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20131118 - Seoul - Advanced Computing Conference 10 - New Trends In Hadoop
 

20131118 - Seoul - Advanced Computing Conference 10 - New Trends In Hadoop

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http://www.zdnet.co.kr/news/news_view.asp?artice_id=20131119145440

http://www.zdnet.co.kr/news/news_view.asp?artice_id=20131119145440

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  • Add your contact information including “MapR Job Title” starting with the word “MapR” such as MapR Solutions Architect etc.Add the #hashtag of the particular meet-up and chose one or more of the others BUT NOT ALL OF THEM
  • Reduce cost and time
  • This slide and the following demos sets the stage to introduce the overall big idea of talk: that there are many cool advances in these different areas, but without the ideas introduced here, it’s hard to connect these technologies together easily and reliably –
  • This slide and the following demos sets the stage to introduce the overall big idea of talk: that there are many cool advances in these different areas, but without the ideas introduced here, it’s hard to connect these technologies together easily and reliably –
  • Gives up random access read on filesGives up strong authentication / authorization modelGives up random access write / append on files
  • http://trends.truliablog.com/vis/metro-movers/
  • Main point: these applications can be served directly from MapR filesystem because it presents as a POSIX filesystem using NFS protocol.Non-bigdata applications like Node.JS and D3.js can work directly off of MapR, and don’t care that it is also a highly scalable DFS
  • Talk track: Let’s look at another example of new technologies on Hadoop in more detailReal-time analysis has many uses, such as Sentiment AnalysisWhat is sentiment analysis and why would you use it? (then start into slide details)
  • Talk track: This diagram shows what you are doing at each step in the process. Of course the final steps are interpretation of the output of your application by humans to gain insight that will power business decisions.
  • Talk track: This diagram shows what you are doing at each step in the process. Of course the final steps are interpretation of the output of your application by humans to gain insight that will power business decisions.
  • Talk track: This diagram shows what you are doing at each step in the process. Of course the final steps are interpretation of the output of your application by humans to gain insight that will power business decisions.
  • Allen: This is the MapR view. You can do it first or the non-mapr view first. I think you said you preferred to do non-mapR first?
  • Note to speaker: listen to explanation at about 23min into Ted’s video for this slide and next slide showing the sliding window
  • Note to speaker: here is the option if you don’t have a distributed system with a reatlime replicated filesystem (such as what MapR has). Alternative is to use something like Kafka. Much harder to build etc.
  • “redundant redundancy” is about too many data copies = inefficient design and risk of inconsistency between systems
  • Allen: This is the MapR view. You can do it first or the non-mapr view first. I think you said you preferred to do non-mapR first?
  • Talk track: Let’s look at another example of new technologies on Hadoop in more detailReal-time analysis has many uses, such as Sentiment AnalysisWhat is sentiment analysis and why would you use it? (then start into slide details)
  • Allen: Is this useful? Is itMapR specific?
  • What is NAS? Should the little elephant say MapR? Remember this slide was from a sales pitch, so you may have to make clear it’s talking about MapR
  • Allen: Do we want to use the images including Korean bowl or better to avoid possible negative reaction and just use a text slide for transition to thinking about legacy applications?
  • Another example of the same thing (this slide can be suppressed – just another view of the inaccuracies)
  • I hid this one and went with the other format of your slide.
  • Again, I’ve hidden this version and gone with the one that is formatted differently. Same content, though.
  • Talk track: In this presentation, we are going to look in more detail at the tools and technologies used for this middle stage, that is done by machine, the processing and visualization/reporting steps…(Allen: don’t know if you want to say more details here, but I suspect this slide is just a transition to get audience focused on the processing & the viz/reporting part of the work flow.
  • Note to speaker: Slide just sets up transition from business goals to architecture diagram slide Don’t need a lot of detail, but introduce Storm, say a little about project & new to Apache AND MENTION MAPR’s Ted Dunning is one of the PROJECT MENTORSTalk track: Now that you see what is the business goal of using real-time sentiment analysis, let’s look at the architecture for a sample project… Here are some technologies you’ll need…{explain] and then say “and on the next slide we see a diagram of the architectural design – or call it work flow?
  • Allen: This is the MapR view. You can do it first or the non-mapr view first. I think you said you preferred to do non-mapR first?
  • Allen: This is the MapR view. You can do it first or the non-mapr view first. I think you said you preferred to do non-mapR first?
  • Allen: This is the MapR view. You can do it first or the non-mapr view first. I think you said you preferred to do non-mapR first?
  • Allen: This is the MapR view. You can do it first or the non-mapr view first. DOES THE WEB SerVER/WEB DATA ALSO COME OFF if not a MapR cluster?????
  • Allen: This is the MapR view. You can do it first or the non-mapr view first. DOES THE WEB SerVER/WEB DATA ALSO COME OFF if not a MapR cluster?????
  • The distinction is not clear to me… Attempt 2 meaning different slide to show same idea? Confusing to mix symbols of actions/ ideas with components in work flow
  • This is my start on the non-MapR view
  • Do you want to include this information?
  • MapR enables integration by providing industry-standard interfacesMore 3rd party solutions work with MapR than any other distributionProprietary connectors not neededNFSAll file-based applications can read and write dataExamples: Linux utilities, file browsers, Informatica UltraMessagingODBC 3.52All BI applications can leverage HiveExamples: Excel, Crystal Reports, Tableau, MicroStrategyLinux PAMAny authentication provider can be usedExamples: LDAP, Kerberos, 3rd party
  • NNote from Ted’s video hints: HBase storage here can be handy but is much more of a sideline to the main idea.

20131118 - Seoul - Advanced Computing Conference 10 - New Trends In Hadoop 20131118 - Seoul - Advanced Computing Conference 10 - New Trends In Hadoop Presentation Transcript