the various methods for putting big data to work for your organization. While specific costs may vary due to the size of your organization, its purchasing power, vendor relationships, and so on, the classes of expense are fairly consistent.
Big data types and sources
The most important decisions you need to make with respect to types and sources are
What data will be necessary to address your business problem?
Article / Updated 03-26-2016
To fully understand the capabilities of Hadoop MapReduce, it’s important to differentiate between MapReduce (the algorithm) and an implementation of MapReduce. Hadoop MapReduce is an implementation of the algorithm developed and maintained by the Apache Hadoop project.
It is helpful to think about this implementation as a MapReduce engine, because that is exactly how it works.
Article / Updated 03-26-2016
Behind all the important trends over the past decade, including service orientation, cloud computing, virtualization, and big data, is a foundational technology called distributed computing. Simply put, without distributing computing, none of these advancements would be possible.
Distributed computing is a technique that allows individual computers to be networked together across geographical areas as though they were a single environment.
Article / Updated 03-26-2016
While the worlds of big data and the traditional data warehouse will intersect, they are unlikely to merge anytime soon. Think of a data warehouse as a system of record for business intelligence, much like a customer relationship management (CRM) or accounting system. These systems are highly structured and optimized for specific purposes.
Article / Updated 03-26-2016
Many companies are exploring big data problems and coming up with some innovative solutions. Now is the time to pay attention to some best practices, or basic principles, that will serve you well as you begin your big data journey.
In reality, big data integration fits into the overall process of integration of data across your company.
Article / Updated 03-26-2016
Big data is most useful if you can do something with it, but how do you analyze it? Companies like Amazon and Google are masters at analyzing big data. And they use the resulting knowledge to gain a competitive advantage.
Just think about Amazon's recommendation engine. The company takes all your buying history together with what it knows about you, your buying patterns, and the buying patterns of people like you to come up with some pretty good suggestions.
Article / Updated 03-26-2016
Four stages are part of the planning process that applies to big data. As more businesses begin to use the cloud as a way to deploy new and innovative services to customers, the role of data analysis will explode. Therefore, consider another part of your planning process and add three more stages to your data cycle.
Article / Updated 03-26-2016
As you enter the world of big data, you'll need to absorb many new types of database and data-management technologies. Here are the top-ten big data trends:
Hadoop is becoming the underpinning for distributed big data management. Hadoop is a distributed file system that can be used in conjunction with MapReduce to process and analyze massive amounts of data, enabling the big data trend.
Article / Updated 03-26-2016
To understand big data, it helps to see how it stacks up — that is, to lay out the components of the architecture. A big data management architecture must include a variety of services that enable companies to make use of myriad data sources in a fast and effective manner.
Here's a closer look at what's in the image and the relationship between the components:
Interfaces and feeds: On either side of the diagram are indications of interfaces and feeds into and out of both internally managed data and data feeds from external sources.
