How I learned to stop worrying and love Oracle
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- Oracle has evolved over time from being a challenger to IBM mainframes in the 1980s to becoming the dominant database in the 1990s and 2000s as client-server and internet applications grew popular. - New technologies like cloud computing, big data, and NoSQL databases now challenge Oracle's dominance as data volumes and types expand beyond traditional relational databases. - Oracle is responding through acquisitions like Sun Microsystems and new products focused on performance, scalability, and lowering costs to process big data and enable private and public cloud deployments. However, its position is still threatened by new database paradigms and technologies from companies like Google, Amazon, and open source projects.
How I learned to stop worrying and love Oracle
- 1. How I learned to stop worrying and love OracleGuy HarrisonDirector Research and Development, [email protected]
- 5. Looking back to 1987…..http://www.yearbookyourself.com/
- 6. 1987: RDBMS/Minicomputer revolution IBM-based MVS mainframes giving way to Minicomputer architecturesEra of Big glasses32-bit computers such as DEC VAXStill dumb terminalsOracle vs IMS/Adabas/DB2
- 7. 1992: Client server revolution IBM PC allows for off loading of some processing to the clientRicher Character mode interfacesFirst graphical interfaces: Windows 3.0Oracle vs Sybase/Ingres/dBase III
- 8. 1999: Internet/Y2K gold rushMassive IT budgetsScalability at all costsJava3-tier applicationsOracle unchallenged
- 9. 2005: After the gold rushTCO and ROI
- 11. SQL Server gains share
- 12. Oracle responds with XE (low end), automation (TCO) and RAC (high end)2009: Big Data and Clouds Volumes of data strain commercial RDBMS Cloud computing mania
- 13. Why worry?Dominant players often fail quicklyBeing on the wrong side of a paradigm shift hurtsTheory of disruptive innovation helps explain rapid shifts
- 14. Functionality demanded at high end of market FunctionalitySustainingInnovationFunctionality demanded at low end of market DisruptiveInnovationTimeDisruptive Innovation Oracle RACOracle10gOracle9iOracleXEThe Innovators Dilemma, Clayton Christensen, Harvard University Press
- 15. Larry, Richard and the cloud the provision of virtualized application software, platforms or infrastructure across the network, in particular the internet. Larry Ellison (Sep 08):“we’ve redefined cloud computing to include everything that we already do … It’s complete gibberish. It’s insane. When is this idiocy going to stop?:Richard Stallman (Oct 08):"It's worse than stupidity: it's a marketing hype campaign." Larry Ellison (Sep 09):“It’s this nonsense ... Water vapour”
- 16. Cloud Ingredients and recipes Utility Computing AKA Private CloudClusteringSingle workload across multiple hostSaaSSoftware as a ServiceSalesforce.comGmail InternetCloudComputingVirtualizationMultiple workloads onSingle hostIaaSInfrastructure as a ServiceAmazon Web ServicesJoyentGrid managementAllocate resources on demandPaaSPlatform as a ServiceGoogle App EngineAzure
- 17. Elastic provisioningCapacity / DemandDemandHardware upgradeUnder provisionedCapacityOver provisionedTime
- 18. Big DataThe Industrial Revolution of data* User generated data:Twitter, Facebook, Amazon Machine generated data:RFID, POS, cell phones, GPSTraditional RDBMS neither economic or capable* http://radar.oreilly.com/2008/11/the-commoditization-of-massive.html
- 19. Big data 1: Google
- 21. Hadoop: Open source Map-reduce Yahoo! Hadoop cluster: 4000 nodes 16PB disk 64 TB of RAM 32,000 Cores
- 22. Big Data 2: Twitter (and Web 2.0)
- 23. The fail whale
- 24. Twitter 2009
- 25. Memcached and ShardingWeb ServersMemcached serversDatabase ServersMasterSlaveSlave
- 27. CAP Theorem: You can’t have it allEventual consistency:“when no updates occur for a long period of time, eventually all updates will propagate through the system and all the replicas will be consistent.”Availability (Total redundancy)Consistency: ACID transactionsRACNo GONoSQL DBPartition Tolerance: Infinite scaleout
- 29. CouchDb
- 30. MongoDb
- 31. Scalaris
- 32. Persevere
- 33. Key Value:
- 34. MemcacheDb
- 35. Voldemort
- 36. Tokyo Cabinet
- 37. Dynamo/Dynamite
- 38. RedisColumn oriented:BigTableHyperTableHbaseSimpleDbAzure Table ServicesCassandra
- 39. Big Data 3: Data Warehousing
- 40. Data warehousing and Oracle
- 42. Column Databases (Vertica)Data is stored together in columnsVery fast answers to analytic aggregate queriesBetter compressionNot write optimized
- 43. Oracle EXADATARAC clusters provide MPPDedicated storage serversHigh Speed infiniband channels Smart storage reduces data transfer requirements
- 44. Big Data vs. Fast Data
- 45. Economics of SSD
- 46. Hierarchical storage management $/GB$/IOP
- 47. Oracle 2009 innovationsSun Oracle database machineExadata flash cacheDatabase flash cache (coming soon)Hybrid Columnar compression
- 48. Not worrying, just wondering...How will Oracle deal respond to Hadoop?Will Oracle play in the NoSQL database world?What will happen to MySQL?What will happen to red-shirt TOAD?
Editor's Notes
- #3: Apologies, I’m a database type.....Quest is best known for toad, but we also have enterprise monitoring across all levels of the stackIn Melbourne, SQL Navigator + the spotlights. It’s not a complete co-incidence about the star trek theme.
- #4: I’m worried about the Toad in the red shirt – we all know that red-shirt crewmen die in Star Trek!
- #6: I know what you’re thinking ... Why the big glassesI wondered to, until I found this picture... I was subliminally role-modeling on BillLarry, of course, doesn’t have the same geeky look.... Or does he. Recently discovered high school photo’s suggest he may have been a big-glassed nerd as well.So we might call 1987 the year of the big glassed geek
- #7: In 1987 I was working for the Australian Govt. We were moving from centralized mainframe in canberra to minicomputers in each state office (VAX VMS and oracle)
- #16: That’s a predictable linear growth curve. Gets much worse for unpredictable or cyclic demand So I think it’s real, and it excites me because it represents the realization of a more industrialized model for providing computing resources. In the early days of electricity everybody had thier own power sources and every company needed engineers as a result. Nowdays, few companies need that...
- #28: Data warehouses doubling every three years.
- #36: Mention columnar compression Mention flash cache in the database In memory ParalelismMPP styles similar to whats happening in M-R
- #37: So while I worry about the red-shirt TOAD, I’m not really worried about Oracle. Oracle remains a highly technically innovative company as well as a skilled in the business of software. I’ve certainly got no regrets specializing in Oracle technology all those years ago. Quest is a fairly diversified company and has no vested interest in Oracle per see. We aim to be a strategic partner across all of your technologies: Oracle, Microsoft, Vmware and in emerging technologies.