Types of OLAP Systems
What Are The Types of OLAP Systems?
OLAP systems vary quite a lot, and they have generally been distinguished by a letter tagged onto the front of the acronym “OLAP,” for On-Line Analytical Processing. MOLAP and ROLAP have classically been the most established types, and the other distinctions represent little more than the marketing programs on the part of the vendors to distinguish themselves, for example, SOLAP and DOLAP. Here, we aim to give you an idea of what these distinctions have meant.
The New Direction in OLAP Technology:
The newest software in the OLAP and Business Intelligence world combines, in real-time, the benefits of both relational tables and multidimensional business data modeling. The latest technology removes the proprietary format of its MOLAP predecessors by living/saving in source relational tables, like SQL Server. Lastly, new OLAP technology maintains a constant connection with existing back-end systems and delivers immediately responsive reports/analytics in Excel and other front-end tools (dashboards, query tools, etc.)
If you like the sound of that, check out Olation® from PARIS Tech, the sponsor of OLAP.com
Major OLAP Technology Types:
Hybrid Transaction / Analytical Processing (HTAP)
Gartner coined the term HTAP in a paper in the beginning of 2014 to describe new in-memory data systems that do both online transaction processing (OLTP) and online analytical processing (OLAP).
HTAP relies on newer and much more powerful, often distributed, processing: sometimes it involves a new hardware “appliance”, and it almost always requires a new software platform. Beyond this, the key point seems to be that all the technology is sited in the relational database. And so, there’s no more data replication, and new transactional information becomes part of an analytical model in as fast a time as is technologically possible.
HTAP represents a new way to tie data together in a way that hasn’t been possible before– a real uniting of relational data stored in tables with the data models that are used for decision making by the business leaders.
For an example of an HTAP product, check out Olation® from PARIS Tech, the sponsor of OLAP.com. Olation can be categorized as an HTAP product — even the name Olation implies the combination of “OLAP” and “relational” technologies.
Multidimensional OLAP (MOLAP) – Cube based
MOLAP products enable end-users to model data in a multidimensional environment, rather than providing a multidimensional view of relational data, as ROLAP products do (see next tab).
The structure of a multidimensional model is not a series of tables (as exists in a relational database) but what is generally referred to as a cube. Cubes modeled in a multidimensional database extend the concept associated with spreadsheets: just as a cell in a spreadsheet represents the intersection of two dimensions (sales of product by region), a cell in a cube represents the intersection of an infinite number of dimension members (e.g., Products, Customers, Regions, Months …nth dimension). As in a spreadsheet, a cell might be calculated by formulas involving other cells.
In short, multidimensional databases allow users to add extra dimensions, rather than additional tables, as in a relational model. And the MOLAP cube structure allows for particularly fast, flexible data-modeling and calculations. For one, locating cells is vastly simplified—an application can identify a cell location by name (at the intersection of dimension members) rather than by searching an index or the entire model (via SQL SELECT statements), as in a relational database. Further, multidimensional models incorporate advanced array-processing techniques and algorithms for managing data and calculations. As a result, multidimensional databases can store data very efficiently and process calculations in a fraction of the time required of relational-based products.
What are the perceived drawbacks of MOLAP tools?
For one, relevant data must be transferred from relational systems,which is aa potentially “redundant” re-creation of data in another (multidimensional) database. Once data has been transferred, there may be no simple means for updating the MOLAP “engine” as individual transactions are recorded by the RDBMS. Also, MOLAP products are typically proprietary systems. For some IT departments, introducing a new database system is an anathema, even if it means significantly greater productivity for the type of planning, analysis and reporting that end-users rely on the (MOLAP) solution to perform.
For a good example of a fast, scalable MOLAP product, check out PowerOLAP® from PARIS Tech, the sponsor of OLAP.com.
Relational OLAP (ROLAP) –Star Schema based
ROLAP products (for Relational OLAP) are credited with being able to directly access data stored in relational databases. The notion is that they can readily retrieve transactional data, although this becomes suspect when very large data sets are in play, or if more complex calculations are to be delivered, based on the transactional data. ROLAP products enable organizations to leverage their existing investments in RDBMS (relational database management system) software.
ROLAP products access a relational database by using SQL (structured query language), which is the standard language that is used to define and manipulate data in an RDBMS. Subsequent processing may occur in the RDBMS or within a mid-tier server, which accepts requests from clients, translates them into SQL statements, and passes them on to the RDBMS.
