TDWI Blog: Data 360

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Analytic Database Management Systems

By Philip Russom, TDWI Research Director

[NOTE: The following article was published in the TDWI Trip Report of May 2012.]

The Technology Survey that TDWI circulated at the recent World Conference in Chicago asked attendees to answer a few questions about analytic database management systems and how these fit into their overall data warehouse architecture. Here’s some background information about analytic databases, plus a sampling of attendees’ responses to the survey:

A “database management system” (DBMS) is a vendor-built enterprise-class software package designed to manage databases, whereas a “database” is a collection of data managed by a DBMS. Hence, an “analytic DBMS” (ADBMS) is a vendor-built DBMS designed specifically for managing data for analytics. ADBMSs are most often optimized for “Extreme SQL,” which involves complex queries that scan terabytes of data or routines that may include thousands of lines of SQL. SQL aside, some ADBMSs support other in-database analytic processing, such as MapReduce, no-SQL parsing methods, and a variety of user-defined functions for data mining, statistical analysis, natural language processing (NLP), and so on. Some vendors package or market their ADBMSs as data warehouse appliances, columnar DBMSs, analytic accelerators, in-memory DBMSs, and cloud/SaaS-based platforms.

Half of organizations surveyed (52%) have no ADBMS.
There are good reasons why some organizations don’t feel the need for a specialized analytic DBMS. (See Figure 1.) Many organizations stick close to reporting, OLAP, and performance management, for which the average enterprise data warehouse (EDW) is more than capable. Others simply haven’t matured into the use of advanced analytics, for which most ADBMSs are designed. Still others have a powerful EDW platform that can handle all data warehouse workloads, including those for advanced analytics. Among the half of respondents that do have one or more ADBMSs, most have between one and five; multiple ADBMSs can result when multiple analytic methods are in use, due to diverse business requirements for analytics. Also, analytics tends to be departmental by nature, so ADBMSs are commonly funded via departmental budgets; and multiple departments investing in analytics leads to multiple ADBMSs.

FIGURE 1. Based on 75 respondents. Approximately how many standalone ADBMS platforms has your organization deployed?
52% = Zero
37% = One to five 
  8% = Six to ten
  3% = More than ten

Half of organizations surveyed (46%) run analytic workloads on their EDW.
The EDW as a single monolithic architecture is still quite common, despite the increasing diversity of data warehouse workloads for analytics, real-time, unstructured data, and detailed source data. (See Figure 2.) Even so, a third of respondents (34%) offload diverse workloads to standalone DBMSs (often an ADBMS), typically to get workload-specific optimization or to avoid degrading the performance of the EDW. If you compare Figures 1 and 2, you see that half of respondents don’t have an ADBMS (Figure 1) because they run analytic workloads on their EDW (Figure 2).

FIGURE 2. Based on 74 respondents. Which of the following best characterizes how data warehouse workloads are distributed in your organization?
46% = One monolithic EDW that supports all workloads in a single DBMS instance
34% = One EDW, plus multiple, standalone DBMSs for secondary workloads
20% = Other

Most respondents consider an ADBMS to be a useful complement to an EDW.
Even some users who don’t have an ADBMS feel this way. (See Figure 3.) According to survey results, an ADBMS provides analytic and data management capabilities that complement an EDW (56%), enables the “analytic sandboxes” that many users need (57%), and optimizes more analytic workloads than the average EDW (58%).

FIGURE 3. Based on 219 responses from 72 respondents. What are the potential benefits of complementing an EDW with an ADBMS? (Select all that apply.)
58% = Optimized for more analytic workloads than our EDW
57% = Enables the “analytic sandboxes” that many users need
56% = Provides analytic and data mgt capabilities that complement our EDW
46% = Isolates ad hoc analytic work that might degrade EDW performance
33% = Manages multi-Tb raw source data for analytics better than EDW
29% = Handles real-time data feeds for analytics better than EDW
22% = Takes analytic processing to Big Data, instead of reverse
  3% = Other

Posted by Philip Russom, Ph.D. on June 8, 2012


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