Can we join two fact tables directly?

Can we join two fact tables directly?

The answer for both is “Yes, you can”, but then also “No, you shouldn’t”. Joining fact tables is a big no-no for four main reasons: 1. Fact tables tend to have several keys (FK), and each join scenario will require the use of different keys.

What is the difference between fact and dimension table?

The main difference between fact table or reality table and the Dimension table is that dimension table contains attributes on that measures are taken actually table. 1. Fact table contains the measuring on the attributes of a dimension table. While in dimension table, There is less records than fact table.

What is outrigger table?

Outriggers are tables or entities that are shared by more than one dimension. A table or entity that is included in a hierarchy but is not directly related to the fact table are known as outriggers. Outriggers are often used when a dimension table or entity is referenced by another dimension.

What is a shrunken dimension?

Shrunken dimensions are conformed dimensions that are a subset of rows and /or columns of a base dimension. Another case of conformed dimension subsetting occurs when two dimensions are at the same level of detail, but one represents only a subset of rows.

What is junk dimension with example?

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. An example of this may be car color (red, black, blue, etc.)

What is factless fact table?

Factless facts are those fact tables that have no measures associated with the transaction. Factless facts are a simple collection of dimensional keys which define the transactions or describing condition for the time period of the fact. It is a fact, plain and simple.

What is star schema and snowflake?

The star schema is the simplest type of Data Warehouse schema. It is known as star schema as its structure resembles a star. A Snowflake Schema is an extension of a Star Schema, and it adds additional dimensions. It is called snowflake because its diagram resembles a Snowflake.

What is rapidly changing dimension?

Rapidly changing dimensions are dimensions where the attribute values of the dimension change frequently causing the dimension grow rapidly if you a have designed the dimension to capture the changes as a Type 2 dimension.

What is the difference between slowly changing dimension and fast changing dimension?

A dimension is a fast changing or rapidly changing dimension if one or more of its attributes in the table changes very fast and in many rows. As you know slowly changing dimension type 2 is used to preserve the history for the changes. …

What is type 1 and type 2 dimension?

In a Type 1 SCD the new data overwrites the existing data. A Type 2 SCD retains the full history of values. When the value of a chosen attribute changes, the current record is closed. A new record is created with the changed data values and this new record becomes the current record.

What are the first 3 dimensions?

Let’s start with the three dimensions most people learn in grade school. The spatial dimensions—width, height, and depth—are the easiest to visualize.

How do you implement SCD Type 1 in SQL?

Type 1 SCDs – Overwriting In a Type 1 SCD the new data overwrites the existing data. Thus the existing data is lost as it is not stored anywhere else. This is the default type of dimension you create. You do not need to specify any additional information to create a Type 1 SCD.

What is Type 2 table?

This method tracks historical data by creating multiple records for a given natural key in the dimensional tables with separate surrogate keys and/or different version numbers. Unlimited history is preserved for each insert.

How would you implement SCD Type 2 in SQL query?

Read: Impala or Hive Slowly Changing Dimension – SCD Type 2 Implementation. Netezza MERGE command to Manipulate Records from Table….

  1. Step 1: Load expired records from TGT to INT table.
  2. Step 2: Get all records which are going to expire.
  3. Step 3: Copy active records from TGT to INT table.

How do you test for SCD Type 2?

Testing Type 2 Slowly Changing Dimensions using ETL Validator

  1. Testing SCD Type 2 Dimensions.
  2. Test 1: Verifying the Current Data.
  3. Test 2: Verifying the uniqueness of the key columns in the SCD.
  4. Test 3: Verifying that historical data is preserved and new records are getting created.

How is SCD Type 2 implemented?

SCD Type 2 methodology is implemented where historical data is maintained in the Dimension table. This method doesn’t overwrites the old data in the dimension table with the new data, perhaps it keeps the previous data and new data with proper versioning using Flags or Timestamps.

Which is valid SCD Type 2?

SCD type 2 will store the entire history in the dimension table. In SCD type 2 effective date, the dimension table will have Start_Date (Begin_Date) and End_Date as the fields. If the End_Date is Null, then it indicates the current row. Know more about SCDs at Slowly Changing Dimensions Concepts.

