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OLTP

OLTP is, in computing, an acronym for on-line transaction processing.

This is a class of program that facilitates and manages transaction-oriented applications, typically for data entry and retrieval in a number of industries, including banking, airlines, mailorder, supermarkets, and manufacturing. Probably the most widely installed OLTP product is IBM's CICS (Customer Information Control System).

Today's online transaction processing increasingly requires support for transactions that span a network and may include more than one company. For this reason, new OLTP software uses client/server processing and brokering software that allows transactions to run on different computer platforms in a network.

Key features of OLAP

• Multi-dimensional views of data
• Intuitive navigation
• Complex analytical functions
• Very high performance
• Consistency

OLAP Operations

• Cube slicing ? come up with 2-D view of data
• Dicing ? data is scoped down to a subset grouping from the original multi-dimensional cube
• Drill-up ? moving up the dimensional hierarchies to higher levels of aggregation
• Drill-down ? moving down to lower levels of details

Drilling Up and Drilling Down

Drilling refers to the investigation of data to greater or lesser detail from a starting point. Typically, in an analytical environment you would start with less detail, at a higher level within a hierarchy, and investigate down through a hierarchy to greater detail. This process is drilling down (to more detailed data). Drilling down means retrieving data at a greater level of detail. Using the market hierarchy as an example, you might start your analysis at the group level and drill down to the region or store level of detail. Drilling up is simply the reverse of that process. Consider the market hierarchy example. If your starting point were an analysis of districts, drilling up would mean looking at a lesser level of detail, such as the region or group level.

Drilling can occur in three ways: Through a single hierarchy From a single hierarchy to nonhierarchical attributes From a single hierarchy to another hierarchy

SLICE

A slice is a subset of a multi-dimensional array corresponding to a single value for one or more members of the dimensions not in the subset. For example, if the member Actuals is selected from the Scenario dimension, then the sub-cube of all the remaining dimensions is the slice that is specified. The data omitted from this slice would be any data associated with the non-selected members of the Scenario dimension, for example Budget, Variance, Forecast, etc. From an end user perspective, the term slice most often refers to a two- dimensional page selected from the cube. SLICE AND DICE The user-initiated process of navigating by calling for page displays interactively, through the specification of slices via rotations and drill down/up

OLAP Product Architecture

Multidimensional OLAP (MOLAP) The traditional approach to OLAP Multi-dimensional cube is physically represented on disk

Relational OLAP (ROLAP) A new approach to OLAP ROLAP tools create virtual multi-dimensional cubes ? data is not stored dimensionally, but in relational tables Capitalizes on existing investments in RDBMS technology

Hybrid OLAP (HOLAP)

Building OLAP Application Phases

I. Design and Create the dimensional model (Star schema). In this phase, the business model is transformed into a dimensional model. Warehouse schema tables and table elements are defined, relationships between schema tables are established, and sources for warehouse data elements are recorded. II. Creating the physical model. In this phase, the dimensional model is transformed into a physical model. This includes the documentation of data element formats, database size planning, and the establishment of partitioning strategies, indexing strategies, and storage strategies. III. In this phase you use tools to extract, transform and load data from all kinds of data sources to your multidimensional database. You data source could be from an operational database, or a spreadsheet, a text file and so on. As the data loaded to your data warehouse / data mart, it has to be integrated, and cleaned. This could be the part that takes most of your time and effort. IV. Creating the cube. Here you define your measures, dimensions and their hierarchies, and then select the storage type: ROLAP, MOLAP, or HOLAP