{"id":3186,"date":"2014-06-05T17:04:40","date_gmt":"2014-06-05T17:04:40","guid":{"rendered":"http:\/\/www.odbms.org\/blog\/?p=3186"},"modified":"2014-06-05T17:04:40","modified_gmt":"2014-06-05T17:04:40","slug":"mike-williams","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2014\/06\/mike-williams\/","title":{"rendered":"NoSQL for the Internet of Things. Interview with Mike Williams."},"content":{"rendered":"

“The Internet of Things is a good fit for NoSQL technologies, as you face the challenge of dealing with huge volumes of data over time. For businesses that wish to scale their IoT implementations and make use of the data that these networks create, NoSQL solutions are a better fit than RDBMS options.”–Mike Williams<\/strong><\/p><\/blockquote>\n

I have interviewed\u00a0Mike Williams<\/strong>, software director for i20 Water<\/a>. Mike has an interesting use case for NoSQL in the area of water distribution networks. We also discussed how NoSQL can be used for the Internet of Things<\/a>.
\nRVZ<\/p>\n

Q1. What is the business of i20 Water?\u00a0<\/strong><\/p>\n

Mike Williams:\u00a0<\/strong>i2O is the world\u2019s leading developer of Smart Pressure Management\u00a0solutions<\/a> for water distribution networks.<\/p>\n

Q2. What are the main benefits for utility companies?\u00a0<\/strong><\/p>\n

Mike Williams:\u00a0<\/strong>i2O\u2019s Smart Pressure Management solutions optimise the performance of\u00a0water distribution networks through improving network visibility, and\u00a0enabling the remote control and automatic optimisation of network\u00a0pressures.
\nThese technology-enabled best practices deliver benefits in six key areas,\u00a0with customers typically achieving return on investment in 6-18 months.
\nThe opportunities for savings fall into two areas: on the network side, we\u00a0see leakage reduction, energy savings and a big reduction in burst pipes.
\nFor our utility customers, there are business-level returns as well based\u00a0on improved customer service and operational cost savings. We also see\u00a0customers being able to extend the life of their assets across the network\u00a0as well, so they see a real long-term benefit to being able to control\u00a0water pressure more accurately.<\/p>\n

Q3. Do you have any metrics to share with us on the estimated volume of water saved by using your software?\u00a0<\/strong><\/p>\n

Mike Williams:\u00a0<\/strong>The best metric we can give is the simple volume of water that we help\u00a0customers save: we currently help our customers to save over 235 Million\u00a0Litres of water every day.<\/p>\n

Q4. How can big data be used to reduce water leakage?\u00a0<\/strong><\/p>\n

Mike Williams:\u00a0<\/strong>The i2O system monitors and controls water pressure throughout a zone or\u00a0network. This enables water companies to fully optimise water pressures\u00a0remotely and automatically to meet agreed customer service levels\u00a0throughout the network. The Big Data is all of this time-series data of\u00a0pressures and flows (and other metrics) for lots of locations over years\u2019\u00a0worth of points.
\nThe solution continuously learns key characteristics within a Zone and\u00a0then automatically controls the pressure within the Zone to achieve a\u00a0stable target pressure at the critical point. This is achieved through a\u00a0sophisticated mathematical algorithm, which automatically generates a\u00a0control model. The control model is supplied \u2018over the air\u2019 from i2O\u00a0software to the Pressure Reducing Valve (PRV)<\/a> controller.
\nThe control model is automatically updated if the software detects a\u00a0significant change in the head loss characteristics. This ensures that no\u00a0more pressure than is required enters the network on an ongoing basis. The\u00a0i2O Automatic Optimisation solution is the world\u2019s first \u2013 and most widely\u00a0deployed \u2013 system for automatically optimising and remotely controlling\u00a0water pressure in your network.
\ni2O\u2019s Automatic PRV Optimisation has delivered significant results in\u00a0hundreds of zones for major water companies worldwide.<\/p>\n

Q5. Could you please describe your IT back-end infrastructure? What are \u00a0the main challenges you have?\u00a0<\/strong><\/p>\n

Mike Williams:\u00a0<\/strong>We have an eco-system of distributed loosely coupled Services, each with\u00a0access to numerous dedicated and tailored data stores.
\nThese services\u00a0collaborate through an Event-Driven Architecture (EDA)<\/a> and a distributed\u00a0Event Broker to deliver business services to our customers.
\nThe main\u00a0challenges are around the scalability of these services and the data\u00a0stores where the vast amount of time-series data are held.
\nDue to the way we have architected our data, we have the ability to replay\u00a0these events over history when we make changes to our products and\u00a0services – we can use this historical data to analyse how devices on our\u00a0networks respond to changes in circumstances, and measure what difference\u00a0our new features would provide.<\/p>\n

Q6. Why did you make the shift to NoSQL? What were you doing previously?\u00a0<\/strong><\/p>\n