What is the alternative of SCD Type 1 & 2 in SSIS?

Option 2: SQL Server MERGE Statement T-SQL MERGE statement first introduced in SQL Server 2008 does a pretty good job in handling Type 1 and Type 2 SCD. Oracle also has a MERGE equivalent. A good post on usage of MERGE statement for SCD can be found in Kimball Group.

How do you implement SCD Type 2 without using update strategy?

Have a sequence generator in the mapping and that is the Surrogate key and then at the session level treat all rows as insert. Make sure that there is no unique index. Because if the same values for those columns processed again the ETL will fail. You can’t treat all rows as Insert and build a Type 2 SCD.

Can we update records without using update strategy?

Update/insert records without using update strategy…

  1. Insert: – Treat all rows as inserts.
  2. Delete: – Treat all rows as deleted.
  3. Update: – Treat all rows as updates.
  4. Data Driven :- Integration Service follows instructions coded into Update Strategy flag rows for insert, delete, update, or reject.

Can we update target without primary key in Informatica?

Your only way to update without primary key is to write an Target override in Target.By doing this, you dont need primary key for updation.

Why update strategy transformation is active in Informatica?

The Update Strategy Transformation in Informatica is an Active and Connected transformation. It is useful to update, insert, or delete the records in a target based on source table data. And the beauty of this transformation is: you can restrict the records from not reaching into the target table.

What is update strategy transformation active or passive?

Update strategy transformation is an active and connected transformation. Update strategy transformation is used to insert, update, and delete records in the target table. It can also reject the records without reaching the target table.

Is lookup an active or passive transformation?

When you configure the Lookup transformation to return a single row, the Lookup transformation is a passive transformation. When you configure the Lookup transformation to return multiple rows, the Lookup transformation is an active transformation.

What is forward rejected rows in update strategy?

Now coming to the Forwarding Rejected Row Option in update strategy, this option is used to forward the rejected rows into the bad file which we define at session level. If you disable this option, the rejected records won’t be available in the bad file (reject file).

How do I get rejected rows in Informatica?

1 Answer. You can find the rejected rows in the session log. Also all rejected records will be available in the bad file. It is generally a good practice to capture all the known/ possible rejections by applying rules and writing them onto a file or table.

Which task is used anywhere to run the shell commands?

standalone task

How do you handle rejected rows in Informatica?

Edit task > Config Object > Error Handling > Error Log Type=”None”. This will write the Error Message and Error (Rejected) records (Source records) into Session log.

Can we join two fact tables directly?

Can we join two fact tables directly?

The answer for both is “Yes, you can”, but then also “No, you shouldn’t”. Joining fact tables is a big no-no for four main reasons: 1. Fact tables tend to have several keys (FK), and each join scenario will require the use of different keys.

How do you link two fact tables?

Connecting Fact Tables

  1. Create a “fact table bridge” table. The bridge table contains the fact key columns of each fact table, plus any filtering columns required.
  2. Create a new fact table containing all the required measures at the combined grain.

Can you share dimension tables between fact tables?

It is also possible to share dimension tables between fact tables. For example, time, item, and location dimension tables are shared between the sales and shipping fact table.

Can we join two fact tables in Obiee?

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

Can star schema have multiple fact tables?

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.

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.

What are the schemas of a data warehouse?

Data Warehouse is maintained in the form of Star, Snow flakes, and Fact Constellation schema. A Star schema contains a fact table and multiple dimension tables. Each dimension is represented with only one-dimension table and they are not normalized.

How are dimension tables shared in data warehousing?

It is also possible to share dimension tables between fact tables. For example, time, item, and location dimension tables are shared between the sales and shipping fact table. Multidimensional schema is defined using Data Mining Query Language (DMQL).

How is the fact table used in data warehousing?

It contains the keys to each of four dimensions. The fact table also contains the attributes, namely dollars sold and units sold. Note − Each dimension has only one dimension table and each table holds a set of attributes. For example, the location dimension table contains the attribute set {location_key, street, city, province_or_state,country}.

Is it OK to directly link fact tables in DWH?

In the end, both approaches have their pros and cons, study both and pick the one you’re most comfortable with. The answer is obvious NO, as per definition any table that is referenced via foreign key from a fact table is a dimension table.

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