Enterprise Mashup Infrastructure Kapow Mashup Server

Slideshow Transcript

1. Slide 1: Ente rpris e Mas hup Infras truc ture Andre as Krohn Product Manager Kapow Te chnologie s
2. Slide 2: Ag e nda  What is a Ma s hup  The Bus ine s s Va lue of Ma s hups  Ma s hup Infras tructure  Enterpris e Mas hup Exa mple s  Demo
3. Slide 3: Intro duc tio n  Why is Exce l so popula r ins ide Enterpris e s?  It a llows the bus ine s s us e rs to look a t da ta the y wa y the y wa nt to  It give the bus ine s s us e rs the a bility to build mini-applic atio ns a s ne e de d without s upport from ce ntra l IT  Why is da ta integration a nd SOA so important ins ide Enterpris es ?  S haring data be twe e n s ys te ms incre a s e s the ove ra ll va lue of tha t da ta  Toge the r s e ve ra l s ys te ms ca n s o lve mo re pro ble ms tha n the s ys te ms ca n individua lly  Ma s hups combine s a nd s implifie s a ll this !
4. Slide 4: Wikipe dia Mas hup (or ma s h it up) is a J a ma ica n Cre ole te rm me a ning to de s troy.  Mas hup (we b a pplica tion hybrid), a we b a pplica tion tha t combine s da ta  a nd/or functiona lity from more tha n one s ource Mas hup (mus ic), a mus ica l ge nre of s ongs tha t cons is t e ntire ly of pa rts of  othe r s ongs Mas hup (vide o), a vide o tha t is e dite d from more tha n one s ource to a ppe a r  a s one Mas hup, in pa rts of the UK me a ns a ma s h or pot of te a (colloq. Yorks hire ),  othe r a re a s bre w or s ta nd te a
5. Slide 5: Mas hup: We b Applic atio n Hybrid  Combine s conte nt from s e ve ra l s ource s via s e rvice s  Focus on s e rvice s not s oftwa re  Ofte n a Ma s hup is built without s upport from the provide rs of the s e rvice s  The We b a s a P la ttform  Mos t ma s hups on the we b toda y a re s imple  A we bs ite + Google Ma ps  Ente rpris e ma s hups  Intra ne t a pps + Inte rne t s ite s + …  You a re a lre a dy us ing Ma s hups !  Exce l s pre a ds he e ts  Ad-hoc re porting  Da ta Migra tion  Conte nt Aggre ga tion  Ma na ge me nt Da s hboa rds
6. Slide 6: Data: The Eme rg ing Pro duc tivity Drive r Transformational Transactional Tacit-Oriented Tasks (20%) Tasks (40%) Tasks (40%) Task-Intensive Data-Intensive IT-Controlled Do-it-Yourself Unstructured Structured Collaborative Individualized Productivity Potential Low High
7. Slide 7: Mas hups : The Ne xt IT Pro duc tivity Wave  Huge untappe d productivity potentia l in knowle dge workers  Strate gic corporate da ta not be ing us ed e ffe ctively  Applica tion controls & re s tricts da ta ge ne ra tion a nd flow  Ce ntra l IT only s upports ke y bus ine s s proce s s e s , not a d-hoc informa tion re s ource s  The re is a ne e d for lightweight a cce s s to corporate da ta  The right da ta , to the right pe ople , a t the right time  Logica l e xte ns ion of S OA inve s tme nt to the we b tie r
8. Slide 8: The Lo ng Tail o f Pro je c ts # Users Using traditional approaches just the most important projects can be implemented With Mashups the long tail of projects can be implemented # Projects
9. Slide 9: Ente rpris e Mas hup Infras truc ture  Enable Enterprise Ma shups  Ente rpris e – up time , ma inta ina nce , s e curity, loa d ba la ncing e tc  Le verage e xis ting infras tructure  Exis ting da ta ba s e s , s e rve rs e tc  Fit into e xis ting choice of te chnology  Extend existing infrastructure  Comple me nting S OA  Improve d a cce s s to e xis ting da ta s ource s (we b s ite s for e xa mple )  Ma jor compone nts  Ma s hup Builde rs  Ma s hup Ena ble rs
10. Slide 10: Mas hup Builde rs  Produce the us e r interface of a Ma s hup  Toda y this is mos tly done by programming  J a va , C#, Ruby on Ra ils . e tc  The trend is towards doing this by a ss e mbling  Ma s hups by non-de ve lope rs  Conne cting widge ts to cre a te compos ite a pplica tions  Exa mples  Exce l  BEA Aqua Logic P a ge s  IBM QEDWiki  Ya hoo! P ipe s  Nee d a cce s s to da ta!
