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BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
BitYota Data Warehouse Podcast
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BitYota Data Warehouse Podcast

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In this slidecast, Dev Patel and Poulomi Damany from BitYota describe the company's Data Warehouse Service. …

In this slidecast, Dev Patel and Poulomi Damany from BitYota describe the company's Data Warehouse Service.

"Our vision is to make data and analytics accessible to all. There's a revolution underway and we're taking sides. We want to create a data platform that enables everyone -- from data scientists and engineers to SQL savvy analysts, business and product users, to understand their data, to build better products/services, and create new avenues of growth and productivity."

Watch the video presentation: http://wp.me/p3RLEV-1SS

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Transcript

  • 1. BitYota – data warehouse service ! An overview! Dev Patel, CEO! © 2014
  • 2. BitYota: Who we are! Problem   Today’s  big  data  analy/cs  is  either  a  ‘Big  Cost’  or  a  ‘Big  Headache’  or  both  for   companies  of  all  sizes.  Users  have  to  learn  new  skills  and  CEOs  need  to  buy   uniquely  engineered,  prohibi/vely  expensive  systems.     Solu+on   BitYota  offers  a  be-er  alterna0ve:  A  Data  Warehouse  Service  for  Big  Data   analy0cs.  This  PaaS  offering  takes  away  both  big  cost  and  big  headache,   making  analy0cs  accessible  to  everyone  at  scale,  with  no  compromise  on   func0onality  or  service  levels.     Customers   Mobile  apps,  E/commerce,  Adver/sing/Marke/ng,  Games   Founded  Sep  2011  by  data  experts:  Dev  Patel,  Harmeek  Bedi  and  Soren  Riise.     Background   Opportunity   Team   Company  has  raised  $12M  through  Seed  &  Series  A  from  Globespan  Capital,   Social+Capital  Partnership,  Dawn  Capital,  Andreessen  Horowitz,  Crosslink   Capital,  Morado  Ventures,  &  individual  investors  Maynard  Webb,  Graham   Summers,  Jerry  Yang  and  Sharmila  Mulligan.   Companies  are  increasingly  looking  to  gain  insights  from  their  data  via   analy/cs.  Analy/cs  for  big  data  in  the  cloud  is  BitYota’s  opportunity.   Management:  Dev  Patel,  CEO;  Harmeek  Singh  Bedi,  CTO;  Soren  Riise,  Chief   Cloud  Service;  Poulomi  Damany,  VP  Product.   Its  core  team  has  35+  years  of  big  data  experience  at  Yahoo!,  Oracle,  Veritas/ Symantec,  Informix,  BMC,  Kabira/Tibco,  and  Microso_.    
  • 3. Does This Sound Like You?!! OR You’re a company that just launched .. And you need some critical insights for what’s next OR Your data infrastructure can’t scale .. And you can’t spare any more engineers or money to maintain it You’re a company that just launched … You have lots of data in multiple silos .. And it takes too long for your analysts to get answers Today’s Big Data = Big Cost/Big Headache! 3
  • 4. What questions do you want answered?! How can I combine social profiles, in-app purchases, and event stream data? Who are my best users? How do I increase engagement? Why is the new app version crashing? Access patterns by OS/ device? What’s my ROI on my marketing spend? Where should I be spending more/less $$$? 4
  • 5. BitYota: Data warehouse for next gen data! Variety, semistructured data Velocity, analytics on fresh data Velocity, “fast” analytics No translation to structure & No data modeling Continuous extract of changing data from MongoDB MPP architecture – scale with Compute Cloud, easy set up with Burst capacity Agility & Time-toMarket No CAPEX & low OPEX Elastic scale up/ down Integration into SQL/BI ecosystem Managed Service & Pay per Use 5
  • 6. BitYota is focused on use cases where …! Customers want:! 1.  Analytics over data from multiple sources! 2.  Migrate analytics from on-premise to Cloud ! 3.  Analytics on data from single source NoSQL or relational transactional systems ! 4.  Analytics on “fresh data” ! 6
  • 7. Markets for BitYota! Companies in! •  Advertising/Marketing! •  Social Media! •  SaaS! •  Games & Entertainment! •  E-commerce! •  Communication & Productivity ! 7
  • 8. BitYota focused in new Big Data Analytics! •  Data from Multiple sources in Multiple formats ! User profiles Social data Server Logs ! ! Volume   Variety   Velocity   ! Semi-structured data types (JSON, XML), Data types for new applications – timestamp, IP, location, etc! Table Layout – row and column, on disk, memory, external tables ! •  Fast time to analytics ! ! Load and explore directly, not dependent on slow & fragile ETL! •  Interactive analytics ! ! Inventory Deploy in a heterogeneous environment; scale out; scale storage & compute independently! •  Flexible Storage ! Sales Orders/ Returns Website Views & Clicks •  Cost effective, elastic capacity! Use ANSI SQL directly on new data types. Leverage existing BI tools! 8
  • 9. Business Analytics on data from MongoDB! Mobile/Web Apps Primary shard SQL over JSON, and access from BI tools BitYota  Cluster   Secondary shards Compute  nodes   Mongo dump Oplog Tail Load BitYota   Extract   Tool   BSON,   JSON   Extract   Load   Data  nodes   Schedule incremental extract and load MongoDB extract format (BSON) Transform  &  Analyze   Joins across collections SQL over JSON, UDFs Transforms into Cols for performance Views for BI tool 9
  • 10. Process to Load Data into BitYota! SOURCE  JSON  DATA   LOAD  DATA   "session":[{   "u":"8927ABBCD2873CCD",   "v":"1.0",   "uid","TheTestUser1",     "dv":"Apple  iPhone  3GS",     "t":200                           }   CREATE  TABLE  session(    jdoc  JSON   )       1-­‐+me  setup     •  Scheduled  Load   •  Schema  auto-­‐discovered   •  Table  auto-­‐created   ANALYZE  DATA   OPTIMIZE  DESIGN   SELECT  jdoc-­‐>'u’,  jdoc-­‐>’t’   FROM  session;   CREATE  TABLE  session_cols  (          u                               TEXT,          t                                 INT,        origjdoc                  JSON  )   PARTITION  BY  RANGE  (t)    (PARTITION  VALUES  ('0'),   PARTITION  VALUES  ('50'),   PARTITION  VALUES  ('200')  )   COLUMNSTORE  STORAGE   (SEGMENTSIZE  13102   TABLESIZE  200000);   INSERT  INTO  session_cols        SELECT  jdoc-­‐>'u',    ( jdoc-­‐>'t')::int8,  jdoc          FROM  session;;   Change  MongoDB  JSON  doc  structure  any/me  =  NO  extra  downstream  effort   needed   10
  • 11. As a Service! •  Launch cluster in minutes ! •  Removes the ‘headache’ of database management! •  No hardware, no software installation & upgrades; no licenses ! •  Available on AWS & Rackspace! 11
  • 12. Recap! •  BitYota is a Cloud based Data Warehouse Service for Big Data Analytics.! •  Its core attributes are:! •  100% Service oriented! •  Analytics on data from multiple sources/formats! •  Analytics on “fresh” data ! •  Customers are gaining deep insights on their business operations! •  Customers in Games, Mobile apps, advertising/ marketing, e/commerce! 12

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