Friday, December 6, 2013

SQL: Top 10 SQL Query Tuning or SQL Optimization

SQL Tuning / SQL Optimization Techniques:

1) SQL query becomes faster if you use the actual columns names in SELECT statement instead of than '*'.
For Example: Write the query as
SELECT id, first_name, last_name, age, subject FROM student_details;
Instead of:
SELECT * FROM student_details; 

2) HAVING clause is used to filter the rows after all the rows are selected. It is just like a filter. Do not use HAVING clause for any other purposes.
For Example: Write the query as
SELECT subject, count(subject)
FROM student_details
WHERE subject != 'Science'
AND subject != 'Maths'
GROUP BY subject;

Instead of:
SELECT subject, count(subject)
FROM student_details
GROUP BY subject
HAVING subject!= 'Vancouver' AND subject!= 'Toronto';

3) Sometimes you may have more than one subqueries in your main query. Try to minimize the number of subquery block in your query.
For Example: Write the query as
SELECT name
FROM employee
WHERE (salary, age ) = (SELECT MAX (salary), MAX (age)
FROM employee_details)
AND dept = 'Electronics';

Instead of:
SELECT name
FROM employee
WHERE salary = (SELECT MAX(salary) FROM employee_details)
AND age = (SELECT MAX(age) FROM employee_details)
AND emp_dept = 'Electronics';

4) Use operator EXISTS, IN and table joins appropriately in your query.
   a) Usually IN has the slowest performance.
   b) IN is efficient when most of the filter criteria is in the sub-query.
   c) EXISTS is efficient when most of the filter criteria is in the main query.
For Example: Write the query as
Select * from product p
where EXISTS (select * from order_items o
where o.product_id = p.product_id)

Instead of:
Select * from product p
where product_id IN
(select product_id from order_items

5) Use EXISTS instead of DISTINCT when using joins which involves tables having one-to-many relationship.
For Example: Write the query as
SELECT d.dept_id, d.dept
FROM dept d
WHERE EXISTS ( SELECT 'X' FROM employee e WHERE e.dept = d.dept);

Instead of:
SELECT DISTINCT d.dept_id, d.dept
FROM dept d,employee e
WHERE e.dept = e.dept;

6) Try to use UNION ALL in place of UNION.
For Example: Write the query as
SELECT id, first_name
FROM student_details_class10
UNION ALL
SELECT id, first_name
FROM sports_team;

Instead of:
SELECT id, first_name, subject
FROM student_details_class10
UNION
SELECT id, first_name
FROM sports_team;

7) Be careful while using conditions in WHERE clause.
For Example: Write the query as
SELECT id, first_name, age FROM student_details WHERE age > 10;

Instead of:
SELECT id, first_name, age FROM student_details WHERE age != 10;

Write the query as
SELECT id, first_name, age
FROM student_details
WHERE first_name LIKE 'Chan%';

Instead of:
SELECT id, first_name, age
FROM student_details
WHERE SUBSTR(first_name,1,3) = 'Cha';

Write the query as
SELECT id, first_name, age
FROM student_details
WHERE first_name LIKE NVL ( :name, '%');

Instead of:
SELECT id, first_name, age
FROM student_details
WHERE first_name = NVL ( :name, first_name);

Write the query as
SELECT product_id, product_name
FROM product
WHERE unit_price BETWEEN MAX(unit_price) and MIN(unit_price)

Instead of:
SELECT product_id, product_name
FROM product
WHERE unit_price >= MAX(unit_price)
and unit_price <= MIN(unit_price)

Write the query as
SELECT id, name, salary
FROM employee
WHERE dept = 'Electronics'
AND location = 'Bangalore';

Instead of:
SELECT id, name, salary
FROM employee
WHERE dept || location= 'ElectronicsBangalore';

Use non-column expression on one side of the query because it will be processed earlier.

