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<ul><li>WHAT THE MARKET-LEADING DBMS VENDORS DON’T WANT YOU TO KNOW </li></ul><ul><li>Disruption is gathering steam </li><...
Curt Monash <ul><li>Analyst since 1981 </li></ul><ul><ul><li>Covered DBMS since the pre-relational days </li></ul></ul><ul...
Database diversity <ul><li>Mike Stonebraker, PhD </li></ul><ul><ul><li>“ One size doesn’t fit all” </li></ul></ul><ul><li>...
The case for grand integrated DBMS <ul><li>Theoretical relational model has great advantages </li></ul><ul><li>Actual rela...
The case for database diversity <ul><li>Different kinds of data require  fundamentally  different kinds of data management...
Application and use cases <ul><li>High-end e-commerce </li></ul><ul><li>100-terabyte analytics </li></ul><ul><li>High-volu...
Data management distinctions <ul><li>Fundamental </li></ul><ul><ul><li>Data manipulation language </li></ul></ul><ul><ul><...
Very practical <ul><li>$ </li></ul>
Major components of DBMS cost <ul><li>License and maintenance </li></ul><ul><ul><li>Especially maintenance </li></ul></ul>...
11 kinds of data management software <ul><li>High-end OLTP/general-purpose DBMS </li></ul><ul><li>Mid-range OLTP/general-p...
High-end OLTP/general-purpose DBMS <ul><li>Oracle, DB2, MS SQL Server, et al. </li></ul><ul><li>Amazing throughput and sca...
Mid-range OLTP/general-purpose DBMS <ul><li>Three main groups </li></ul><ul><ul><li>Crippled high-end (“Express” editions)...
Row-based analytic RDBMS <ul><li>Data warehouses should be in separate instances </li></ul><ul><ul><li>But that’s not enou...
Column- or array-based analytic RDBMS <ul><li>Retrieving whole rows carries penalties </li></ul><ul><ul><li>I/O  </li></ul...
Text search engines <ul><li>“ 85% of all information is in text” … </li></ul><ul><ul><li>…  and 16.9% of all statistics ar...
XML and OO DBMS <ul><li>Reasons for logical XML structures </li></ul><ul><ul><li>Schema flexibility </li></ul></ul><ul><ul...
RDF and other graphical DBMS <ul><li>“ Semantic web” is overhyped … </li></ul><ul><li>…  but the world DOES need ontology ...
Event/stream processing engines <ul><li>Design point = super-low latency … </li></ul><ul><ul><li>…  but there are other ap...
Embedded DBMS for devices <ul><li>Products </li></ul><ul><ul><li>Sybase SQL Anywhere </li></ul></ul><ul><ul><li>solidDB – ...
Matching analytic DBMS to use cases <ul><li>100 Tb data mart </li></ul><ul><li>50 Tb enterprise data warehouse </li></ul><...
Matching OLTP/general DBMS to use cases <ul><li>Market leader </li></ul><ul><ul><li>High-end e-commerce </li></ul></ul><ul...
Clayton Christensen’s “disruption” narrative <ul><li>Market leaders have many advantages, including top technology. </li><...
That’s what’s happening here <ul><li>Much DBMS complexity is without benefit </li></ul><ul><li>Other complexity only benef...
And the big vendors know it <ul><li>Oracle is diversifying furiously </li></ul><ul><li>Oracle has announced a clear focus ...
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  • Hello, and welcome to what is now my third webcast sponsored by EnterpriseDB. To set your expectations – the views here are my own. The subjects emphasized are those I think may be of most interest to EnterpriseDB’s target audience – and that includes a broader perspective than just the narrow question of which database management system to use for your next new application. This webcast is part of a whole ongoing program of free research – sponsored webcasts, sponsored white papers, and blogs that aren’t sponsored by anybody at all. I encourage you to sign up for our feed, or just visit monash.com and DBMS2.com for a whole lot more commentary on database-related subjects. I’ve been a DBMS analyst for a very long time. When I started, the largest DBMS vendor – IBM excepted – was under $50 million in revenue, and Oracle was under $5 (million).
  • IOU that I’ll get to these at the end
  • The first 9 are relevant to many enterprises and web-based businesses. The other 2 are more acquired tastes.
