What is olap in sql

You can see that the region dimension is first rolled up followed by the quarter dimension. Note the two rows which have been left out when compared to the result of the CUBE operator in Table 1. Whereas the previous example applied the GROUP BY ROLLUP construct to two completely independent dimensions, it can also be applied to attribute types that represent different aggregation levels and hence different levels of detail along the same dimension. We could then formulate the following ROLLUP query, yielding sales totals respectively per city, per country, per region and the grand total:.

Since the three attribute types represent different levels of detail in the same dimension, they are transitively dependent on one another, illustrating the fact that these data warehouse data are indeed denormalized. Consider the following example:. One way to speed up performance is by turning some of these OLAP queries into materialized views. 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.

For example, setting this option to the default and recommended value of 30 percent means that aggregations will be built to give the OLAP cube a percent estimated performance gain. However, this does not mean that 30 percent of the possible aggregations will be built.

Usage-based optimization makes it possible for SSAS to log the requests for data so that when a query is run, the information is fed into the aggregation design process. SSAS then reviews the data and recommends which aggregations should be built to give the best estimated performance gain.

Each measure group in a cube is divided into partitions, where a partition defines a portion of the fact data that is loaded into a measure group. Partitions are completely transparent to the end user, but they have an important impact on performance and scalability.

For example, partitions can be processed separately and in parallel. They can have different aggregation designs. You can reprocess a partition without affecting all the other partitions in a measure group.

Also, SSAS automatically scans only the partitions that contain the necessary data for a query, which can vastly improve query performance. Cube partitioning is performed on every data warehouse maintenance job run, which is hourly by default. The specific process module that runs is named ManageCubePartitions. It always runs after the CreateMartPartitions step.

This dependency data is stored in the infra. Utility, in the PartitionUtil class. Specifically, there is a ManagePartitions method in the class that handles all partition maintenance. Online analytical processing OLAP is a technology that organizes large business databases and supports complex analysis. It can be used to perform complex analytical queries without negatively affecting transactional systems.

The databases that a business uses to store all its transactions and records are called online transaction processing OLTP databases. These databases usually have records that are entered one at a time. Often they contain a great deal of information that is valuable to the organization.

The databases that are used for OLTP, however, were not designed for analysis. Therefore, retrieving answers from these databases is costly in terms of time and effort. OLAP systems were designed to help extract this business intelligence information from the data in a highly performant way.

This is because OLAP databases are optimized for heavy read, low write workloads. A semantic data model is a conceptual model that describes the meaning of the data elements it contains. Organizations often have their own terms for things, sometimes with synonyms, or even different meanings for the same term. For example, an inventory database might track a piece of equipment with an asset ID and a serial number, but a sales database might refer to the serial number as the asset ID. There is no simple way to relate these values without a model that describes the relationship.

Semantic modeling provides a level of abstraction over the database schema, so that users don't need to know the underlying data structures. This makes it easier for end users to query data without performing aggregates and joins over the underlying schema. Also, usually columns are renamed to more user-friendly names, so that the context and meaning of the data are more obvious. If you are not sure, which should not be the case, click the Suggest button. Suggest button will provide you the suggestion for the measure groups.

It is important to note that you have to choose only the required measure columns. If unnecessary columns are selected, it will cause delays in cube processing. In the above example, we have eliminated the Revision Number column which is not a business measure column. Dimension is a collection of referenced information so that measures can be analyzed into detail. From the following screen, you can choose the required dimensions and modified them as shown below.

Though the cube is configuration is completed, every dimension is empty. So it is important to add attributes to the dimensions. It is essential to add only the required attributes. Otherwise, the cube process will take longer and the cube will be larger.

If the cubes are larger, cube accessing also will have a negative impact.