Write the query as
SELECT id, name, salary
FROM employee
WHERE salary < 25000;

Instead of:
SELECT id, name, salary
FROM employee
WHERE salary + 10000 < 35000;

Write the query as
SELECT id, first_name, age
FROM student_details
WHERE age > 10;

Instead of:
SELECT id, first_name, age
FROM student_details
WHERE age NOT = 10;

8) Use DECODE to avoid the scanning of same rows or joining the same table repetitively. DECODE can also be made used in place of GROUP BY or ORDER BY clause.
For Example: Write the query as
SELECT id FROM employee
WHERE name LIKE 'Ramesh%'
and location = 'Bangalore';

Instead of:
SELECT DECODE(location,'Bangalore',id,NULL) id FROM employee
WHERE name LIKE 'Ramesh%';

9) To store large binary objects, first place them in the file system and add the file path in the database.

10) To write queries which provide efficient performance follow the general SQL standard rules.
   a) Use single case for all SQL verbs
   b) Begin all SQL verbs on a new line
   c) Separate all words with a single space
   d) Right or left aligning verbs within the initial SQL verb

Source

Tuesday, November 12, 2013

SQL Server Date Formats

Standard Date Formats
Date Format
Standard
SQL Statement
Sample Output
Mon DD YYYY 1
HH:MIAM (or PM)
Default
SELECT CONVERT(VARCHAR(20), GETDATE(), 100)
Jan 1 2005 1:29PM 1
MM/DD/YY
USA
SELECT CONVERT(VARCHAR(8), GETDATE(), 1) AS [MM/DD/YY]
11/23/98
MM/DD/YYYY
USA
SELECT CONVERT(VARCHAR(10), GETDATE(), 101) AS [MM/DD/YYYY]
11/23/1998
YY.MM.DD
ANSI
SELECT CONVERT(VARCHAR(8), GETDATE(), 2) AS [YY.MM.DD]
72.01.01
YYYY.MM.DD
ANSI
SELECT CONVERT(VARCHAR(10), GETDATE(), 102) AS [YYYY.MM.DD]
1972.01.01
DD/MM/YY
British/French
SELECT CONVERT(VARCHAR(8), GETDATE(), 3) AS [DD/MM/YY]
19/02/72
DD/MM/YYYY
British/French
SELECT CONVERT(VARCHAR(10), GETDATE(), 103) AS [DD/MM/YYYY]
19/02/1972
DD.MM.YY
German
SELECT CONVERT(VARCHAR(8), GETDATE(), 4) AS [DD.MM.YY]
25.12.05
DD.MM.YYYY
German
SELECT CONVERT(VARCHAR(10), GETDATE(), 104) AS [DD.MM.YYYY]
25.12.2005
DD-MM-YY
Italian
SELECT CONVERT(VARCHAR(8), GETDATE(), 5) AS [DD-MM-YY]
24-01-98
DD-MM-YYYY
Italian
SELECT CONVERT(VARCHAR(10), GETDATE(), 105) AS [DD-MM-YYYY]
24-01-1998
DD Mon YY 1
-
SELECT CONVERT(VARCHAR(9), GETDATE(), 6) AS [DD MON YY]
04 Jul 06 1
DD Mon YYYY 1
-
SELECT CONVERT(VARCHAR(11), GETDATE(), 106) AS [DD MON YYYY]
04 Jul 2006 1
