Dimensions allow the filtering, grouping, and labeling of data. For example, you can filter computers by the installed operating system and group people into categories by gender or age. The data can then be presented in a format where the data is categorized naturally into these hierarches and categories to allow a more in-depth analysis. Dimensions may also have natural hierarches to allow users to "drill down" to more detailed levels of detail.
For instance, the Date dimension has a hierarchy that can be drilled down by Year, then Quarter, then Month, then Week, and then Day. For example, Microsoft team members might want a quick and simple summary of the sales of the Xbox One gaming console in They can further drill down to get sales figures for a more focused time frame. Business analysts may want to examine how the sales of Xbox One consoles were affected by the launch of the new console design and the Kinect for Xbox One.
This helps them determine what sales trends are occurring and what potential revisions of business strategy are needed. By filtering on the date dimension, this information can be quickly delivered and consumed. This slicing and dicing of data is enabled only because the dimensions have been designed with attributes and data that can easily be filtered and grouped by the customer.
All dimensions use the primary data warehouse data mart as their source, even in multiple data mart scenarios. In multiple data mart scenarios, this can possibly lead to dimension key errors during processing of the cube. A measure group is the same concept as a fact in data warehouse terminology. Just as facts contain numeric measures in a data warehouse, a measure group contains measures for an OLAP cube. All the measures in an OLAP cube that derive from a single fact table in a data source view also can be considered to be a measure group.
There can be instances, however, in which there will be multiple fact tables from which the measures in an OLAP cube derive. Measures of the same detail level are united in one measure group. Measure groups define what data will be loaded into the system, how the data is loaded, and how the data is bound to the multidimensional cube. Each measure group also contains a list of partitions, which hold the actual data in separate, nonoverlapping sections. Measure groups also contain aggregation design, which defines the presummarized data sets that are calculated for each measure group to improve the performance of user queries.
Measures are the numeric values that users want to slice, dice, aggregate, and analyze; they are one of the fundamental reasons why you would want to build OLAP cubes using data warehousing infrastructure. Much of your OLAP cube development time will be spent determining and defining which measures will be displayed and how they will be calculated.
Measures are values that usually map to numeric columns in a data warehouse fact table, but they can also be created on dimension and degenerate dimension attributes.
An example of a measure that exists in the data warehouse is ActivityTotalTimeMeasure. ActivityTotalTimeMeasure is a measure from ActivityStatusDurationFact that represents the time that each activity is in a certain status. The detail level of a measure is made up of all the dimensions that are referenced. For example, the detail level of the ComputerHostsOperatingSystem relationship fact consists of the Computer and Operating System dimensions.
Aggregation functions are calculated on measures to enable further data analysis. The most common aggregation function is Sum. Other common aggregation functions include Min, Max, and Count. After the raw data has been processed in an OLAP cube, users can perform more complex calculations and queries using multidimensional expressions MDX to define their own measure expressions or calculated members.
SQL Server was not designed to work with the data model that multidimensional databases support. When a user drills down into the data in an OLAP cube, the user is analyzing the data at a different level of summarization. The level of detail of the data changes as the user drills down, examining the data at different levels in the hierarchy.
As the user drills down, he or she moves from summary information to data with a more narrow focus. The following are examples of drilling down:. When users drill-through data, they want to see all the individual transactions that contributed to the OLAP cube's aggregated data. In other words, the user can retrieve the data at a lowest level of detail for a given measure value.
For example, when you are given the sales data for a particular month and product category, you can drill through that data to see a list of each table row that is contained within that cell of data. It is common to confuse the terms "drill down" and "drill through" with each other. The main difference between them is that a drill-down operates on a predefined hierarchy of data-for example, USA, then into Washington, then into Seattle-within the OLAP cube.
A drill-through go directly to the lowest level of detail of data and retrieves a set of rows from the data source that has been aggregated into a single cell. Organizations can use key performance indicators KPIs to gauge the health of their enterprise and their performance by measuring their progress toward their goals. KPIs are business metrics that can be defined to monitor progress toward certain predefined objectives and goals.
A KPI usually has a target value and an actual value, which represents a quantitative goal that is critical to the success of the organization. KPIs are usually displayed in groups on a scorecard to show the overall health of the business in one quick snapshot. An example of a KPI is to complete all change requests within 48 hours.
A KPI can be used to measure the percentage of change requests that are resolved within that time frame. You can create dashboards to represent KPIs visually. For example, you might want to define a KPI target value for completion of all change requests within 48 hours to 75 percent. A partition is a data structure that holds some or all of the data in a measure group. Every measure group is divided into partitions.
A partition defines a subset of the fact data that is loaded into the measure group. Partitions are a feature that is transparent to the end user, but they have a major impact on both the performance and the scalability of OLAP cubes. All partitions for a measure group always exist in the same physical database.
For example, you can remove or reprocess the data in one partition of a measure group without affecting the rest of the measure group. When you load new data into a fact table, only the partitions that should contain the new data are affected. Partitioning also improves processing and query performance for OLAP cubes.
SSAS can process multiple partitions in parallel, leading to a much more efficient use of CPU and memory resources on the server.
While it runs a query, SSAS fetches, processes, and aggregates data from multiple partitions as well. Only partitions that contain the data that is relevant to a query are scanned, which reduces the overall amount of input and output. One example of a partitioning strategy is to place the fact data for each month into a monthly partition. At the end of each month, all the new data goes into a new partition, which leads to a natural distribution of data with nonoverlapping values.
Aggregations in an OLAP cube are presummarized data sets. SSAS can use these aggregations when it answers queries to reduce the amount of necessary calculations, returning the answers quickly to the user. Building the correct aggregations can drastically improve query performance. This is often an evolving process throughout the lifetime of the OLAP cube as its queries and usage change. A base set of aggregations is usually created that will be useful for most of the queries against the OLAP cube.
Aggregations are built for each partition of an OLAP cube within a measure group. When an aggregation is built, certain attributes of dimensions are included in the presummarized data set. Users can quickly query the data based on these aggregations when they browse the OLAP cube. Aggregations must be designed carefully because the number of potential aggregations is so large that building all of them would take an unreasonable amount of time and storage space.
The Performance Gain Reaches option defines what percentage of aggregations is built. Big Data Expand child menu Expand. Live Project Expand child menu Expand. AI Expand child menu Expand. Toggle Menu Close. Search for: Search. OBIEE is a multifaceted network of tools which can create a more fluid and better-integrated data flow for your business.
This adds a second logical table source to the logical table, adds four new logical columns for the fact table foreign keys, and adds a second logical source to the Units logical column. Now that I've mapped the second fact table source, I can start doing the same for the dimensions. As the column names coming in from Oracle are different to the logical column names derived from the Analysis Services cube, BI Administrator has added them as new columns.
What I need to do now is map the original logical columns correctly to the Oracle physical source, and then I can remove these columns. I do this by double-clicking on one of the original logical columns and selecting the Data Type tab.
I then use the Column Mapping dialog to correctly map each logical column to its physical source. I do this by clicking on the ellipses Once this is done, I end up with all of my logical columns properly mapped to the Oracle data source, including the ones that were present in the Analysis Services model and that didn't have corresponding names in the Oracle model.
After mapping in the columns, there's a couple of more steps to perform as we've now got more detailed data in our logical model. The product and time dimensions, which currently go down to family, warehouse and quarter level only, can now be extended down one more level and the new logical columns, which came in via the Oracle import, can be mapped to them, like this:. Finally, I need to add the new detail-level columns to the presentation layer, so that they're visible in Answers.
Now I'm ready to report on the data using Answers. I start off with a query that should be satisfied from Analysis Services.
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