ROLAP products provide GUIs and generate SQL execution plans that typically remove end-users from the SQL writing process. However, this over-reliance on processing via SQL statements—including processing for multidimensional analysis—is a drawback. Whether it is generated “transparently” or not, SQL is the language of relational tables: SQL’s vocabulary is limited and its grammar often inflexible, at least to accommodate the most sophisticated modeling required for multidimensional analyses.
There are further drawbacks to structuring a multidimensional model solely within relational tables: Before end-users can submit requests, the relevant dimension data must be extracted and reformatted in de-normalized structures known as star schema or snowflakes (so-called because of the way the tables are conjoined). These tabular structures are necessary to provide acceptable analytical performance. Sophisticated ROLAP applications also require that aggregate tables be pre-built and maintained, eliminating the need to process summary data at runtime
One advantage of ROLAP over the other styles of OLAP analytic tools is that it is deemed to be more scalable in handling huge amounts of data. ROLAP sits on top of relational databases therefore enabling it to leverage several functionalities that a relational database is capable of.
Hybrid OLAP (HOLAP)
HOLAP is the product of the attempt to incorporate the best features of MOLAP and ROLAP into a single architecture. This kind of tool tries to bridge the technology gap of both products by enabling access to or use of both multidimensional database (MDDB) and Relational Database Management System (RDBMS) data stores. HOLAP systems store larger quantities of detailed data in the relational tables while the aggregations are stored in the pre-calculated cubes. HOLAP also has the capacity to “drill through” from the cube down to the relational tables for delineated data.Some of the advantages of this system are better scalability, quick data processing and flexibility in accessing of data sources. The issue with HOLAP systems lies precisely in the fact that they are hybrids: at best they partake of the strengths of other systems…but they also evince the weaknesses of each, in an attempted mashup of two distinct technologies.
There are also less popular kinds of OLAP technology that one might encounter every so often, listed below. Some of these self-designated product types do not really exist any longer. (An example is WOLAP, since nearly all products provide a Web interface, to meet market demand.) But they are included, to help provide a backgrounder in how vendors have tried to set themselves apart, and also how the OLAP market developed over time.
Desktop OLAP (DOLAP)
Desktop OLAP, or “DOLAP,” is based on the idea that user can download a section of an OLAP model from another source, and work with that dataset locally, on their desktop. DOLAP is purportedly easier to deploy, with a potential lower cost, but almost by definition comes with a limited functionality in comparison with other OLAP applications.
Web OLAP (WOLAP)
Simply put, a WOLAP signifies a Web browser – based OLAP technology. And it suggests a technology that is Web-based only, without any kind of option for a local install or local client to access data. The most appealing features of this style of OLAP was (past tense intended, since few products categorize themselves this way) the considerably lower investment involved on the client side (“all that’s needed is a browser”) and enhanced accessibility to connect to the data. The fact is that by now most OLAP products provide an option for Web-only connectivity, while still allowing other client options for more robust data modeling and other functionality than a Web client can provide.
Mobile OLAP is merely refers to OLAP functionalities on a wireless or mobile device. This enables users to access and work on OLAP data and applications remotely thorough the use of their mobile devices.
Spatial OLAP (SOLAP)
The aim of Spatial OLAP (thus, SOLAP) is to integrate the capabilities of both Geographic Information Systems (GIS) and OLAP into a unified solution, thus facilitating the management of both spatial and non-spatial data. The driving idea is to provide quick exploration of data that can point to trends and analysis in a geographic context, whether place-names sourced from a GIS or overlaying maps that show, for example, customer purchase behavior.
More Articles on OLAP & Business Intelligence
Human Resources is beginning to pull ahead in the Business Analytics world. One thing is certain, if you want to run a successful business, you need to hire the right people to help you get there. Who is in charge of recruiting and hiring your team members?...
Be Mindful to Keep these Practices throughout your Business Intelligence Project Getting Business Intelligence Best Practices to work well is challenging. Check out the 6 best practices outlined in this Infographic. Check it out and see how your organization stacks...
Who doesn’t want the largest return on their investment? The article written by Chris Twogood, How To Produce Ongoing Returns With Big Data, compares current Data and Analytics systems to what he claims is actually needed in today’s businesses. He...
Dennis McCafferty of CIO Insight recently wrote an article that addresses 11 of the top practices of Business Intelligence. With Business Intelligence controlling such key factors in today’s companies such as, analytics, business performance management, text...
Let’s take a look into some reasons that business intelligence projects tend to fail....
James Haight of Blue Hill Research recently wrote a blog post that breaks down the costs and numbers of Data Preperation. Typical reports focus on hours and efficiency. As stated by James Haight, “At Blue Hill, we recently published a benchmark report in which...