Mon DD, YY 1
-
SELECT CONVERT(VARCHAR(10), GETDATE(), 7) AS [Mon DD, YY]
Jan 24, 98 1
Mon DD, YYYY 1
-
SELECT CONVERT(VARCHAR(12), GETDATE(), 107) AS [Mon DD, YYYY]
Jan 24, 1998 1
HH:MM:SS
-
SELECT CONVERT(VARCHAR(8), GETDATE(), 108)
03:24:53
Mon DD YYYY HH:MI:SS:MMMAM (or PM) 1
Default +
milliseconds
SELECT CONVERT(VARCHAR(26), GETDATE(), 109)
Apr 28 2006 12:32:29:253PM 1
MM-DD-YY
USA
SELECT CONVERT(VARCHAR(8), GETDATE(), 10) AS [MM-DD-YY]
01-01-06
MM-DD-YYYY
USA
SELECT CONVERT(VARCHAR(10), GETDATE(), 110) AS [MM-DD-YYYY]
01-01-2006
YY/MM/DD
-
SELECT CONVERT(VARCHAR(8), GETDATE(), 11) AS [YY/MM/DD]
98/11/23
YYYY/MM/DD
-
SELECT CONVERT(VARCHAR(10), GETDATE(), 111) AS [YYYY/MM/DD]
1998/11/23
YYMMDD
ISO
SELECT CONVERT(VARCHAR(6), GETDATE(), 12) AS [YYMMDD]
980124
YYYYMMDD
ISO
SELECT CONVERT(VARCHAR(8), GETDATE(), 112) AS [YYYYMMDD]
19980124
DD Mon YYYY HH:MM:SS:MMM(24h) 1
Europe default + milliseconds
SELECT CONVERT(VARCHAR(24), GETDATE(), 113)
28 Apr 2006 00:34:55:190 1
HH:MI:SS:MMM(24H)
-
SELECT CONVERT(VARCHAR(12), GETDATE(), 114) AS [HH:MI:SS:MMM(24H)]
11:34:23:013
YYYY-MM-DD HH:MI:SS(24h)
ODBC Canonical
SELECT CONVERT(VARCHAR(19), GETDATE(), 120)
1972-01-01 13:42:24
YYYY-MM-DD HH:MI:SS.MMM(24h)
ODBC Canonical
(with milliseconds)
SELECT CONVERT(VARCHAR(23), GETDATE(), 121)
1972-02-19 06:35:24.489
YYYY-MM-DDTHH:MM:SS:MMM
ISO8601
SELECT CONVERT(VARCHAR(23), GETDATE(), 126)
1998-11-23T11:25:43:250
DD Mon YYYY HH:MI:SS:MMMAM 1
Kuwaiti
SELECT CONVERT(VARCHAR(26), GETDATE(), 130)
28 Apr 2006 12:39:32:429AM 1
DD/MM/YYYY HH:MI:SS:MMMAM
Kuwaiti
SELECT CONVERT(VARCHAR(25), GETDATE(), 131)
28/04/2006 12:39:32:429AM
Here are some more date formats that does not come standard in SQL Server as part of the CONVERT function.
Extended Date Formats
Date Format
SQL Statement
Sample Output
YY-MM-DD
SELECT SUBSTRING(CONVERT(VARCHAR(10), GETDATE(), 120), 3, 8) AS [YY-MM-DD]
SELECT REPLACE(CONVERT(VARCHAR(8), GETDATE(), 11), '/', '-') AS [YY-MM-DD]
99-01-24
YYYY-MM-DD
SELECT CONVERT(VARCHAR(10), GETDATE(), 120) AS [YYYY-MM-DD]
SELECT REPLACE(CONVERT(VARCHAR(10), GETDATE(), 111), '/', '-') AS [YYYY-MM-DD]
1999-01-24
MM/YY
SELECT RIGHT(CONVERT(VARCHAR(8), GETDATE(), 3), 5) AS [MM/YY]
SELECT SUBSTRING(CONVERT(VARCHAR(8), GETDATE(), 3), 4, 5) AS [MM/YY]
08/99
MM/YYYY
SELECT RIGHT(CONVERT(VARCHAR(10), GETDATE(), 103), 7) AS [MM/YYYY]
12/2005
YY/MM
