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Big data architecture and patterns, Part 4: Understanding atomic and composite patterns for big data solutions The patterns covered in this article help define the parameters of a big data solution. The article addresses the most common and recurring big data problems and solutions. The atomic patterns describe the typical approaches for consuming, processing, accessing, and storing big data. Composite patterns, which are comprised of atomic patterns, are classified according to the scope of the big data solution. Because each composite pattern has several dimensions, there are many variations of each pattern. The patterns enable business and technical users to apply a structured approach to establishing the scope and defining the high level solution for a big data problem. Also available in: Chinese Russian Portuguese	Articles	26 Nov 2013
Big data architecture and patterns, Part 3: Understanding the architectural layers of a big data solution The logical layers of a big data solution help define and categorize the various components required for a big data solution that must address the functional and non-functional requirements for a given business case. This set of logical layers outlines the critical components of a big data solution from the point where data is acquired from various data sources to the analysis required to derive business insight to the processes, devices, and humans who need the insight. Also available in: Chinese Russian Portuguese	Articles	15 Oct 2013
Big data architecture and patterns, Part 1: Introduction to big data classification and architecture Big data problems are often complex to analyze and solve. The sheer volume, velocity, and variety of the data make it difficult to extract information and business insight. A good first step is to classify the big data problem according to the format of the data that must be processed, the type of analysis to be applied, the processing techniques at work, and the data sources for the data that the target system is required to acquire, load, process, analyze and store. Also available in: Chinese Russian Portuguese Spanish	Articles	17 Sep 2013
Big data architecture and patterns, Part 2: How to know if a big data solution is right for your organization This article describes a dimensions-based approach for assessing the viability of a big data solution. By answering questions that explore each dimension, apply what you know about your own environment to determine whether a big data solution is appropriate. A careful look at each dimension yields clues about whether it's time for your big data services to evolve. Also available in: Chinese Russian Portuguese Spanish	Articles	08 Oct 2013
Big data architecture and patterns, Part 5: Apply a solution pattern to your big data problem and choose the products to implement it Using a scenario-based approach, this article outlines solution patterns that can help define your big data solution. Each scenario starts with a business problem and describes why a big data solution is required. A specific solution pattern (made up of atomic and composite patterns) is applied to the business scenario. This step-by-step approach helps identify the components required for the solution. At the end of the article, some typical products and tools are suggested. Also available in: Chinese Russian	Articles	17 Dec 2013