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BI EE uses the ADM driver to extract the data from HFM.

Note: HFM (Hyperion Financial Management)

Example:

I EE connects to HFM through JavaHost. So, the setup for configuring the connectivity is to basically make the EPM environment variables available for access to BI EE. Before making the changes, there are a couple of things to be aware of

1. Ensure that DCOM is enabled on the BI Server machine. This is needed for HFM client to work/connect properly with HFM Server.

2. Ensure that HFM Client is installed.

The above assumes EPM 11.1.2.x version. If you are on Hyperion 9.3.3, then you need to have the ADM drivers installed manually. All of this is documented clearly. So, rather than re-iterating that i will just point to the link here. Ensure that you follow all the steps mentioned in the docs else you will have issues in the connectivity. Once all the pre-requisite steps are done, we start off with the metadata import process.

Oracle Essbase developers creating planning and budgeting applications have been keenly aware of the advantages and disadvantages of the two different storage types available to them:

* Block storage option (BSO) databases support a wide range of calculations, including forecasts, allocations, and aggregations through procedural calculation scripts. The results of these calculations can be written back to the database, but applications that use BSO storage are limited to about 10 dimensions and member counts in the low hundreds of thousands.

* Aggregate storage option (ASO) databases can support about 20 dimensions and member counts in the millions. ASO databases use multidimensional functions rather than procedural scripts to perform calculations, but the results of queries cannot be stored back in the database, making ASO unsuitable for most financial management applications.

NQSConfig.ini is the main configuration file of Bi Server and contains a lot of parameters.

The parameters in the Query Result Cache Section provide configuration information for Oracle BI Server caching. The query cache is enabled by default. After deciding on a strategy for flushing outdated entries, you should configure the cache storage parameters in Fusion Middleware Control and in the NQSConfig.INI file.

Modeling Time Series Function in OBIEE

Modeling Time Series Data

Time series functions provide the ability to compare business performance with previous time periods, allowing you to analyze data that spans multiple time periods. For example, time series functions enable comparisons between current sales and sales a year ago, a month ago, and so on.

Because SQL does not provide a direct way to make time comparisons, you must model time series data in the Oracle BI repository. First, set up time dimensions based on the period table in your data warehouse. Then, you can define measures that take advantage of this time dimension to use the AGO, TODATE, and PERIODROLLING functions.

A: OBIEE 11g is centred on WebLogic Server as the underlying application server architecture.

B: If you want to scale out this infrastructure, you would typically run the OBIEE 11g installer on a second server, and instead of choosing to do a new install, you’d choose to instead scale out the existing BI domain.

C: OBIEE Clustering – Approach of Oracle BI Server Load balancing/Failover

You can specify a Driving table parameter in a complex join.

Driving tables are for use in optimizing the manner in which the Oracle BI Server processes cross-database joins when one table is very small and the other table is very large.

The BI Server will query the driving table (the small one) and will get a list of values. The BI Server will then generate a second SQL using a parameterized IN list with the values from the first query.

Specifying driving tables leads to query optimization only when the number of rows being selected from the driving table is much smaller than the number of rows in the table to which it is being joined.

OBIEE - Grain (Level of summarization)

Grain definition in the context of OBIEE.

The following list describes the different grains in navigating a query:

* Aggregation grain. The grain of the aggregate source of from a level based metrics

* Query grain. The grain of the request.

* Time Series grain. The grain at which the time series function aggregation is requested.

* Storage grain. The query in the example can be computed from daily sales or from monthly sales, or from quarterly sales.

Note:

The aggregation grain is defined in the content tab of the logical table source.

When the query grain match the aggregation grain of a logical table source, this one is used to create the physical query.

This warning indicates that the given column is only mapped to logical table sources that are disabled. The warning brings this issue to the repository developer's attention in case the default behavior is not desired.

Note: This source should be combined with other sources at this level (check box)

Description: Check this box when data sources at the same level of aggregation do not contain overlapping information. In this situation, all sources must be combined to get a complete picture of information at this level of aggregation.

Note 2: To use a source correctly, the Analytics Server has to know what each source contains in terms of the business model. Therefore, you need to define aggregation content for each logical table source of a fact table. The aggregation content rule defines at what level of granularity the data is stored in this fact table.

Use the Content tab of the Logical Table Source dialog box to define any aggregate table content definitions, fragmented table definitions for the source, and Where clauses (if you want to limit the number of rows returned).