SELECT CONVERT(VARCHAR(5), GETDATE(), 11) AS [YY/MM]
99/08
YYYY/MM
SELECT CONVERT(VARCHAR(7), GETDATE(), 111) AS [YYYY/MM]
2005/12
Month DD, YYYY 1
SELECT DATENAME(MM, GETDATE()) + RIGHT(CONVERT(VARCHAR(12), GETDATE(), 107), 9) AS [Month DD, YYYY]
July 04, 2006 1
Mon YYYY 1
SELECT SUBSTRING(CONVERT(VARCHAR(11), GETDATE(), 113), 4, 8) AS [Mon YYYY]
Apr 2006 1
Month YYYY 1
SELECT DATENAME(MM, GETDATE()) + ' ' + CAST(YEAR(GETDATE()) AS VARCHAR(4)) AS [Month YYYY]
February 2006 1
DD Month 1
SELECT CAST(DAY(GETDATE()) AS VARCHAR(2)) + ' ' + DATENAME(MM, GETDATE()) AS [DD Month]
11 September 1
Month DD 1
SELECT DATENAME(MM, GETDATE()) + ' ' + CAST(DAY(GETDATE()) AS VARCHAR(2)) AS [Month DD]
September 11 1
DD Month YY 1
SELECT CAST(DAY(GETDATE()) AS VARCHAR(2)) + ' ' + DATENAME(MM, GETDATE()) + ' ' + RIGHT(CAST(YEAR(GETDATE()) AS VARCHAR(4)), 2) AS [DD Month YY]
19 February 72 1
DD Month YYYY 1
SELECT CAST(DAY(GETDATE()) AS VARCHAR(2)) + ' ' + DATENAME(MM, GETDATE()) + ' ' + CAST(YEAR(GETDATE()) AS VARCHAR(4)) AS [DD Month YYYY]
11 September 2002 1
MM-YY
SELECT RIGHT(CONVERT(VARCHAR(8), GETDATE(), 5), 5) AS [MM-YY]
SELECT SUBSTRING(CONVERT(VARCHAR(8), GETDATE(), 5), 4, 5) AS [MM-YY]
12/92
MM-YYYY
SELECT RIGHT(CONVERT(VARCHAR(10), GETDATE(), 105), 7) AS [MM-YYYY]
05-2006
YY-MM
SELECT RIGHT(CONVERT(VARCHAR(7), GETDATE(), 120), 5) AS [YY-MM]
SELECT SUBSTRING(CONVERT(VARCHAR(10), GETDATE(), 120), 3, 5) AS [YY-MM]
92/12
YYYY-MM
SELECT CONVERT(VARCHAR(7), GETDATE(), 120) AS [YYYY-MM]
2006-05
MMDDYY
SELECT REPLACE(CONVERT(VARCHAR(10), GETDATE(), 1), '/', '') AS [MMDDYY]
122506
MMDDYYYY
SELECT REPLACE(CONVERT(VARCHAR(10), GETDATE(), 101), '/', '') AS [MMDDYYYY]
12252006
DDMMYY
SELECT REPLACE(CONVERT(VARCHAR(10), GETDATE(), 3), '/', '') AS [DDMMYY]
240702
DDMMYYYY
SELECT REPLACE(CONVERT(VARCHAR(10), GETDATE(), 103), '/', '') AS [DDMMYYYY]
24072002
Mon-YY 1
SELECT REPLACE(RIGHT(CONVERT(VARCHAR(9), GETDATE(), 6), 6), ' ', '-') AS [Mon-YY]
Sep-02 1
Mon-YYYY 1
SELECT REPLACE(RIGHT(CONVERT(VARCHAR(11), GETDATE(), 106), 8), ' ', '-') AS [Mon-YYYY]
Sep-2002 1
DD-Mon-YY 1
SELECT REPLACE(CONVERT(VARCHAR(9), GETDATE(), 6), ' ', '-') AS [DD-Mon-YY]
25-Dec-05 1
DD-Mon-YYYY 1
SELECT REPLACE(CONVERT(VARCHAR(11), GETDATE(), 106), ' ', '-') AS [DD-Mon-YYYY]
25-Dec-2005 1
1 To make the month name in upper case, simply use the UPPER string function.