  • Yeah, every IBM deal and partnership inquiry can be explained away – but there sure are a lot of them
  • Transcript of "enterprisedb-april-2008-powerpoint-final.ppt"

    1. 1. <ul><li>WHAT THE MARKET-LEADING DBMS VENDORS DON’T WANT YOU TO KNOW </li></ul><ul><li>Disruption is gathering steam </li></ul>
    2. 2. Curt Monash <ul><li>Analyst since 1981 </li></ul><ul><ul><li>Covered DBMS since the pre-relational days </li></ul></ul><ul><ul><li>Also analytics, search, etc. </li></ul></ul><ul><li>Own firm since 1987 </li></ul><ul><li>Publicly available research </li></ul><ul><ul><li>Blogs, including DBMS2 ( www.dbms2.com -- the source for most of this talk) </li></ul></ul><ul><ul><li>Feed at www.monash.com/blogs.html </li></ul></ul><ul><ul><li>White papers and more at www.monash.com </li></ul></ul>
    3. 3. Database diversity <ul><li>Mike Stonebraker, PhD </li></ul><ul><ul><li>“ One size doesn’t fit all” </li></ul></ul><ul><li>Curt Monash, PhD </li></ul><ul><ul><li>“ Horses for courses” </li></ul></ul><ul><ul><li>“ Database diversity” </li></ul></ul><ul><li>Mike and Curt </li></ul><ul><ul><li>The world needs 9 to 11 different kinds of data management software </li></ul></ul>
    4. 4. The case for grand integrated DBMS <ul><li>Theoretical relational model has great advantages </li></ul><ul><li>Actual relational DBMS are versatile and modular </li></ul><ul><li>Software developers have economies of scale </li></ul><ul><li>Vendor consolidation theoretically saves effort and money </li></ul><ul><li>So does database consolidation </li></ul>
    5. 5. The case for database diversity <ul><li>Different kinds of data require fundamentally different kinds of data management software </li></ul><ul><li>Putting all that together in one system is extremely hard </li></ul><ul><li>Nobody has ever done it well </li></ul>
    6. 6. Application and use cases <ul><li>High-end e-commerce </li></ul><ul><li>100-terabyte analytics </li></ul><ul><li>High-volume call center </li></ul><ul><li>Media-heavy web startup </li></ul><ul><li>Simple departmental application </li></ul><ul><li>General enterprise or SaaS app </li></ul><ul><ul><li>End-user or ISV </li></ul></ul>
    7. 7. Data management distinctions <ul><li>Fundamental </li></ul><ul><ul><li>Data manipulation language </li></ul></ul><ul><ul><li>Data access method </li></ul></ul><ul><li>Practical </li></ul><ul><ul><li>Type of data </li></ul></ul><ul><ul><li>Type of hardware </li></ul></ul><ul><ul><li>Administrative burden </li></ul></ul><ul><ul><li>Performance stresses and metrics </li></ul></ul>
    8. 8. Very practical <ul><li>$ </li></ul>
    9. 9. Major components of DBMS cost <ul><li>License and maintenance </li></ul><ul><ul><li>Especially maintenance </li></ul></ul><ul><li>Hardware, power, facilities </li></ul><ul><ul><li>Mainly for VLDB analytics </li></ul></ul><ul><li>Installation and ongoing administration </li></ul><ul><ul><li>Time-to-benefit is a factor too </li></ul></ul><ul><li>Programming </li></ul><ul><ul><li>Sometimes a differentiator </li></ul></ul>
    10. 10. 11 kinds of data management software <ul><li>High-end OLTP/general-purpose DBMS </li></ul><ul><li>Mid-range OLTP/general-purpose DBMS </li></ul><ul><li>Row-based analytic RDBMS </li></ul><ul><li>Column- or array-based analytic RDBMS </li></ul><ul><li>Text search engines </li></ul><ul><li>XML and OO DBMS (but these may merge with search) </li></ul><ul><li>RDF and other graphical DBMS (but these may merge with relational) </li></ul><ul><li>Event/stream processing engines (aka CEP) </li></ul><ul><li>Embedded DBMS for devices </li></ul><ul><li>Sub-DBMS file managers (e.g. MapReduce/Hadoop) </li></ul><ul><li>Science DBMS </li></ul>
    11. 11. High-end OLTP/general-purpose DBMS <ul><li>Oracle, DB2, MS SQL Server, et al. </li></ul><ul><li>Amazing throughput and scale-up </li></ul><ul><li>Bullet-proofing </li></ul><ul><ul><li>24/7 </li></ul></ul><ul><ul><li>Security certifications </li></ul></ul><ul><li>Datatype extensibility </li></ul><ul><li>Expensive, expensive, expensive </li></ul>
    12. 12. Mid-range OLTP/general-purpose DBMS <ul><li>Three main groups </li></ul><ul><ul><li>Crippled high-end (“Express” editions) </li></ul></ul><ul><ul><li>ISV/VAR-focused (Progress, several non-relational) </li></ul></ul><ul><ul><li>Open source-based (Postgres, MySQL) </li></ul></ul><ul><li>Some are comparable to (or better than) the systems that ran the world in the 1990s </li></ul><ul><ul><li>What does the Postgres family still lack? </li></ul></ul><ul><li>Generally inexpensive </li></ul>
    13. 13. Row-based analytic RDBMS <ul><li>Data warehouses should be in separate instances </li></ul><ul><ul><li>But that’s not enough </li></ul></ul><ul><li>Sequential vs. random reads </li></ul><ul><li>MPP vs. SMP </li></ul><ul><li>Teradata, Netezza, DATAllegro </li></ul>
