Skip to main content

Facts and Fact table- Data warehouse fundamentals- part 5

Types of fact table
Fact tables are of two types
  1. CFT (Cumulative fact table)
  2. SFT (Snap Shot fact table)


Cumulative fact table
If we are loading the values into fact table based on time, then that is called cumulative fact table.

Snap Shot fact table
If we are loading the values based on client requirement, then that is called snap shot fact tables.

Types of facts

Fact: Fact is a numeric value, based on that numeric value; we are going to analyze data.
There are three types of facts are there. They are as follows.
  1. Additive fact
  2. Semi additive fact
  3. Non additive fact


Additive fact: - If fact values are coming from all dimensional tables, then such a fact is called additive fact.

Semi additive fact: - If fact values are coming from few dimension tables, then that is called semi additive fact.
Example: Transactions at bank (say if I drop deposit some money in my mom’s account, she only gets to know that amount is credited. She won’t know who credited money or how it is credited etc.)
Non additive fact: - If facts are not coming from any dimension table, then it is called Non additive fact.


REVENUE
PROFIT
PROFIT PERCENTAGE
20000
2000
-----------
40000
4000
50%

Here Revenue and profit are facts and profit percentage which is also a fact, is calculated using these two facts.
Here we can observe that Profit percentage values are not coming from any dimension tables.
Some more examples are Profits, Loss, gains, Ratio etc.


Factless Fact: - If fact table cannot contain any facts, then it is called factless fact.

SLNO
PID
LID
TID
CID
REVENUE
PROFIT
1
22
356
459
16
25,000
6000
2
56
45
546
75
60,000
4500
Upto






71
-----
----------
400 (Representing June 4th)
---------
----------
--------
72
52
56
985
21
75,321
9000


Here we know that by table that there is no sale happened on June 4th (TID- 400). So analysis is done why ‘No sale’ happened on that day.

Factless fact is basically used for negative analysis.

Comments

  1. Hi I read your post very carefully and I think you are right that a well written post should be at least a 100 words and should capture the essence of your blog, book or article.

    MSBI Training in Chennai

    Informatica Training in Chennai

    ReplyDelete

Post a Comment

Popular posts from this blog

BIG Data, Hadoop – Chapter 2 - Data Life Cycle

Data Life Cycle The data life cycle is pictorial defined as show below:     As we see, in our current system, we capture/ Extract our data, then we store it and later we process for reporting and analytics. But in case of big data, the problem lies in storing and then processing it faster. Hence Hadoop takes this portion, where it stores the data in effective format (Hadoop distributed File System) and also process using its engine (Map Reduce Engine). Since Map Reduce engine or Hadoop engine need data on HDFS format to process, We have favorable tools available in market to do this operation. As an example, Scoop is a tool which converts RDBMS to HDFS. Likewise we have SAP BOD to convert sap system data to HDFS.

OLE DB provider "Microsoft.ACE.OLEDB.12.0" for linked server "(null)" returned message "The Microsoft Access database engine cannot open or write to the file ''. It is already opened exclusively by another user, or you need permission to view and write its data.". Msg 7303, Level 16, State 1, Line 1 Cannot initialize the data source object of OLE DB provider "Microsoft.ACE.OLEDB.12.0" for linked server "(null)".

OLE DB provider "Microsoft.ACE.OLEDB.12.0" for linked server "(null)" returned message "The Microsoft Access database engine cannot open or write to the file ''. It is already opened exclusively by another user, or you need permission to view and write its data.". Msg 7303, Level 16, State 1, Line 1 Cannot initialize the data source object of OLE DB provider "Microsoft.ACE.OLEDB.12.0" for linked server "(null)". If you get this error while Loading Data From Excel to SQL Server, then, close the Excel sheet opened and try to run queries again.

Talend ETL Part 1: SQL Server Database to Excel Sheet

Hello All, Of many ETL tools available in Market, One of the strong tool is Talend. Difference between other ETL tools and tools like Pentaho, Talend, Clover ETL, Adeptia Integration etc, is that they support NO SQL Cross domains, BIG Data, Hadoop etc. Other ETL tools like, SSIS, Informatica are now coming with their higher versions, which consists of Hadoop Integration. Basically We can say, there are two databases types. 1) RDBMS (Example: SQL Server, MySQL, Oracle etc) 2) Non RDBMS (Example: MongoDB, InfiniDB etc) Talend Supports Non RDBMS databases. Here I would like to share my hands on experience on Talend and how to use it and explain basic components of Talend. Approx there are 500 components we can find in Talend. So lets Kick Start from Basics. First lets try to load Data from Microsoft SQL Server to Excel. Steps: Step1: Open Talend Studio. Step 2: Right click on Job Design and Create a new Job by giving some job name. Step 3: Give the name o...