Friday, November 8, 2013

OBIEE 11g: Bridge Tables and Many to Many Relationships in OBIEE 11g


by John Cook

Bridge tables - entire books have been devoted to this concept, countless blogs write about it, and organizations offer entire classes dedicated to demystifying this idea. Ralph Kimball, creator of Kimball Dimensional Modeling and founder of the Kimball Group has written quite a few great articles discussing the theory of bridge tables.

Yet when researching for comprehensive guides on how to actually implement a bridge table in OBIEE 11g, the documentation available is either:
  • Out of date
    • Contains implementation steps for OBIEE 10g which has since been deprecated
    • Does not contain adequate detail 
      • e.g. missing key steps
This guide is going to outline the basic use case of a many to many relationship, how OBIEE 11g resolves this dilemma and how to successfully implement a bridge table model within the 11g platform.

First thing's first - what is a bridge table and why do we need it?

At its core, bridge table solve the many to many relationship we encounter in many datasets. Many to many relationships in itself are not "bad", but when attempting to conform a data set to a star schema - many to many relationships just do not work. Star schemas assume a one to many (1:N) cardinality from the dimension to the fact. This means "one attribute of a dimension row can be found in many rows of the fact table".

For Example:
  • One job (job dimension) can be performed by many people
    • You would see the same JOB_WID repeating in the fact table
  • One employee (employee dimension) can have many jobs
    • You would see the same EMPLOYEE_WID  repeating in the fact table
  • One call at a call center(ticket dimension) can have many ticket types
    • You would see the same CALL_WID repeating in the fact table
  • One patient (patient dimension) can have many diagnosis
    • You would see the same PATIENT_WID repeating in the fact table

This 1:N cardinality is represented in OBIEE as (using call center/employee example) :
"Cardinality of '1' applied to the dimension and cardinality of 'N' applied to the fact'

But what happens when in the above examples, the cardinality is actually N:N? 

For Example:
  • Many employees can have multiple jobs and each job can be performed by multiple employees
  • Many patients can have multiple diagnosis and each diagnosis can be 'assigned' to many patients
  • Many calls can have multiple call ticket types and each ticket type can belong to multiple calls
This many to many relationship is initially (and incorrectly) represented in OBIEE 11g as:
'Cardinality of '1' is applied to the two dimension tables and cardinality of 'N' is applied to the fact'


Any BI Architect should recognize the above model - it's a traditional star schema! If you stop here and decided to ignore the issue with your dataset and 'hope' OBIEE aggregates the model correctly, you're about to be disappointed.

Why star schemas dont work for N:N cardinality

Consider the following scenario: You're a call center manager and you want to capture the number of calls with positive feedback per employee. You also want to capture the type of calls an employee answers in any given day.

Upon analysis of the requirements you conclude that "each call received by an employee can have many call types and each call type can be answered by multiple employees".

For example:
  • I answer a take a call that is classified as a 'new call', 'urgent', and 'out of state transfer' (three different call types) - this is the "each call received by an employee can have many call types".
  • A colleague also received a phone call that is classified as 'out of state transfer' - this is the 'each call type can be answered by multiple employees"

Now let's put this data in a traditional star schema fact table as modeled below:

ID EMPLOYEE_WID CALL_TYPE_WID  NUMBER_OF_GOOD_CALLS
1 1 1 300
2 1 2 300
3 1 3 300
4 2 2 500
5 2 3 500
6 3 1 200

You can see in the above data set that:

  • EMPLOYEE 1:
    • Has 3 different call types
    • Has 300 positive reviews (NUMBER_OF_GOOD_CALLS) 
      • This metric is at the EMPLOYEE level and not the call type level!
  • EMPLOYEE 2:
    • Has 2 different call types
    • Has 500 positive reviews (NUMBER_OF_GOOD_CALLS)
      • This metric is at the EMPLOYEE level and not the call type level
  • EMPLOYEE 3:
    • Has 1 different call type
      • Has 200 positive reviews (NUMBER_OF_GOOD_CALLS)
Now you receive a requirement to create a KPI that displays the Number of Good Calls as a stand alone widget.

