More importantly, what can it do for you? You'll find step-by-step instructions for installation and setup, a guide to administrative tasks. MDX Multi-Dimensional eXpressions is extraordinarily powerful, and is used for both self-service reporting and advanced analytics. If you are going to do multi-dimensional analysis, you have to know MDX. MDX enables business users to perform analyses and generate reports that are next to impossible in any other query language, such.
This comprehensive guide will teach you all there is to know about the next generation business warehouse from SAP! Start with a fresh installation or migrate from an existing system. Then, step into key SAP HANA user roles and discover core capabilities for administration, application development, advanced analytics, security, data integration, and more.
It is a new product, built entirely on one of the most advanced in-memory platforms today. It provides a simple set of objects that is well suited for modeling an agile and flexible layered architecture of a modern data. Enter the name of the Function Module that you want to use as data source for Virtual Providers. When data is loaded from one BI object to other BI object, transformation is applied on the data.
Transformation is used to convert a field of source into the target object format. Each Transformation consists of minimum one transformation rule. As different rule types and routines are available that allows you to create simple to complex transformations. Transformation Rules Transformation rules are used to map source fields and target fields. Different rule types can be used for transformation.
Routines are defined as local ABAP classes and it consists of predefined definition and implementation area. Routines can be defined as transformation rule for a key figure and they are available as rule types.
Data is sent to delta queue or PSA table in real time. The real time data acquisition is used when you transfer data more frequently — hourly or every minute and data is refreshed at the report level multiple times in a single time interval. Real-time data acquisition is one of the key properties of data source and data source should support real time data acquisition. Each InfoObject resides in an InfoArea and you can define it in a folder which is used to hold similar files together.
How to create an Infoarea? To create an Infoarea, go to RSA workbench. InfoArea created will be shown at the bottom. Each InfoProvider contains multiple InfoObjects. InfoObjects are used in reports to analyze the data stored and to provide information to the decision makers. InfoObjects are created in the InfoObject Catalog. Click on the create button. The next step is to save and activate the InfoObject Catalog.
You can use the Reference Characteristics if the new characteristics, which have to be created has the same technical properties of an existing characteristic. You can use template characteristics for a new characteristic that has some of the technical properties of an already existing characteristic. Click Continue. In the next window you will get the Edit Screen of the InfoObject. This InfoObject will be saved and Active.
You can also use maintain InfoObject icon from the toolbar menu. You can change text and meaning of an InfoObject. The InfoObject with key figures — is not possible if the key figure type, data type or aggregation of the key figure is used in the InfoProvider. What is a DataStore Object? A DSO DataStore Object is known as the storage place to keep cleansed and consolidated transaction or master data at the lowest granularity level and this data can be analyzed using the BEx query.
A DataStore Object contains key figures and the characteristic fields and data from a DSO can be updated using Delta update or other DataStore objects or even from the master data. These DataStore Objects are commonly stored in two dimensional transparent database tables. The key contains request id, package id and record number.
Once the activation is done, the request is deleted from the activation queue. A request is then added to the Activation queue. It can be triggered manually or automatically. It takes a standard DSO by default. Click the Create F5 button. Click on Edit to change the settings for SID generation. You can see that the InfoObject is added under the key fields section. It is different from standard DSOs owing to the way how it processes the data. This data is stored in the same format in which it was loaded in to the DataStore Object for any direct update by the application.
The data is retrieved from external systems using APIs. For data with keys already in the system, the data is changed. You can access data for reporting and analysis immediately after it is loaded. To create a direct update DataStore, go to administration workbench. Click on the Create F5 button. To change the DSO type, go to the settings tab as shown in the following screenshot. Click on the Edit icon and in a new window which opens, you can change the DataStore Object and also select its Type.
Write Optimized DSO provides a temporary storage area for large sets of data if you are executing complex transformations for this data before it is written on to the DataStore object. The data can then be updated to further InfoProviders. You only have to create the complex transformations once for all data.
Business rules are only applied when the data is updated to additional InfoProviders. This means that you can save time and further process the data quickly. Reporting is possible on the basis of these DataStore objects.
This allows you to process the data more quickly. In Write optimized DSO, loaded data is not aggregated. If two data records with the same logical key are extracted from the source, both records are saved in the DataStore object. The record made responsible for aggregation remains, however, so that the aggregation of data can take place later in standard DataStore objects. The system generates a unique technical key for the write-optimized DataStore object.
The standard key fields are not necessary with this type of a DataStore object. If there are standard key fields anyway, they are called semantic keys, so that they can be distinguished from the other technical keys. Click on the Edit icon and when a new window opens, you can change the DataStore Object and also select the type which is required. Infosets are used to join data and that data is used in the BI system. When an InfoObject contains time dependent characteristics, then that type of a join between data sources is called as a temporal join.
