Big Data

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Ten Hot Big Data Trends

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: [more…]

How to Analyze Big Data to Get Results

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 [more…]

Best Practices for Big Data Integration

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 [more…]

Big Data Planning Stages

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 [more…]

Integrate Big Data with the Traditional Data Warehouse

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 [more…]

Explore the Big Data Stack

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 [more…]

Laying the Groundwork for Your Big Data Strategy

Companies are swimming in big data. The problem is that they often don't know how to pragmatically use that data to be able to predict the future, execute important business processes, or simply gain new [more…]

Defining Big Data: Volume, Velocity, and Variety

Big data enables organizations to store, manage, and manipulate vast amounts of disparate data at the right speed and at the right time. To gain the right insights, big data is typically broken down by [more…]

Understanding Unstructured Data

Unstructured data is different than structured data in that its structure is unpredictable. Examples of unstructured data include documents, e-mails, blogs, digital images, videos, and satellite imagery [more…]

The Role of Traditional Operational Data in the Big Data Environment

Knowing what data is stored and where it is stored are critical building blocks in your big data implementation. It's unlikely that you'll use RDBMSs for the core of the implementation, but it's very likely [more…]

Basics of Big Data Infrastructure

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 [more…]

Managing Big Data with Hadoop: HDFS and MapReduce

Hadoop, an open-source software framework, uses HDFS (the Hadoop Distributed File System) and MapReduce to analyze big data on clusters of commodity hardware—that is, in a distributed computing environment [more…]

Big Data For Dummies Cheat Sheet

Companies must find a practical way to deal with big data to stay competitive — to learn new ways to capture and analyze growing amounts of information about customers, products, and services. Data is [more…]

How Big Data Analytics Can Prevent Fraud

One benefit of your big data analytics can be fraud prevention. By many estimates, at least 10 percent of insurance company payments are for fraudulent claims, and the global sum of these fraudulent payments [more…]

Five Plans for Big Data Success

While big data is only in the first stages, you want to plan for success. It is never too early to get started with planning and good practices so that you can leverage what you are learning and the experience [more…]

Five Big Data Best Practices

Big data is only in the first stages, but it is never too early to get started with best practices. As with every important upcoming technology, it is important to have a strategy in place and know where [more…]

Ten Big Data Do’s and Don’ts

Many companies that are beginning their exploration of big data are in the early stages of execution. Consider these do’s and don’ts as part of your strategy. Most companies are experimenting with pilots [more…]

Structured Data in a Big Data Environment

The term structured data generally refers to data that has a defined length and format for big data. Examples of structured data include numbers, dates, and groups of words and numbers called [more…]

Unstructured Data in a Big Data Environment

Unstructured data is data that does not follow a specified format for big data. If 20 percent of the data available to enterprises is structured data, the other 80 percent is unstructured. Unstructured [more…]

Layer 0 of the Big Data Stack: Redundant Physical Infrastructure

At the lowest level of the big data stack is the physical infrastructure. Your company might already have a data center or made investments in physical infrastructures, so you’re going to want to find [more…]

Layer 1 of the Big Data Stack: Security Infrastructure

Security and privacy requirements, layer 1 of the big data stack, are similar to the requirements for conventional data environments. The security requirements have to be closely aligned to specific business [more…]

Layer 2 of the Big Data Stack: Operational Databases

At the core of any big data environment, and layer 2 of the big data stack, are the database engines containing the collections of data elements relevant to your business. These engines need to be fast [more…]

Layer 3 of the Big Data Stack: Organizing Data Services and Tools

Organizing data services and tools, layer 3 of the big data stack, capture, validate, and assemble various big data elements into contextually relevant collections. Because big data is massive, techniques [more…]

Layer 4 of the Big Data Stack: Analytical Data Warehouses

The data warehouse, layer 4 of the big data stack, and its companion the data mart, have long been the primary techniques that organizations use to optimize data to help decision makers. Typically, data [more…]

The Importance of Virtualization to Big Data

Solving big data challenges requires the management of large volumes of highly distributed data stores along with the use of compute- and data-intensive applications. Virtualization provides the added [more…]

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