    14. 14. Column- or array-based analytic RDBMS <ul><li>Retrieving whole rows carries penalties </li></ul><ul><ul><li>I/O </li></ul></ul><ul><ul><li>Optimization </li></ul></ul><ul><li>Columnar is better </li></ul><ul><ul><li>But not in all use cases </li></ul></ul><ul><li>MOLAP may be superceded </li></ul>
    15. 15. Text search engines <ul><li>“ 85% of all information is in text” … </li></ul><ul><ul><li>… and 16.9% of all statistics are made up out of thin air </li></ul></ul><ul><li>There really are a lot of words out there </li></ul><ul><ul><li>And search interfaces are hugely important </li></ul></ul><ul><li>Text search has its own data access methods </li></ul><ul><ul><li>May play more nicely with columnar than row-based RDBMS </li></ul></ul><ul><li>Watch integrations with other analytic datatypes </li></ul><ul><ul><li>Attivio (relational, a little XML) </li></ul></ul><ul><ul><li>Mark Logic (a lot of XML) </li></ul></ul>
    16. 16. XML and OO DBMS <ul><li>Reasons for logical XML structures </li></ul><ul><ul><li>Schema flexibility </li></ul></ul><ul><ul><li>Dressed-up text </li></ul></ul><ul><ul><li>XML is the transport format, and it’s too complex to unpack </li></ul></ul><ul><ul><li>The data came from neither an RDMS nor text store in the first place </li></ul></ul><ul><li>Native XML data access methods </li></ul><ul><ul><li>Like text and object </li></ul></ul><ul><li>So far mainly in niches </li></ul>
    17. 17. RDF and other graphical DBMS <ul><li>“ Semantic web” is overhyped … </li></ul><ul><li>… but the world DOES need ontology management systems </li></ul><ul><li>Much depends on path length </li></ul><ul><li>Analytic RDBMS may do the job </li></ul>
    18. 18. Event/stream processing engines <ul><li>Design point = super-low latency … </li></ul><ul><ul><li>… but there are other applications </li></ul></ul><ul><li>Data is “executed against” queries rather than vice versa </li></ul><ul><li>Could be the future of BI … </li></ul><ul><ul><li>… and of social networking </li></ul></ul>
    19. 19. Embedded DBMS for devices <ul><li>Products </li></ul><ul><ul><li>Sybase SQL Anywhere </li></ul></ul><ul><ul><li>solidDB – focused on caching post-acquisition? </li></ul></ul><ul><ul><li>Cloudscape – vaporized? </li></ul></ul><ul><ul><li>McObject – tiny startup </li></ul></ul><ul><li>Features </li></ul><ul><ul><li>Load-and-forget </li></ul></ul><ul><ul><ul><li>Zero-DBA </li></ul></ul></ul><ul><ul><li>Small-footprint </li></ul></ul><ul><ul><ul><li>Sometimes -- subsettable library </li></ul></ul></ul>
    20. 20. Matching analytic DBMS to use cases <ul><li>100 Tb data mart </li></ul><ul><li>50 Tb enterprise data warehouse </li></ul><ul><li>5 Gb – 5 Tb OLTP offload </li></ul>
    21. 21. Matching OLTP/general DBMS to use cases <ul><li>Market leader </li></ul><ul><ul><li>High-end e-commerce </li></ul></ul><ul><ul><li>High-volume call center </li></ul></ul><ul><li>Mid-range </li></ul><ul><ul><li>Web startup </li></ul></ul><ul><li>It depends on how locked-in you are </li></ul><ul><ul><li>Simple departmental application </li></ul></ul><ul><ul><li>General enterprise or SaaS app </li></ul></ul>
    22. 22. Clayton Christensen’s “disruption” narrative <ul><li>Market leaders have many advantages, including top technology. </li></ul><ul><li>Followers come up with good technology too. </li></ul><ul><li>The leaders stay ahead by making their products ever better and more complex. </li></ul><ul><li>The followers sell into new or non-mainstream markets, at prices the leaders can’t match. So they dominate new markets. </li></ul><ul><li>Old markets turn into low-margin commodity-fests. </li></ul><ul><li>Unless they diversify, old leaders are doomed . </li></ul>
    23. 23. That’s what’s happening here <ul><li>Much DBMS complexity is without benefit </li></ul><ul><li>Other complexity only benefits a few high-end customers </li></ul><ul><li>Data warehouse specialists exploit radically superior technology (e.g., MPP) </li></ul><ul><li>Open source vendors have radically different price points and business models </li></ul><ul><li>Open source adoption has been strongest in non-traditional markets. </li></ul>
    24. 24. And the big vendors know it <ul><li>Oracle is diversifying furiously </li></ul><ul><li>Oracle has announced a clear focus on top-end customers </li></ul><ul><li>IBM is obviously focused on the high end too </li></ul><ul><li>Oracle and (to some extent) IBM are buying alternative DBMS technologies </li></ul><ul><li>Microsoft and IBM aren’t dependent on the DBMS business anyway </li></ul>
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