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Marcia Kaufman
Marcia Kaufman specializes in cloud infrastructure, information management, and analytics.
Articles & Books From Marcia Kaufman
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
Existing analytics tools and techniques will be very helpful in making sense of big data. The algorithms that are part of these tools, however, must be able to work with large amounts of potentially real-time and disparate data. A competent infrastructure must be in place to support this. And, vendors providing analytics tools will also need to ensure that their algorithms work across distributed implementations.
Article / Updated 03-26-2016
Cloud computing has evolved in recent years. The new world of the hybrid cloud is an environment that employs both private and public cloud services. Companies are realizing that they need many different types of cloud services in order to meet a variety of customer needs.
The growing importance of hybrid cloud environments is transforming the entire computing industry as well as the way businesses are able to leverage technology to innovate.
Article / Updated 03-26-2016
Business Process as a Service (BPaaS) is any type of horizontal or vertical business process that’s delivered based on the cloud services model. These cloud services — which include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) — are therefore dependent on related services.
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.