White Paper: Big Data and the hype around IoT

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1 1 White Paper: Big Data and the hype around IoT Author: Alton Harewood 21 Aug 2014 (first published on LinkedIn) If I knew today what I will know tomorrow, how would my life change? For some time the idea of big data has been running high in the media so when Gartner recently published the 2014 Hype Cycle and indicated Big Data has moved rapidly over the peak of the Hype Cycle into the "trough of disillusionment", it raised the eyebrows of many observers. Has Gartner got it wrong? Is there still more to come? Gartner suggests that "while interest in big data remains undiminished, it has moved beyond the peak because the market has settled into a reasonable set of approaches, and the new technologies and practices are additive to existing solutions." [Source: Gartner's Hype Cycle Special Report for 2014] So, all the hype is real, big data and in particular how businesses will use that data is as important and exciting as it was last year it s just that in 2014, there is more hype about the Internet of Things #IoT illustrating just how quickly attention is moving in technology compared to just four years ago. So, in this article we will talk about the hype around the IoT and also about where Big Data is, what to expect as it 'settles down' as a technology and how its power is being harnessed through exciting new technologies. #IoT If you have not heard of the Internet of Things don't be too concerned, conceptually you may have heard of it but not realized it for what it was. To illustrate, consider your lowly (or wonderful) refrigerator. Imagine now if it could make your life easier by reading the bar code of everything placed in it. Through a user interface - voice or screen driven, it could tell you if something has passed its best before date. If its tied to big data through the internet, it might be able to suggest other brands of products and even the local store with the best price or coupons for the product this week! How about it sending you an SMS to pick up more milk? And so on. Imagine this now from the business perspective, the value of the data in the context of hundreds of consumers, considering how to get that data (with permission) and leverage it as part of a bigger data pool, this then is where big data remains front and center event though IoT is riding the crest. Through Hadoop and other frameworks the distributed refrigerator network could become an incredibly powerful data source, if Joe public allows it to share.

2 2 The humble refrigerator could be one of many parts of the IOT that businesses need to interact with. How will they do so without big data? From a business perspective, once you get beyond the consumer benefits, the IoT is only as valuable as the data it is collecting, storing and subsequently presenting for analysis to the businesses providing the new capabilities be they smart fridges, personal monitoring devices tied to insurance, security systems and a plethora of other 'things' that could be hooked into the internet! Big Data So, Big Data is not off the table, it s just "settled down". How so? Well, in 2011 there was a great infographic [ ] that demonstrated the concepts of the big data challenge that showed that in that year alone an estimated 1.8 zettabytes of data would be collected. Are you trying to size that up? Think 57.5 BILLION 32GB ipads of data! This number is estimated to double annually. Size of the data problem is in itself a big problem. So too is understanding data relationships, structure of the data and understanding the content from different perspectives. A great example of this is a Tweet about a cat from one person may hashtag #cat or #Meow or #KittyKat etc., creating multiple elements for the same entity. Finally, architecture plays into the problem, should a database be duplicated, or the application, or both? Alongside this proliferation of data gathering the world has progressed from flat spreadsheet databases to the enormous data storage and growth of frameworks to manage these. These range from intuitive, macro enabling distributed Hadoop environments through to the dynamically and intelligently harnessed data interrogated by powerful AI engines like IBM s Watson. Data management and formats passed over time from flat text documents of the 90s and early 2000s, through iterations of similarly flat but more powerful hypertext, wikis and blogs, through the path of tagging-based social media that includes personalization of data (folksonomy), metadata (RDF) to the subsequent arising conceptual relationship knowledge management (ontologies classes & relationships) These latter changes though have happened rapidly from a technology advancement perspective so that in the space of perhaps 10 years huge steps forward have been achieved to build out ways to better manage data of enormous sizes. Twitter is an excellent example of the challenge of "folksonomy" in the handling of big data. The number of tools emerging to better manage Twitter feeds and isolate keywords or HASHTAGS, shows that while the tool itself provides a wonderful social interaction platform, much of its big data power requires significant effort to harness. This in turn suggests that there are still many iterations of social media advancement to come with Web 3.0.

