Types of Views in OBIEE

View Name	Description
Compound Layout	Use the compound layout view to assemble different views for display on a dashboard. On the Criteria tab, you can click the following button to access the compound layout view.
Title	Use the title view to add a title, a subtitle, a logo, a link to a custom online help page, and timestamps to the results.
Table	Use the table view to show results in a standard table. Users can navigate and drill down in the results. You can add totals, customize headings, and change the formula or aggregation rule for a column. You can also control the appearance of a column and its contents, and specify formatting to apply only if the contents of the column meet certain conditions. On the Criteria tab, you can click the following button to access the table view.
Chart	Use the chart view to drag and drop columns to a layout chart. You can customize the title, legend location, axis titles, and data labels. You can customize the size and scale of the chart, and control colors using a style sheet. Oracle BI Answers supports a variety of standard chart types, including bar charts, column charts, line charts, area charts, pie charts, and scatter charts. Custom chart subtypes include two-and-three-dimensional, absolute, clustered, stacked, combination, and custom. On the Criteria tab, you can click the following button to access the chart view.
Pivot Table	Use the pivot table view to take row, column, and section headings and swap them around to obtain different perspectives. You can drag and drop headings to pivot results, preview them, and apply the settings. Users can navigate through pivot tables and drill down into information. Users can create complex pivot tables that show aggregate and nonrelated totals next to the pivoted data, allowing for flexible analysis. For an interactive result set, elements can be placed in pages, allowing users to choose elements. On the Criteria tab, you can click the following button to access the pivot table view.
Gauge	Use the gauge view to show results as gauges, such as dial, bar, and bulb-style gauges.
Filters	Use the filters view to show the filters in effect for a request. Filters allow you to constrain a request to obtain results that answer a particular question.
Column Selector	Use the column selector view to permit users to dynamically change which columns appear in results. This allows users to analyze data along several dimensions. By changing the facts, users can dynamically alter the content of the results.
View Selector	Use the View Selector view to select a specific view of the results from among the saved views. When placed on a dashboard, the view selector appears as a drop-down list from which users can make a selection.
Legend	Use the Legend view to document the meaning of special formatting used in results, such as the meaning of custom colors applied to gauges.
Funnel Chart	Use the funnel chart view to show a three-dimensional chart that represents target and actual values using volume, level, and color. It is useful for depicting target values that decline over time, such as a sales pipeline.
Narrative	Use the narrative view to show the results as one or more paragraphs of text. You can type in a sentence with placeholders for each column in the results, and specify how rows should be separated.
Ticker	Use the ticker view to show the results of the request as a ticker or marquee, similar in style to the stock tickers that run across many financial and news sites on the Internet. You can control what information is presented and how it scrolls across the page.
Static Text	Use the static text view to include static text in the results. You can use HTML to add banners, tickers, ActiveX objects, Java applets, links, instructions, descriptions, graphics, and so on, in the results.
No Results	The no results view allows you to specify explanatory text to appear if the request does not return any results.
Logical SQL	Use the logical SQL view to show the SQL generated for the request. This view is useful for trainers and Oracle BI administrators, and is usually not included in results for typical users. You cannot modify this view, except to delete it.
Create Segment	The create segment view is for users of the Oracle's Siebel Marketing Version 7.7 (or higher) operational application. Use it to display a Create Segment link in the results. Users can click this link to create a segment in their Oracle Siebel Marketing operational application, based on the results data.
Create Target List	The create target list view is for users of Oracle's Siebel Life Sciences operational application integrated with Oracle's Siebel Life Sciences Analytics applications. Use it to create a Create Target List link in the results. Users can click this link to create a target list, based on the results data, in their Oracle Siebel operational application.

Difference between Star & Snowflake Schema

Star Schema: It has single fact table connected to dimension tables like a star. In star schema only one join establishes the relationship between the fact table and any one of the dimension tables.A star schema has one fact table and is associated with numerous dimensions table and depicts a star.
Snowflake Schema: It is an extension of the star schema.In snowflake schema, very large dimension tables are normalized into multiple tables. It is used when a dimensional table becomes very big.In snow flake schema since there is relationship between the dimensions Tables it has to do many joins to fetch the data.Every dimension table is associated with sub dimension table.
The main difference between star schema and snowflake schema is that

• The star schema is highly denormalized and the snowflake schema is normalized. So the data access latency is less in star schema in comparison to snowflake schema. As the star schema is denormalized, the size of the data warehouse will be larger than that of snowflake schema.
• Performance wise, star schema is good. But if memory utilization is a major concern, then snow flake schema is better than star schema.
• A dimension table will not have parent table in star schema, whereas snow flake schemas have one or more parent tables.
• The dimensional table itself consists of hierarchies of dimensions in star schema,whereas hierarchies are split into different tables in snow flake schema. The drilling down data from top most hierarchies to the lowermost hierarchies can be done.

What are Dimension and Fact?

Dimensions are categories by which summarized data can be viewed. E.g. a profit summary in a fact table can be viewed by a Time dimension (profit by month, quarter, year), Region dimension (profit by country, state, city), Product dimension (profit for product1, product2).
A fact table is a table that contains summarized numerical and historical data (facts) and a multipart index composed of foreign keys from the primary keys of related dimension tables.
In data warehousing, a dimension is a collection of reference information about a measurable event. These events are known as facts and are stored in a fact table. Dimensions categorize and describe data warehouse facts and measures in ways that support meaningful answers to business questions. They form the very core of dimensional modeling.
Dimension tables are referenced by fact tables using keys. When creating a dimension table in a data warehouse, a system-generated key is used to uniquely identify a row in the dimension. This key is also known as a surrogate key. The surrogate key is used as the primary key in the dimension table. The surrogate key is placed in the fact table and a foreign key is defined between the two tables. When the data is joined, it does so just as any other join within the database.

Normalizer Transformation In Informatica

Normalizer transformation normalizes records from COBOL & relational sources allowing you to organize the data according to your needs.A normalizer transformation can appear anywhere in a data flow when you normalize a relational source.
The Normalizer transformation receives a row that contains multiple-occurring columns and returns a row for each instance of the multiple-occurring data. The transformation processes multiple-occurring columns or multiple-occurring groups of columns in each source row.
For example, a relational table contains quarterly sales totals by store. You need to create a row for each sales occurrence. You can configure a Normalizer transformation to return a separate row for each quarter.
The following source rows contain four quarters of sales by store:quarters of sales by store:

Store1	100	300	500	700
Store2	250	450	650	850

The Normalizer returns a row for each store and sales combination. It also returns an index that identifies the quarter number:

Store1	100	1
Store1	300	2
Store1	500	3
Store1	700	4
Store2	250	1
Store2	450	2
Store2	650	3
Store2	850	4