Alan Nugent

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.

When people talk of map and reduce in big data, they do so as operations within a functional programming model. Functional programming is one of the two ways that software developers create programs to address business problems. The other model is procedural programming. Take a quick look to understand the differences and to see when it's best to use one or the other model.

The fundamental structure for graph databases in big data is called “node-relationship.” This structure is most useful when you must deal with highly interconnected data. Nodes and relationships support properties, a key-value pair where the data is stored. These databases are navigated by following the relationships.

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?

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.

Clearly, the very nature of the cloud makes it an ideal computing environment for big data. So how might you use big data together with the cloud? Here are some examples: IaaS in a public cloud: In this scenario, you would be using a public cloud provider’s infrastructure for your big data services because you don’t want to use your own physical infrastructure.

Cloud computing is a method of providing a set of shared computing resources and is becoming increasingly important for your big data initiative. The cloud includes applications, computing, storage, networking, development, and deployment platforms, as well as business processes. Cloud computing turns traditional siloed computing assets into shared pools of resources based on an underlying Internet foundation.

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.

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.