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WHAT THE MARKET-LEADING DBMS VENDORS DON’T WANT YOU TO KNOW Disruption is gathering steam PowerPoint Presentation
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WHAT THE MARKET-LEADING DBMS VENDORS DON’T WANT YOU TO KNOW Disruption is gathering steam. Curt Monash. Analyst since 1981 Covered DBMS since the pre-relational days Also analytics, search, etc. Own firm since 1987 Publicly available research

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