Can you change the grain of a fact table?

Can you change the grain of a fact table?

You can do this in a few steps: like this if memory serves: Your market data should be presented to the cube as a view. If it is not create one and in the DSV replace the table with “another table” and point it to the new view.

Can we have more than one fact table star schema?

Although the diagram in this chapter shows a single fact table, a star schema can have multiple fact tables. A more complex schema with multiple fact tables is useful when you need to keep separate sets of measurements that share a common set of dimension tables.

How do you put data into a fact table?

Data Warehousing and Machine Learning

  1. 9 February 2015. Populating Fact Tables.
  2. Create The Temp Table. SELECT * INTO #fact_sales FROM dw.dbo.fact_sales WHERE 1 = 0.
  3. Populate The Temp Table. SET IDENTITY_INSERT #fact_sales ON.
  4. Update Existing Records. UPDATE f.
  5. Insert New Records. INSERT INTO dw.dbo.fact_sales.

Can a cube have multiple fact tables?

Since a single cube can now be based on multiple fact tables, OLAP modeling with SSAS more closely aligns with the way most customers want to view their enterprise data.

Can we join two fact tables in Obiee?

Joining two fact tables with different dimensions into single logical table. It is required to create dimensions for all dimension tables. Not that we have two logical table sources for SALES fact logical table.

What to do when Obiee fact tables do not join to all dimension tables?

An alternate approach is to physically join DimX to FactTable1 using a complex join having the join condition 1=1. The aggregation content for both logical fact table sources can be set at Detail so that any query containing columns from DimX can use FactTable1 as a source.

What is fact fact join?

Fact extensions can be defined by a fact relation instead of a table relation. With a fact relation, the table join is possible on any table that contains the fact.

What is non conformed dimension?

A non-conformed dimension is where you add an attribute to an analysis which does not logically relate to all the facts. Take this example, here we have an analysis with 2 “facts” and 1 “conformed dimension” i.e. the “Store” dimension attribute relates to both “# POIs” and “# Orders”.

Why do we need conformed dimension?

Conformed dimensions are dimensions that are shared by multiple stars. They are used to compare the measures from each star schema [3]. The reuse of conformed dimensions is very common in order to “support true, cross-business process analysis” [6].

What is conformed dimension with example?

For two dimension tables to be considered as conformed, they must either be identical or one must be a subset of another. There cannot be any other type of difference between the two tables. For example, two dimension tables that are exactly the same except for the primary key are not considered conformed dimensions.

Why a factless fact table is used?

Factless fact tables are used for tracking a process or collecting stats. They are called so because, the fact table does not have aggregatable numeric values or information. There are two types of factless fact tables: those that describe events, and those that describe conditions.

What should be in a fact table?

In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is located at the center of a star schema or a snowflake schema surrounded by dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys.

Is a fact table normalized or denormalized?

In general Fact table is normalized and Dimension table is denormalized. So that you will get all required information about the fact by joining the dimension in STAR schema.

How do you create a fact table?

Steps in designing Fact Table:

  1. Identify a business process for analysis(like sales).
  2. Identify measures or facts (sales dollar).
  3. Identify dimensions for facts(product dimension, location dimension, time dimension, organization dimension).
  4. List the columns that describe each dimension.

Does a fact table have a primary key?

The fact table also has a primary (composite) key that is a combination of these four foreign keys. As a rule, each foreign key of the fact table must have its counterpart in a dimension table. Therefore a dimension table can also be a fact table for a separate star schema.

What is difference between star and snowflake schema?

Star and snowflake schemas are similar at heart: a central fact table surrounded by dimension tables. The difference is in the dimensions themselves. In a star schema each logical dimension is denormalized into one table, while in a snowflake, at least some of the dimensions are normalized.

What is the main reason of having multi fact?

The reason is simple, it’s easy to organize and it’s easy to read. If you know your dimension tables and your fact table, you can already answer the question of whether you can aggregate by dimensions or “slice and dice” by categories. The problem occurs when you need to add another fact table to a model.

Is Snowflake OLAP or OLTP?

Snowflake is designed to be an OLAP database system. One of snowflake’s signature features is its separation of storage and processing: Storage is handled by Amazon S3.

Which is wrong about snowflake schema?

Explanation: Snowflake schema is an arrangement of tables in a multidimensional database system. It contains Fact Tables connected to multi-dimension tables. Second statement is also false as snowflake schema requires high maintenance efforts to avoid data update and insert anomalies.

Which schema is faster star or snowflake?

The Star schema is in a more de-normalized form and hence tends to be better for performance. Along the same lines the Star schema uses less foreign keys so the query execution time is limited. In almost all cases the data retrieval speed of a Star schema has the Snowflake beat.

Why do we apply snowflake schema?

A snowflake schema is a variation on the star schema, in which very large dimension tables are normalized into multiple tables. Dimensions with hierarchies can be decomposed into a snowflake structure when you want to avoid joins to big dimension tables when you are using an aggregate of the fact table.

Should I use star or snowflake schema?

Star schemas will only join the fact table with the dimension tables, leading to simpler, faster SQL queries. Snowflake schemas have no redundant data, so they’re easier to maintain. Snowflake schemas are good for data warehouses, star schemas are better for datamarts with simple relationships.

What are the advantages disadvantages of snowflake schema?

Advantages and Disadvantages of the Snowflake Schema There are two main advantages to the snowflake schema: Better data quality (data is more structured, so data integrity problems are reduced) Less disk space is used then in a denormalized model.

What is a snowflake schema give an example?

The snowflake schema consists of one fact table which is linked to many dimension tables, which can be linked to other dimension tables through a many-to-one relationship. Example: Figure shows a snowflake schema with a Sales fact table, with Store, Location, Time, Product, Line, and Family dimension tables.

How can we prevent snowflake schema?

Avoid snowflaking or normalization of a dimension table, unless required and appropriate. Do not snowflake hierarchies of one dimension table into separate tables. Hierarchies should belong to the dimension table only and should never be snowflakes.

How do you make a snowflake schema?

Using the Snowflake Create Schema command

  1. : Provide a unique name for the Schema you want to create.
  2. Transient: It represents a temporary Schema.
  3. Clone: It is used to create a clone of an existing Schema.
  4. At|Before: This field is to used to provide a time-stamp to clone a Schema.

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