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.
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
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.
IMDBS = In-Memory Database Systems
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.
S dillon mtlc 5-02-2013
Big Data & Energy EfficiencyStephen Dillon, Data Architect, Technology Strategystephen.email@example.comMay. 02. 2013
Schneider Electric 2- Technology Strategy - Stephen Dillon – 2013BIG DATAVolume, Velocity, Variety
Schneider Electric 3- Technology Strategy - Stephen Dillon – 2013EnergyEfficiencyNeedsReal-timeAnalyticsCloudTechnologies(NoSQL &NewSQL)The Cloud is thenew data center.One size does notfit all!Memory is the newdisk.In Support of Big Data & EnergyEfficiency
Schneider Electric 4- Technology Strategy - Stephen Dillon – 2013Supporting Energy Efficiency●You have had the technologies to provide batch processing andanalytics 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 theNewSQL umbrella.● In-memory database systems such as VoltDB
Schneider Electric 5- Technology Strategy - Stephen Dillon – 2013I.M.D.B.S. Today●In-memory Database Systems have been around for decades inanalytics 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.
Schneider Electric 6- Technology Strategy - Stephen Dillon – 2013Trends & Expectations●Real-time analytics● On-demand energy needs this to reach the next level of sustainability andefficiency● 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; notwhat happened a month ago.●We understand; if the demandresponse window shrinks, energyefficiency & savings are gained.
Schneider Electric 7- Technology Strategy - Stephen Dillon – 2013No “I” in Big Data…oh wait a minute
Schneider Electric 8- Technology Strategy - Stephen Dillon – 2013PANELISTSIntroductions