These temporal Joins are used to map a period of time. At the time of reporting, other InfoProviders handle time-dependent master data in such a way that the record that is valid for a pre-defined unique key date is used each time. You can define a Temporal join as a join that contains at least one time-dependent characteristic or a pseudo time- dependent InfoProvider. An InfoSet can also be defined as a semantic layer over the data sources.
You can use the temporal join with InfoSet to specify at a particular point of time when you want to evaluate the data. The data joined using Infosets are available to use in BEx queries for reporting. The joins can be divided into the following queries: Inner Join This join returns rows when there is a complete match in both the tables.
The rows from the second, or right hand side table only show up if they have a match with the rows from the first table. You can define Temporal join that contains at least one time-dependent characteristic or a pseudo time-dependent InfoProvider. Self Join When a table is joined to itself, which is like you are joining a table twice. Start with the InfoProvider section — Here you can define the object that you want to use while defining an InfoSet.
Click on Select InfoProvider option. This will allow you to select the InfoProvider to which data is joined. Select Join type, etc. An InfoCube consists of a set of relational tables which are logically joined to implement the star schema.
A fact table in the star schema is joined with multiple dimension tables. They are available as InfoProviders for analysis and reporting purposes. It consists of a number of InfoObjects that are filled with data from staging. It has the structure of a star schema.
The real-time characteristic can be assigned to an InfoCube. There are large fact tables that contains key figure for InfoCube and multiple smaller dimension tables that surround it. An InfoCube contains fact tables that further contain key figures and characteristics of an InfoCube that are stored in the dimensions. These dimensions and fact tables are linked to each other using identification numbers dimension IDs. The key figures in an InfoCube are related to characteristics of its dimension.
Granularity degree of detail of key figures in an InfoCube is defined by its characteristics. Characteristics that logically belong together are grouped together in a dimension. The fact table and dimension tables in an InfoCube are both Relational database tables. An InfoCube consists of a fact table which is surrounded by 16 dimension tables and master data that is lying outside the cube. It is a self-enclosed data set encompassing one or more related business processes. A reporting user can define or execute queries against an info cube.
Your own InfoCube should begin with a letter between A to Z and that it should be 3 to 9 characters in length. All InfoObjects to be used in an InfoCube should be available in an active version. Once all this is done, you can click on Create as shown in the following screenshot.
To create a copy of an already existing InfoCube, you can enter an InfoCube as a template. Rename the dimension as per the InfoObject. Add InfoObject to dimension. In a similar way you can also create new dimensions and add InfoObjects. In a similar way you can add other key figures as well. Real time InfoCubes are used in connection with the entry of planning data.
This will delete the transaction data and InfoCube will be set to inactive. You can schedule this report as background job for InfoCubes with more than 10, data records because the runtime could potentially be long. In Virtual Provider, it allows read only read access to the data.
Types of Virtual Providers As mentioned above, there is a need to find out when a Virtual InfoProvider should be used. In this case, a virtual provider can be based on a DataSource for direct access or on another InfoProvider. Either the BEx query is executed or you navigate inside the query. But, a request is sent through the virtual provider to its source and the needed data is returned back. A query is executed on VirtualProvider that triggers a data request with characteristic selections.
The source structure is dynamic and is determined by the selections. Virtual Provider Based on Functional Module This is the most complex type of a VirtualProvider but at the same time it is also more flexible using which you can add data from the source and also can apply complex calculations or any changes before it is pushed to the OLAP engine.
You have a number of options for defining the properties of the data source more precisely. According to these properties, the data manager provides various function module interfaces for converting the parameters and data.
These interfaces have to be implemented outside the BI system. Using InfoObjects as Virtual Providers In this you allow a direct access to the source system for an InfoObject type of a characteristic that you have selected for use as an InfoProvider. So there is no need to load the master data, however direct access can have negative impact on query performance. How to setup InfoObjects as Virtual Providers? Go to InfoObjects Maintenance page.
Next is to go the modeling tab, select the InfoProvider tree. Here, the system constructs the union set of the data sets involved and all the values of these data sets are combined. In an InfoSet you create the dataset using joins.
These joins only combine values that appear in both tables. As compared to a Union, joins form the intersection of the tables. Then the MultiProvider screen appears. You can also transfer the entire dimensions.
The next step is to save and activate the MultiProvider and only this activated MultiProvider will be available for reporting and analysis. The data from a flat file can be transferred to BI from a workstation or from an application server.
Subsequent fields are then no longer in the correct order. Depending on the conversion routine being used, you can also use other formats.
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