by clicking on the page. A slider will appear, allowing you to adjust your zoom level. Return to the original size by clicking on the page again.
the page around when zoomed in by dragging it.
the zoom using the slider on the top right.
by clicking on the zoomed-in page.
by entering text in the search field and click on "In This Issue" or "All Issues" to search the current issue or the archive of back issues respectively.
by clicking on thumbnails to select pages, and then press the print button.
this publication and page.
displays a table of sections with thumbnails and descriptions.
displays thumbnails of every page in the issue. Click on a page to jump.
allows you to browse through every available issue.
GCN : April 2014
Big data is big business for government. It's clear in the funding provided for big data-related projects, and it's clear in the response from agencies at all levels of government. In 2012, the Obama Administration announced the Big Data Research Development Initiative, providing more than $200 million to launch new big data-related projects across federal government. And state and local governments are just as keen on harnessing the power of big data to boost security, prevent fraud, enhance service delivery and improve emergency response. Agencies have been doing what they can to gain control of and manage the variety, volume and velocity of structured and unstructured data. That's increasingly important, as the pace of big data continues unabated. Deltek estimates that the volume of data stored by federal agencies alone will increase from 1.6 to 2.6 petabytes within two years. can help manage these growing stores of structured and unstructured data, including: Better analytics: This is an area of rapid and progressive growth. Advanced capabilities now allow for in-memory analysis, complex event processing and visualization along with real-time stream processing and stream analysis capabilities. Better search: Moving beyond simple word-based search, many big data tools are incorporating semantic search, which examines the intent of the question and presents answers that more closely matches what the searchers seek. Advancements in NoSQL: NoSQL is an important technology in making big data come to life. This horizontally scalable database technology is very vast variety of data sources to produce more value. Advanced NoSQL products now offer semantic search, support for a work directly with Hadoop. Like Hadoop, NoSQL is a distributed computing model that uses commodity hardware. Advancements in Hadoop: Hadoop has matured from a batch-processing model to a much more versatile tool appropriate for real-time big data applications. The most maturation has occurred in HDFS (Hadoop Distributed File System), which manages data storage across nodes, and MapReduce, a distributed processing paradigm. Bi D A ce M ke Di ere ce G me GAME CHANGING ECHNOLOG O MEE AGENC MI ION SPONSORED REPORT NoSQL SQL NoSQL Type of b e O e (SQL) M y, i clu i key lue ore , ocume b e , wi e colum ore r p b e S ro uppor for u ruc ure ocume No Ye E e of i e r i i form io from o er y em Di cul , ome ime expe i e Simple Sc l bili y Ver ic lly c l ble. Require i io l re ource o ruly c le Horizo lly c l ble. Au om ic lly pre o o mul iple er er ; er er c be e or remo e from e l yer wi ou pplic io ow ime Sc em Fixe ruc ure ype m ke i i cul o i form io bou ew i em D c be i er e i NoSQL b e wi ou fir efi i b e c em . T e form of e bei i er e c be c e y ime, wi ou pplic io i rup io . H oop i e r e No Ye SQL VS. NoSQL: YOU BE THE JUDGE For decades, relational, or QL-based databases, have been the database schema of choice to store and manage data. But in the era of big data, the traditional database model often can't keep up. It has di culty dealing with the multitude of unstructured data types, as well as the massive amounts of data that must be stored, managed and manipulated. o address these issues, many vendors have built next-generation databases with No QL ("Not only QL"), a database structure optimized for scalability, retrieval and large amounts of data that combines traditional QL with innovative ways of querying and access. With No QL, it's easier to manage projects with large, varied data stores, such as medical health records processing, geospatial mapping and fraud detection. Check out this chart for some di erences between the two.