• Like
Sascha Dittmann, Ernst & Young: Big Data in der Cloud
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Sascha Dittmann, Ernst & Young: Big Data in der Cloud

  • 591 views
Published

Lightning Talk anlässlich des zweiten CloudCamp Frankfurt am 24.5.2012 in der Brotfabrik in Hausen.

Lightning Talk anlässlich des zweiten CloudCamp Frankfurt am 24.5.2012 in der Brotfabrik in Hausen.

Published in Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
591
On SlideShare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
2
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. * Sascha Dittmann Software Developer / Solution Architect Twitter: @SaschaDittmann Blog: http://www.sascha-dittmann.de
  • 2. * Seit über 13 Jahren: * Software Developer * Solution Architect* Seit über 7 Jahren: * Trainer für technische Trainings * Sprecher auf Konferenzen * IT Consulting *
  • 3. 180.000.000.000.000.000.0001.800.000.000.000.000.000.000 *
  • 4. 180.000.000.000.000.000.000 = 0,18 ZB (Zettabytes) - Stand 2006 1.800.000.000.000.000.000.000 = 1,8 ZB (Zettabytes) - Stand 2011* Quelle: IDC – Analyze the Future
  • 5. Vertikale Skalierung Horizontale Skalierung *
  • 6. Atomicy BasicallyConsistecy AvailableIsolation Soft StateDurabilty Eventually Consistent *
  • 7. *
  • 8. DataNode DataNode DataNode 0067011990999991950051507004+68750 0043011990999991950051512004+68750 0043011990999991950051518004+68750 0043012650999991949032412004+62300 0043012650999991949032418004+62300 1949,0 1952,-11 Map Map Map 1950,22 1950,55 1950,33 Sort Sort Sort 1949,0 1950,[22,33,55] Shuffle Shuffle Shuffle 1952,-11 Reduce 1949,0 1950,55 1952,-11 *
  • 9. DataNode DataNode DataNode 0067011990999991950051507004+68750 0043011990999991950051512004+68750 0043011990999991950051518004+68750 0043012650999991949032412004+62300 0043012650999991949032418004+62300 1949,0 1952,-11 Map Map Map 1950,22 1950,55 1950,33 1949,0 1952,-11Combine Combine Combine 1950,55 1950,33 Sort Sort Sort 1949,0 1950,[33,55] Shuffle Shuffle Shuffle 1952,-11 Reduce 1949,0 1950,55 1952,-11 *
  • 10. RDBMS Map/ReduceDatenmenge Gigabytes PetabytesZugriff Interaktiv und Batch BatchLese- / Schreibzugriffe Viele Lese- und Einmaliges Schreiben Schreibzugriffe Viele LesezugriffeDatenstruktur Statisches Schema Dynamisches SchemaDatenintegrität Hoch NiedrigSkalierverhalten Nicht-Linear Linear *
  • 11. *