PROBLEM 1 - Aggregation :
The number of good calls you received based on the above fact table is not 2100 - it's 300 + 500 + 200 = 1000

  • Employee 1 received 300 good calls
  • Employee 2 received 500 good calls
  • Employee 3 received 200 good calls
but due to the many to many cardinality of the data, the star schema duplicates the measures because each employee can take calls of many call types and each call type can be assigned to many employees!

PROBLEM 2 - Grand Totaling:

What if you don't care about aggregates? What if you just want a report that contains the employee, call type and a summation/grand total?

Notice how NUMBER_OF_GOOD_CALLS is repeated across multiple call types and the grand total is still incorrect. It's being duplicated due to the many to many relationship that exists between call type and employee. Furthermore, it paints an incorrect picture that NUMBER_OF_GOOD_CALLS is some how related to CALL_TYPE

How do we resolve this many to many cardinality with a bridge table?

When all is said and done, the incorrectly built star schema:

should be modified to:

Let's break this down:

The bridge table:

This the purpose of the bridge table is to resolve the many to many relationship between the call type and employee. It will contain, at a minimum, the following four columns:
  1. The primary key of the table
  2. The EMPLOYEE_WID
  3. The CALLTYPE_WID
  4. The weight factor
The weight factor is what's going to resolve the issue of double counting. 
  • If an employee has 3 call types, there would be 3 rows and the weight factor of each row would be .33
  • If an employee has 10 call types, there would be 10 rows and the weight factor of each row would be .1
In our bridge table data set, we're going to use the same 3 EMPLOYEE_WIDs and create the following:

ID CALL_TYPE_WID EMPLOYEE_WID  WEIGHT
11 1 1 0.33
12 2 1 0.33
13 3 1 0.33
23 2 2 0.5
24 3 2 0.5
31 1 3 1
You can see from this example that we've taken the N:N dataset in the fact table and moved it into this bridge.

The dimension that is joined to both the fact and bridge

This is a generic dimension that contains the unique EMPLOYEE IDs in your organization's dataset.
For example:
ID EMPLOYEE_ID
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10

The dimension that is joined to only the bridge table

This dimension contains all of the possible call types. Note how this table is not physically joined to the fact. This is because this specific dimension (CALL_TYPE) is what's causing the N:N cardinality
For example:
ID DESC
1 Call Type 1
2 Call Type 2
3 Call Type 3
4 Call Type 4
5 Call Type 5
6 Call Type 6
7 Call Type 7
8 Call Type 8
9 Call Type 9
10 Call Type 10

The Fact Table

We've moved the N:N cardinality from the original fact table to the bridge table so the new fact table now contains exactly one row per employee and does not have the CALL_TYPE_WID.

ID EMPLOYEE_WID NUMBER_OF_GOOD_CALLS
1 1 300
2 2 500
3 3 200

How do we implement this model in OBIEE 11g?

Step 1: Import Tables into Physical Layer

This is always the first step performed when creating a model regardless of its type. In the above example i'm importing four tables:
Step 2: Create the Physical Data Model
Based on our data set above the join conditions would be implemented as follows:
  • 1:N relationship from employee dimension to fact table
  • 1:N relationship from employee dimension to bridge
  • 1:N relationship from call type dimension to bridge
Notice how employee_demo_d is the only dimension that is joined to the fact. w_call_type_d is not joined to the fact because that is the dimension that is causing the many to many relationship issue.


