Can you have 2 fact tables?

Can you have 2 fact tables?

Usually you will have two fact tables when you cannot join both the facts together.

Which is better snowflake schema or star schema?

Snowflake schemas will use less space to store dimension tables but are more complex. Star schemas will only join the fact table with the dimension tables, leading to simpler, faster SQL queries. Snowflake schemas are good for data warehouses, star schemas are better for datamarts with simple relationships.

Which is wrong about snowflake schema?

Second statement is also false as snowflake schema requires high maintenance efforts to avoid data update and insert anomalies. Also it’s computational method requires more number of joins for query processing. Third statement is true as it is the most important feature of snowflake schema.

Where do we use 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.

What is Snowflake schema 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.

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.

What is a snowflake data model?

In computing, a snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape. The snowflake schema is represented by centralized fact tables which are connected to multiple dimensions..

Is Snowflake a NoSQL?

Snowflake has some distinct advantages over NoSQL databases like Cassandra and mongoDB. As Snowflake loads semi-structured data, it records metadata which is then used in query plans and query executions, providing optimal performance and allowing for the querying of semi-structured data using common SQL.

Is fact table normalized or denormalized?

Fact tables are completely normalized To get the textual information about a transaction (each record in the fact table), you have to join the fact table with the dimension table. Some say that fact table is in denormalized structure as it might contain the duplicate foreign keys.

What is a good alternative to the star schema?

This makes the snowflake schema a better choice than the star schema if you want your data warehouse schema to be normalized . However, complex joins mean that the performance of the snowflake schema is generally worse than the star schema.

Which schema is most popular?

Star schema

Which schema is most popular and why?

#1) Star Schema This is the simplest and most effective schema in a data warehouse. A fact table in the center surrounded by multiple dimension tables resembles a star in the Star Schema model. The fact table maintains one-to-many relations with all the dimension tables.

Why is it called a star schema?

The star schema gets its name from the physical model’s resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star’s points.

What is factless fact table?

A factless fact table is a fact table that does not have any measures. It is essentially an intersection of dimensions (it contains nothing but dimensional keys). For example, you can have a factless fact table to capture student attendance, creating a row each time a student attends a class.

What are the 3 types of SCD?

What are the types of SCD?

  • Type 0 – Fixed Dimension. No changes allowed, dimension never changes.
  • Type 1 – No History. Update record directly, there is no record of historical values, only current state.
  • Type 2 – Row Versioning.
  • Type 3 – Previous Value column.
  • Type 4 – History Table.
  • Type 6 – Hybrid SCD.

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 is junk dimension with example?

A Junk Dimension is a dimension table consisting of attributes that do not belong in the fact table or in any of the existing dimension tables. The nature of these attributes is usually text or various flags, e.g. non-generic comments or just simple yes/no or true/false indicators.

When would you use a junk dimension?

Junk dimensions are used to reduce the number of dimensions in the dimensional model and reduce the number of columns in the fact table. A junk dimension combines two or more related low cardinality flags into a single dimension.

How do you name a junk dimension?

Note that a junk dimension is always type 0 (static). The name of the dimension contains the word “junk”, usually after “dim” rather than at the end. For the Y/N columns, the column names are standardised with “IS_…” or “… _FLAG”.

What is mean by junk dimension?

A junk dimension combines several low-cardinality flags and attributes into a single dimension table rather than modeling them as separate dimensions. There are good reasons to create this combined dimension, including reducing the size of the fact table and making the dimensional model easier to work with.

Why do we need degenerate dimensions?

Degenerate dimensions also serve as a helpful tie-back to the operational world. This can be especially useful during data staging development to align fact table rows to the operational system for quality assurance and integrity checking.

What are the different types of facts?

There are three types of facts:

  • Summative facts: Summative facts are used with aggregation functions such as sum (), average (), etc.
  • Semi summative facts: There are small numbers of quasi-summative fact aggregation functions that will apply.

What are three types of facts?

There are three types of facts:

  • Additive: Additive facts are facts that can be summed up through all of the dimensions in the fact table.
  • Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others.

What are the fact table’s categories?

The Three Types of Fact Tables

  • Transaction fact tables.
  • Periodic snapshot tables, and.
  • Accumulating snapshot tables.

How facts are divided in fact table?

A fact table typically has two types of columns: those that contain facts and those that are a foreign key to dimension tables. The grain of a fact table represents the most atomic level by which the facts may be defined. The grain of a sales fact table might be stated as “sales volume by day by product by store”.

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