How the value-based storage model in the Correlation DBMS overcomes the limitations of SQL to provide
unrestricted flexibility for data analysis and exploration.
The technologies underlying information
management infrastructures today—relational database
management systems (RDBMSs) and structured query language
(SQL)—are very efficient at answering standard business
queries. However, the limitations of SQL and RDBMS technology make them manifestly inadequate for answering
more interesting and insightful questions that can lead to
innovative strategic and tactical decisions, or rich data analysis
of any data set.
Data warehouses contain much more valuable
information that could be used to perform such data analysis—if
they could be accessed effectively. The fundamental
shortcomings of RDBMS and SQL technology are:
They enforce a rigid
structure on the types of relationships that can be explored.
Any question that lies outside the current index structure of
a dataset requires an IT project to re-architect the indexing,
schema or data cube.
They also enforce a
rigid structure on how questions can be asked. People can't ask
the database questions in natural human language; they have to
understand the relationships between columns and tables so
that complex SQL queries can be written to provide the answers.
They are unable to
accommodate certain types of questions at all, such as incremental
queries (asking a question, then asking follow-up questions
based on the answer to the first query) or associative
queries (finding all of the information known about a person,
place, product or other element in a data set).
A radically new type of database structure—the correlation database management system (CDBMS)—is
free of the limitations of SQL and ideally structured for explorational queries that can unlock the
true value contained in organizational databases and enable informed,
data-driven decision-making.
By automatically and completely indexing all
information while loading, the CDBMS value-based storage (VBS)
structure not only significantly reduces the time-to-analytics,
it eliminates the trade-off between query speed and flexibility.
We invite you to learn how a CDBMS
enables new types of queries—not possible with SQL—that
reflect natural human thought processes.
I'd like a copy of this white paper: "When Standard SQL Queries Can't Get the Job Done"
