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.

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.

Big data is all about high velocity, large volumes, and wide data variety, so the physical infrastructure will literally "make or break" the implementation. Most big data implementations need to be highly available, so the networks, servers, and physical storage must be resilient and redundant. Resiliency and redundancy are interrelated.

A number of cloud delivery models exist for big data. Try talking to those with experience to figure out which type of delivery model is best for your big data initiative. Infrastructure as a Service Infrastructure as a Service (IaaS) is one of the most straightforward of the cloud computing services. IaaS is the delivery of computing services including hardware, networking, storage, and data center space based on a rental 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.

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.

Big data is not a single technology but a combination of old and new technologies that helps companies gain actionable insight. Therefore, big data is the capability to manage a huge volume of disparate data, at the right speed, and within the right time frame to allow real-time analysis and reaction. Big data is typically broken down by three characteristics: Volume: How much data Velocity: How fast that data is processed Variety: The various types of data Although it’s convenient to simplify big data into the three Vs, it can be misleading and overly simplistic.

ETL tools combine three important functions (extract, transform, load) required to get data from one big data environment and put it into another data environment. Traditionally, ETL has been used with batch processing in data warehouse environments. Data warehouses provide business users with a way to consolidate information to analyze and report on data relevant to their business focus.

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.