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S dillon mtlc 5-02-2013
This document discusses how big data and cloud technologies can support energy efficiency through real-time analytics. It notes that batch processing on historical data using technologies like Hadoop and Amazon EC2 is useful, but to truly enable energy efficiency companies need to leverage in-memory databases and real-time analytics. This allows analyzing data as it streams in and reacting quickly to energy needs and demands, shrinking the demand/response window and gaining greater efficiency and savings. Trends include pushing computations toward data in real-time to enable previously impossible innovations and solutions that help end users react quickly based on current conditions.
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S dillon mtlc 5-02-2013
- 1. Big Data &Energy Efficiency Stephen Dillon, Data Architect, Technology Strategy stephen.dillon@schneider-electric.com May. 02. 2013
- 2. Schneider Electric 2-Technology Strategy - Stephen Dillon – 2013 BIG DATA Volume, Velocity, Variety
- 3. Schneider Electric 3-Technology Strategy - Stephen Dillon – 2013 Energy Efficiency Needs Real-time Analytics Cloud Technologies (NoSQL & NewSQL) The Cloud is the new data center. One size does not fit all! Memory is the new disk. In Support of Big Data & Energy Efficiency
- 4. Schneider Electric 4-Technology Strategy - Stephen Dillon – 2013 Supporting Energy Efficiency ●You have had the technologies to provide batch processing and analytics on historical data. You just need to go do it. ● Examples include Hadoop, Map Reduce, Amazon EC2, Windows Azure ●You need to leverage Real-time analytics and technologies under the NewSQL umbrella. ● In-memory database systems such as VoltDB
- 5. Schneider Electric 5-Technology Strategy - Stephen Dillon – 2013 I.M.D.B.S. Today ●In-memory Database Systems have been around for decades in analytics systems. ● Supported column-stores only ● Non-OLTP or non-row-based ●Only recently has it become feasible to support OLTP in-memory. ●New breed of IMDBS designed to leverage advances in main memory ● ACID compliant ●We can now store entire Terabyte level databases in main memory.
- 6. Schneider Electric 6-Technology Strategy - Stephen Dillon – 2013 Trends & Expectations ●Real-time analytics ● On-demand energy needs this to reach the next level of sustainability and efficiency ● What took hours or days can be done in minutes or less ●Trend is to push computations toward the data...not vice versa. ●Software designers can now create solutions not previously possible. ● Those innovations are taking place today. ●End users will be able to react quickly per what is really happening; not what happened a month ago. ●We understand; if the demandresponse window shrinks, energy efficiency & savings are gained.
- 7. Schneider Electric 7-Technology Strategy - Stephen Dillon – 2013 No “I” in Big Data…oh wait a minute
- 8. Schneider Electric 8-Technology Strategy - Stephen Dillon – 2013 PANELISTS Introductions
Editor's Notes
- #3 We should all know, at this discussion what Big Data is and why it is important. So we will not attempt to cover this today. Hopefully there is nobody in the room viewing Big Data as the individual in this slide.
- #4 There are three key areas that influence the solutions we create for Big Data and the resulting support for energy efficiency.The CloudTechnologiesReal-time Analytics
- #5 The technologies for batch processing have been around for a few years now and are generally accepted by those in the know.Batch processing can only take us so far. We have to begin looking ahead to energy efficiency solutions that lead us to the next levels of sustainability. We will do this via real-time analytics.
- #6 IMDBS = In-Memory Database Systems
- #8 Big Data is in the Wild West phase. Solution architectures will be made up of many technologies to solve individual problems; and rightfully so. However, there are no standards and as such it can be inherently difficult for teams to communicate.