Mike Williams:\u00a0<\/strong><\/strong>The challenge was the overall scale\u2013up of the whole platform. From both a\u00a0technical and a business stand-point, we needed to scale up for the next years.\u00a0Based on the number of devices that our customers had in place, and the\u00a0number of new customers that we were projected to win, our RDBMS was not\u00a0able to cope by design. Storing time-series data is a specialist need that\u00a0column-oriented data stores are better suited to than traditional RDBMS\u00a0row-oriented technologies. We were (and for some customers, still are)\u00a0using MS SQL Server<\/a> as the only data store. NoSQL technologies also better\u00a0fit our data modelling needs such as searching, where we employ the\u00a0ElasticSearch<\/a> NoSQL<\/a> solution in a clustered manner.<\/p>\n

Q7. What is your experience of using a NoSQL database so far?\u00a0<\/strong><\/p>\n

Mike Williams: \u00a0<\/strong>So far it has been very positive. We had a learning curve to go up and the\u00a0technologies themselves (Cassandra<\/a> and ElasticSearch<\/a>) have matured greatly\u00a0in the last 18 months that we have been using them.<\/p>\n

Q8. What does the future hold for Cassandra in your organisation? How are\u00a0\u00a0you using other database types as well?\u00a0<\/strong><\/p>\n

Mike Williams:\u00a0<\/strong>We moved over to using Cassandra as our main data store for time-series\u00a0data \u2013 this was because it provides better support for columnar data, as\u00a0well as meeting the requirement that we had around scale.\u00a0We are committed to Cassandra for the foreseeable future and so it\u2019s\u00a0future is to grow alongside our business. We also use ElasticSearch as\u00a0mentioned, as well as PostgresSQL<\/a> when our specific data models dictate the use of tabular, relational data.<\/p>\n

Q9. Do you work on the cross-over with the Internet of Things?\u00a0<\/strong><\/p>\n

Mike Williams: \u00a0<\/strong>Indeed we do as we produce intelligent devices that communicate with our\u00a0Platform over the Internet (GPRS <\/a>mobile network). The devices\u00a0automatically sample for pressure, temperature and other information that\u00a0can then be compared across the network.<\/p>\n

The Internet of Things<\/a> is a good fit for NoSQL<\/a> technologies, as you face\u00a0\u00a0the challenge of dealing with huge volumes of data over time. For\u00a0businesses that wish to scale their IoT implementations and make use of\u00a0the data that these networks create, NoSQL solutions are a better fit than\u00a0RDBMS options.
\nThe ability to capture information from across our network has two key\u00a0value propositions: the first is for our customers right now, as they can\u00a0manage their water pressure more effectively. The second is the long term\u00a0value that the data can provide. By being able to model and re-use\u00a0historical data, we can offer much more value to customers than they can\u00a0achieve by themselves. We can add new features to our platform, and\u00a0demonstrate how these new features can provide greater opportunities to\u00a0save money and water for customers.<\/p>\n

———————<\/p>\n

Mike Williams<\/strong> is the software director for i20 Water<\/a>. He has 25 years of experience working for innovative high-tech companies in finance, payments, process engineering and the environment, where he has focused on solving problems and translating business challenges into tangible technical solutions. Before joining i2O Water Mike was the Chief Software Architect and head of development for Bottomline Technologies, the leading supplier of banking transaction and payments software.
\nMike is also an Agile Coach and has helped transform numerous businesses as they become Agile in their approaches to business as a whole and not just software development. Mike is the organiser and founder of the Agile South Coast group.
\n<\/em><\/p>\n

Related Posts<\/strong><\/p>\n

–Big Data and NoSQL: Interview with Joe Celko. ODBMS Industry Watch, February 20, 2014.<\/a><\/strong><\/p>\n

Resources<\/strong>
\nODBMS.org: NOSQL DATA STORES<\/strong>
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\nFree Software<\/em><\/a>
\nArticles Papers Presentations<\/a><\/em>
\nDocumentations,Tutorials,Lecture Notes<\/a><\/em>
\nPhD and Master Thesis<\/a><\/em><\/p>\n

Follow ODBMS.org on Twitter: @odbmsorg<\/a><\/strong><\/p>\n

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“The Internet of Things is a good fit for NoSQL technologies, as you face the challenge of dealing with huge volumes of data over time. For businesses that wish to scale their IoT implementations and make use of the data that these networks create, NoSQL solutions are a better fit than RDBMS options.”–Mike Williams I […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[66,92,680,679,286,678,681,412,413,446,499],"_links":{"self":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/3186"}],"collection":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/comments?post=3186"}],"version-history":[{"count":22,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/3186\/revisions"}],"predecessor-version":[{"id":3310,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/3186\/revisions\/3310"}],"wp:attachment":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/media?parent=3186"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/categories?post=3186"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/tags?post=3186"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}