3 3 The facts are that what was considered a panacea in the early 2000s, the relational database management system, RDBMS, which has at its heart SQL, has significant performance problems as the data complexity, and its use, expands. So, how is the power of big data being harnessed? Concepts such as NoSQL arose largely due to the problems inherent in managing big data. NoSQL is not suggesting that SQL is bad. NoSQL postulates that there are alternative and better ways to store data. Consider a typical SQL query on a large data set and you immediately start to get the idea of where this is coming from and where the concept would be going to. There has to be a way to interrogate larger sets of data, understand their relationships and repeat the task reliably and better than using SQL. The impact of Graph Databases You may have heard of key values, graph databases, big tables and document categories, or maybe just one of these. Each of these are emerging NoSQL categories. Key-Value stores focus on the position of an object defined by its key. Big Tables extend the idea of columns to column families and add rights & permissions (ACL) Document databases (categories) are similar to key-values but the database knows what the value is (classic example MongoDB) Graph Databases focus modeling the structure of the data. This is the most exciting part, the relationship, interconnectivity of the data. Inspired by Graph Theory the data model is a Property Graph that has Nodes, Relationships (edges) between Nodes, Key-Value pairs on both nodes and edges and sometimes edge labels or node/edge types. Neo4J is the classic example of graph database frameworks. We ll dwell on graph databases a little more as the roots help us understand how the concept can help practically. Graph database is based on graph theory which began in the18 th century with studies by Euler; this became the origin of topology and in fact the concept of network relationships. At its essence, graph theory deals with the relationships. Euler wanted to know if he could walk over each of seven bridges of Königsberg (then Prussia) once without crossing one of them a second time. He could prove this in practice as due to the odd number of bridge endpoints in each section of the town, it simply was not possible to cross every bridge once, the edges and points and their locations influenced the outcome. Euler went on to prove this mathematically. This concept is the basis of understanding relationships in complex patterns, such an underground map routes or street maps and in our case often distributed big data.

4 4 Neo4j is an example of a graph database where data is stored in nodes that are connected by relationships with properties. This structure is used to describe simple or complex relationships between data. Why Graphs? Well, if you draw a small relationship diagram on a whiteboard, in order to represent it in an RDBMS you would need to translate it to an entity-relationship (ER) diagram that extrapolates all possible relationships and then massage it (normalize) to fit the relational database. With a graph database the model on the whiteboard is implemented directly, this speed to deploy is a huge advantage when dealing with complex data. Each of the NoSQL concepts fits into the scaling problem as follows: Key-Value stores best for large data, low complexity Big tables slightly less data and slightly increased complexity Document databases usually less data but increased complexity Graph Databases less data but great complexity Examples of NoSQL? These include Facebook, Twitter, LinkedIn, Google, Amazon, Hadoop, MongoDB and more. Examples of enterprise big data management solutions Examples of taking the opportunity and driving big data storage further include Cloudera, Hortonworks and MapR Technologies. The nature of Hadoop though means that a lower number of companies are paying for the privilege against say existing users of Oracle. This is largely because the suite of applications riding the platform are not solving the big data analytics need that is poised to be the largest change in our current technology generation. Companies like Pivotal, for example, are trying to close that gap presenting with the Hadoop platform the ability to batch store and process data then leverage both a standalone or HDFS integrated SQL DB or In-memory standalone or HDFS integrated SQL DB to complete analysis on 100% of data (versus a typical EDW which in many cases is a fraction of the enterprise data often fractured around the company). There are of course many companies building a portfolio in this arena including the likes of IBM, Microsoft, Google and many merely trying to manage the cloud storage problem. Aligned to some of the suites solutions are tools that include the key growth opportunity of predictive analytics for big data.

5 5 This article kind of grew as I wrote but I think I hit a natural end point here. While you see IoT riding a crest of media attention, consider that now is indeed time to get to grips with your big data, to examine your options, many of them in the cloud and to ensure that you are fully prepared as it moves rapidly through the slope of enlightenment to practical and almost limitless usefulness!

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