OPEN SOURCE BIG DATA
Tat’s because, in adopting a big data sys- tem, you gain an ability to sift through large volumes of data from a variety of sources at a faster rate than traditional databases. Tis is done by breaking the data into smaller pieces and spreading the processing of that data across many machines in “parallel” and returning the response to a consolidation point. Tis is known as parallel computation, and it’s what is needed to tackle the data man- agement challenges faced by our cyber network defenders. Google is the most recognized pioneer in tackling the big data challenge of indexing and searching the unceasing volume, variety and veloc- ity—known as the 3Vs of big data—of structured and unstructured data.
BIG DATA TOOLS AND TECHNOLOGY: A PRIMER Hadoop is a free, Java-based program- ming framework that supports the processing of large data sets in a distrib- uted computing environment. It is also an important tool to consider when imple- menting a big data strategy. Hadoop is sponsored by the Apache Software Foundation, which is dedicated to sup- porting open-source software projects for the public good. At its simplest, Hadoop provides a parallel-processing computing framework for data storage and process- ing. Tis is important for enterprise-level analysis because of physical limitations on how quickly a single machine can pro- cess information.
For example, when deploying a basic Hadoop system you first build all index- ing strategies. Tese indexes are what allow you to organize data in a way that makes it quickly searchable, like a table of contents. For organizations looking to develop products to support big data, this first step has become a point of product differentiation, as performance is based on how well data is indexed. Product
106
A HANDFUL OF OPTIONS
A vendor- and product-neutral government off-the-shelf (GOTS) platform provides an environment for developing complex, cyber-hardened systems that lend themselves to frequent technology refreshes and rapid insertion of cutting-edge technology. (SOURCE: USAASC/Exdez/iStock)
differentiation is key for companies look- ing to distinguish their product or service in the marketplace. Other differences (or divergences) become more evident as applications are built on top of the data store. Differences in visualizations, data science libraries, cloud architecture and access management are a few examples. While many of the same open-source dis- tributions are used as a starting point, the end result is a product that is intended to work, on its own, from infrastructure to the user.
Te government is developing a strategy to enable communities with big data
needs to have access to this technology. Tere are special considerations that need to be taken into account to ensure that this is done in a sustainable manner. A strategy of an open government platform with vendor-provided applications and infrastructure is an approach derived, in part, from the National Institute of Stan- dards and Technology’s (NIST) cloud computing reference architecture. Big data systems leveraged for cyber analytics are typically built using cloud standards and technology. For the end user, this means access to all of the services through a modern Web browser. For engineers, it means building access through a modular
Army AL&T Magazine
July-September 2016
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156 |
Page 157 |
Page 158 |
Page 159 |
Page 160 |
Page 161 |
Page 162 |
Page 163 |
Page 164 |
Page 165 |
Page 166 |
Page 167 |
Page 168 |
Page 169 |
Page 170 |
Page 171 |
Page 172 |
Page 173 |
Page 174 |
Page 175 |
Page 176 |
Page 177 |
Page 178 |
Page 179 |
Page 180 |
Page 181 |
Page 182 |
Page 183 |
Page 184
