An analysis of the advantages and disadvantages of relational, columnar and correlation databases for complex and demanding analytics data warehousing environments.
Although relational databases (RDBMS) are the most common choice for data warehouse implementations, their record-based structure is far from ideal. As data volumes grow and users demand more sophisticated analytical data warehousing capabilities, the deficiencies of the RDBMS to data storage become more conspicuous. RDBMS data warehousing systems are difficult to design; extremely inefficient in their use of disk space and I/O; challenging to maintain; and, worst of all, require designers to compromise between optimizing query performance and maximizing query flexibility.
In response to these shortcomings, alternative database architectures—columnar and correlation—have been developed. Columnar databases use less disk space and are more efficient in their I/O demands than records-based data warehouses but force their own compromise between optimizing for new record insertion versus data selection and retrieval.
A radically new data warehousing platform, the correlation DBMS (CDBMS), eliminates all such design tradeoffs. There is virtually no upfront design effort required. The CDBMS automatically designs and builds a data warehouse using a data-generated schema and indexes 100% of data values during the data loading process, which optimizes for both performance and query flexibility. A CDBMS minimizes disk storage and I/O requirements, makes new data ready for use as soon as the load process is complete, and enables creation and execution of unique types of queries not supported by either RDBMSs or columnar databases.
This white paper provides a data warehousing architecture comparison, comparing the data storage models, hardware demands, performance levels, design considerations and analytical flexibility of all three data structures in an analytics environment.
To learn how a CDBMS provides a data warehousing structure that is superior to relational or columnar database models for complex and demanding analytics environments, please fill out the form below.
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