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Big data meets scalable visualizations

jatorre
November 08, 2013

Big data meets scalable visualizations

jatorre

November 08, 2013
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  1. 4

  2. 5

  3. 6

  4. Maps are the most popular type of data visualization Everything

    happens somewhere ! Where are your clients? IP=location ! So everything can be analyzed and visualized on maps
  5. Making maps is hard because… Tools are not there yet.

    They are for GIS experts ! Handling 100 points is easy, 1Million is hard ! Data chages! Is not about printing maps online!
  6. 23

  7. Most people don’t need Big Data technologies But when you

    can’t…. when it really explodes… You just need to start collecting and analyzing data. Don’t focus on technology, probably your database can already do it ! You are not Facebook, don’t be cheat
  8. Foreign Data wrappers Connect PostgreSQL to almost anything Oracle Hadoop

    MySQL MongoDB CouchDB Redis …. Twitter Email S3
  9. WITH%hgrid% %%%%%AS%(SELECT%Cdb_rectanglegrid(Cdb_xyz_extent(8,%12,%5),% %%%%%%%%%%%%%%%%Cdb_xyz_resolution(5)%*%4,% %%%%%%%%%%%%%%%%%%%%%%%%%%%Cdb_xyz_resolution(5)%*%4)%AS%cell)% SELECT%x,% %%%%%%%y,% %%%%%%%Array_agg(c)%vals,% %%%%%%%Array_agg(d)%dates% FROM%%%(SELECT%St_xmax(hgrid.cell)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%x,% %%%%%%%%%%%%%%%St_ymax(hgrid.cell)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%y,%

    %%%%%%%%%%%%%%%Count(i.cartodb_id)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%c,% %%%%%%%%%%%%%%%Floor((%Date_part('epoch',%built)%Q%Q10418716800%)%/%32837875)%d% %%%%%%%%FROM%%%hgrid,% %%%%%%%%%%%%%%%us_po_offices%i% %%%%%%%%WHERE%%St_intersects(i.the_geom_webmercator,%hgrid.cell)% %%%%%%%%GROUP%%BY%hgrid.cell,% %%%%%%%%%%%%%%%%%%Floor((%Date_part('epoch',%built)%Q%Q10418716800%)%/%32837875)% %%%%%%%)%f% GROUP%%BY%x,% %%%%%%%%%%y
  10. { %%rows:%[ %%{ %%%%x:%0, %%%%y:%0, %%%%vals:%[2], %%%%dates:%[457] %%}, %%{ %%%%x:%1,

    %%%%y:%0, %%%%vals:%[1,1,4], %%%%dates:%[2,3,4] %%%%} %%] }
  11. 1 10 100 1000 3mb 70mb 300mb 1.5 2 1.2

    300 70 3 Raw Datacube Payload sizes
  12. Think on the value of location on your data, and

    use it! Is very likely you have geospatial data already ! Complete the big data cycle: Don't forget data visualization ! Find the stories inside the data and show them!