11. Slide 11: S o , Whe re Is The Data? File s ys te m We b Bas e d Databas e (Uns truc ture d) (Uns truc ture d) (S truc ture d) MS Office Doc’s, We b Ba se d Conte nt Exa mple s RDMS, Flat files P DF, e ma il & Da ta Ente rpris e S cope 15%-20% of da ta 80%-85% of da ta Growth Tre nd Mode s t Exploding Exploding SQL Querie s Docume nt Re trie va l P age Views Da ta Acce s s Me thod Acce s s Gra nula rity Data Ele me nt Docume nt Le ve l Only Full Pa ge P rogra mma tic AP I’s Ye s P roprie ta ry No Knowle dge Worke r Low Me d High P roductivity P ote ntia l
12. Slide 12: Ac c e s s the Data: Mas hup Enable rs  Acce s s the unstructured da ta  Webs craping – a cce s s a ll interna l a nd e xternal webs ites  Us e HTML a s a n AP I!  Se rve functiona lity to Mas hup Builders  RES T, RS S , Atom e tc  Ma kes inte rna l a nd externa l resource s a vaila ble  Exa mples :  Ka pow Ma s hup S e rve r  Ope nka pow  Fe e d43
13. Slide 13: Ins ide a Mas hup Data Mashup Users Sources API WS* SQL Mashup Builder DB JMS RPC API Widget AJAX REST Mashup Apps Enabler Web Atom Scraping Widget HTML RSS Web WS* API Widget SQL WS*
14. Slide 14: Kapo w Mas hup S e rve r  Ena bling Ma s hups by giving a cce s s to uns tructure d we b da ta Ha ndle s uns tructure d HTML Ha ndle s J a va S cript Ha ndle s cookie s , s e s s ions , a uthe ntica tion, HTTP s e tc  Ma king da ta & functiona lity from HTML a va ila ble a s ne e de d RES T S e rvice , RS S /Atom fe e d, J a va AP I, C# AP I, P ortle ts , S OAP WS , Writte n to a da ta ba s e e tc  Automa ting wha t a pe rs on ca n do in a brows e r = Robot  250+ cus tome rs Audi, Ba nk of Ame rica , S imply Hire d e tc  Ope nka pow.com Fre e Ma s hup Community Build robots a nd s ha re with the Community Cre a te RES T s e rvice s a nd RS S /Atom fe e ds from we b s ite s
15. Slide 15: Hig hlig hte d Fe ature s  Acce s s the functiona lity of a we b a pp without a n AP I Ha ndle s multi-pa ge na viga tion Doe s it with gre a t s ta bility a nd robus tne s s  Cre a te RS S /Atom fe e ds , RES T s e rvice s from a lmos t a ny we b s ite .  Adva nce d J a va S cript ha ndling a nd e xe cution.  Ha ndle s logins to prote cte d s ite s us ing cookie s , HTTP & form a uthe ntica tion, HTTP s .  Ve ry powe rful da ta e xtra ction a nd HTML inte ra ction us ing re gula r e xpre s s ions , conve rte rs a nd pa tte rns .  Fle xible e rror ha ndling a nd de bugging.  Gra phica l point-a nd-click de ve lopme nt e nvironme nt with 1-click de ployme nt.  Full control ove r the proce s s flow in a robot, including conditions a nd loops
16. Slide 16: Type s o f Ente rpris e Mas hups  Opportunistic Applica tions  S itua tiona l Applica tions  Ma s hups -on-the -fly  Re purpos ing  P orta l Ena ble me nt  Mobilis ing Applica tions  Web Automa tion  Conte nt Migra tion  S wive l-cha ir a utoma tion  Data Colle ction  Compe ta tive Inte llige nce  Re puta tion Ma na ge me nt
