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No Sql Bigdata Drone

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No Sql Bigdata Drone

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No Sql Bigdata Drone

  1. 1. Me: Dony Riyanto, a veteran geeks Code Pascal in 91’s, Assembly in 92’s, Learn soldering in 97’s, and hunger for more knowledges (2015) vizualize.me/donyriyanto slideshare.net/donyriyanto donyriyanto@yahoo.com
  2. 2.  Why should I learn Internet? Why should I have a mobile phone (hp)?  Is PHP developer need to learn NoSQL/NewSQL?
  3. 3.  Wikipedia source:  Some other source:
  4. 4.  You need to flatten the world to 2D (rows n cols) and create the relationship.  They lough at you when your table un- normal (no/wrong relationship)  You’re commited to relationship  A complex relationship with ‘normal’ table is not good for very big data, and its badly slowing down
  5. 5. ‘New’ types of data/model:  Key/Value  Document  Graph/Relation  Relational/Large Column  Also Raw file (image, sound, log, webpage, PDF, DNA structure, finger print)
  6. 6.  MariaDB  MongoDB  Neo4J  Redis  VoltDB  So much more
  7. 7.  “A bin replacement for MySQL”  Maria is the little sister of My  MariaDB is 100% compatible to corresponding MySQL version, but with many additional features: › Support UUID natively › Support big data and a lot faster › Support flexible collumn and JSON like data, natively › etc
  8. 8. When should I use MariaDB?  Stick to MySQL but need better solution  Have existing project with mysql that have database problem
  9. 9.  From the maker of PostgreSQL  World fastest in-memory DB to date  Why in-memory? They found that 70% of an SQL query time is cosumed by disk operation,  So they just come out with this great solution:  A sql like database all in memory  It is grid by its self. Its both Master and Slave  SQL90 subset compatible  Pretty much easier to understand when used to have conventional so called “Rows and Cols”
  10. 10.  ‘Spike’ project  When fast is critical (financial data, command center application, medical, etc)
  11. 11.  Read reviews and comparrisons  Review key user’s review  Learn by doing
  12. 12.  Imagine that you have data of all banking transaction in the world or in Indonesia,  Imagine that you have access to Facebook database and all the post and profile of their users,  Imagine that you can have access to all SMS traffic in Indonesia,  What would you do?
  13. 13.  Imagine that you have real time agriculturar data of Indonesian’s Petani (farmer),  Imagine that you have real time satellite and ground station’s data of Indonesian farming field,  Image that you have real time data of national statistics (BPS),  Image that you have realtime data of commodities market,  Can you help our farmer faster before they crop?
  14. 14.  Telco  Tax  Banking  Agriculture  Industry  Crime handling  Corruption handling  Dissaster handling  So so much more
  15. 15. The so called 3 Vs  Volume  Variety  Velocity And more Vs:  Virality, Viscosity  Veracity, Value
  16. 16. Big data need ‘Big’ infrastructures:  Database that can handle big data (combination of RDBMS, data warehousing, NoSQL, big data)  Hardware (server, storage, network) that can handle big data  Software: › Data visualisation › Analytical tools › Machine learning tools  Things to gather data (sensors network): › IoT, smart things, › Drone, ROV, HASP, satellite  Brainware: Data Scientist (“is the most sexiest job in coming years”)
  17. 17.  Is internet for you? Yess  Developers (DBA, mobile dev, web dev, backend dev, UI/UX designers, makers)  Small Medium Enterprises (UMKM),  Bioinformatics, ElectroInformatics, GeoInformatics, Scientist, Statistical guy, (how they quickly detect malaria epidemi in a particullar area just by tweet)  Advertising industries, Manufacturing, Farming and agriculture (UCS ‘ads that see’, blumbangreksa)
  18. 18. Know your web visitor better.  Collect all information for 3 months: › Everyting: traffic, cookies, browser agent, IP, location, average time per click/page/leave, click source, etc › Challange them to login using socmed (facebook, twitter) or tag › Record their post and socmed actvities › Analyze the words, profile back ground, ages, communities  Make a simple data visualization a.k.a dashboard.  Can you see clearly now?  Repeat the task with collaboration to partner’s web site...
  19. 19.  Play video  See: slideshare.net/donyriyanto

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