17. Slide 17: Oppo rtunis tic Applic atio ns  Addres s ing the Long Tail  Allowing individua ls, sma ll groups or de pa rtme nts to build the re on a pplications  Solving s mall proble ms not worth the investment from ce ntral IT  Taken toge the r thes e s ma ll a pps increas e productivity  Short lifes pan  Throw-a wa y a pps  Apps owne d by bus ine s s us ers, infrastructure owne d by ce ntral IT  S e lf-S e rvice IT
18. Slide 18: Oppo rtunis tic Applic atio ns : Audi  Ma ny integration proble ms  Built a pla tform to be a ble to quickly solve ne w proble ms  Applications drive n by the Bus ine s s Side  Ce ntral IT provide s the infras tructure  Using Ka pow Ma s hup Se rver to provide lightweight SOA ena bleme nt (among othe r things )  Aggrega te content and se rvice s from interna l and e xterna l sources
19. Slide 19: Re purpo s ing  Taking e xisting a pplica tions a nd repurpos ing the m for diffe rent pla tforms and/or us e ca s es  No nee d to cha nge the code in the e xis ting a pplica tions  Avoid cos tly imple me nta tion proje cts  Avoid ris king ne w s e curity is s ue s  Interact with existing a pplica tions via Ma shup Ena ble rs & Exis ting Infras tructure  S e rvice e na ble  P ortle t ge ne ra tion for Ente rpris e P orta ls  Common us e ca s es  P orta l e na ble me nt  Mobiliza tion
20. Slide 20: Re purpo s ing : De uts c he Po s t Wo rld Ne t  20+ busine ss units, e a ch with it’s own unique portal  DHL  Ma ilingfa ctory  e Filia le  e tc  New Deutsche P os t portal with fe a tures from a ll bus ine s s units  Myde uts che pos t.de  Built by repurpos ing the e xis ting porta ls  Ta ke the fe a ture s from e a ch porta l a s ne e de d  P a cka ge it into one unite d porta l  The origina l porta ls a re s till up a nd running
21. Slide 21: We b Auto matio n  Automa te jobs that a pe rson could pe rform  Improve d e fficie ncy  Improve d qua lity  Improve d re lia bility  Common us e ca s es  Automa te d Conte nt Migra tion (a s oppos e to ”bio-migra tion”)  S wive l cha ir a utoma tion
22. Slide 22: We b Auto matio n: GMX  GMX.ne t  Ma jor hos ting compa ny, 1&1 hos ting  Fre e Ema il provide r  Automa ting integration with othe r online e ma il provide rs  Via the we b inte rfa ce che ck e ma il  S ynchronize a ddre s s books  e tc
23. Slide 23: Data Co lle c tio n  Colle ct and structure large amounts of unstructured da ta  Data s ource s both on interne t a nd intrane t  Add value by a dding cus tom ana lyzis or vis ua liza stion to the da ta  Common us e ca s es :  Compe ta tive Inte llige nce  Informa tion S yndica tion  Re puta tion Ma na ge me nt
24. Slide 24: Data Co lle c tio n: S imply Hire d  Job se a rch e ngine  Colle cts jobs from othe r job s ites (Monster.com) as well a s from individua l compa nie s page s  No nee d for the job s ites to provide an AP I, a ll da ta collected via we b pa ge s  Se t industry record  Colle cte d 1 million job profile s in 3 months  5 million job profile s in 2 ye a rs  Provide job se a rch both on S implyHired.com and on 3rd pa rty s ites  Linke dIn  MyS pa ce
25. Slide 25: De mo S c e nario – Co mpe titive Inte llig e nc e Web Based Pricing Data
26. Slide 26: Que s tio ns ? www.kapo wte c h.c o m www.o pe nkapo w.c o m More informa tion a t the Kapow Technologie s booth