Step 3:  Create the Logical Data Model
The creation of the BMM is where we deviate from our standard build steps of a traditional star schema:

  1. All associated dimension tables referencing the bridge table will be stored in a single BMM table
  2. The single BMM table will have two logical table source

Step 3.1 : Drag the fact table and dimension table that is connected to the fact table into the BMM. 
In our example, we are dragging w_calls_f and w_employee_demo_d into the BMM:




Step 3.2: Create a 2nd LTS in the existing dimension table


  1. Right click W_EMPLOYEE_DEMO_D -> New Object -> New Logical Table Source
  2. Name it 'Bridge'
  3. Add W_BRIDGE_D and W_CALLTYPE_DEMO_D (the two dimensions not directly joined to the fact table) under the 'Map to these tables' section

  1. Next add the remaining dimension columns from W_CALLTYPE_DEMO_D and W_BRIDGE_DEMO_D to the Dimension table in the BMM

Step 3.3: Create a level-based dimension hierarchy for the dimension BMM
  1. This step should be completed whether or not the schema is a star or bridge

Step 3.4: Confirm the BMM model has a 1:N relationship from the dimension to fact
Step 3.5: Set aggregation rule of NUMBER_OF_GOOD_CALLS to sum 
All measures in the BMM must have a mathematical operation applied to the column

Step 3.5: Set the Content level of the dimension table to 'detail' in within the LTS of the fact table
Again, this is something that should always take place regardless of the type of model

Step 4: Create the Presentation Layer
This part is straight forward, just drag the folders from the BMM into the new subject area:

The moment of truth
So why did we go through this elaborate exercise again? To fix the aggregation issues we were having with NUMBER_OF_GOOD_CALLS due to the N:N cardinality of the data set. Let's create that 'standalone KPI' Number of Good Calls:

Notice how the metric correctly sums to 1000. Let's check the back end physical query to confirm:
Notice how it's hitting the fact table and not the bridge or the call type dimension. 
But what about the weight factor?
Let's go back to the scenario where we want to compare across dimensions joined via the bridge table (EMPLOYEE and CALL_TYPE):
  • When creating a report that uses a measure from the fact table, a dimension value from the the employee table, and a dimension value from the table that causes the N:N cardinality - you need to use the weight factor to make sure your measure isn't getting double or triple counted:

  • Notice column is using the the NUMBER_OF_GOOD_CALLS multiplied by the WEIGHT factor in column 2
  • Each row in column 1 correctly represents the NUMBER_OF_GOOD_CALLS in the fact table despite having the repeated values of multiple call types
  • Note the aggregation of grand total sums to 997. This is because the weight factor is rounded to the 2nd decimal for EMPLOYEE_WID = 1 (.33%)
In order for grand totaling to work correctly with bridge table measures that use weight facts you must set the aggregation rule of the column (in this case column 1) to sum within Answers:



So what did we accomplish in this guide?
  • A basic understanding of many to many (N:N) cardinality
  • A basic understanding of why the star schema won't work for N:N cardinality
  • How to resolve the cardinality issue with a bridge table
  • How to implement a bridge table in OBIEE 11g
Source

Tuesday, October 29, 2013

OBIEE: Calculating First Day of Year, Quarter, Month in Answers / Analytics

The difference between syntax is highlighted:

First Day of Calendar Year: TIMESTAMPADD(SQL_TSI_DAY, -1*(DAYOFYEAR(CURRENT_DATE )-1) , CURRENT_DATE )
First Day of Calendar Quarter: TIMESTAMPADD(SQL_TSI_DAY, -1*(DAY_OF_QUARTER(CURRENT_DATE )-1) , CURRENT_DATE )
First Day of Calendar Month: TIMESTAMPADD(SQL_TSI_DAY, -1*(DAYOFMONTH(CURRENT_DATE )-1) , CURRENT_DATE )

Hope this helped!

Thursday, October 17, 2013

SQL: Formatting SQL Statement

Key components of Data Manipulation Language (DML) SQL statement's format:

SELECT
INSERT
UPDATE
DELETE
SELECT
INSERT INTO
VALUES
UPDATE
SET
WHERE
DELETE
FROM
WHERE
FROM
INSERT INTO
SELECT
FROM
WHERE


WHERE
AND
OR



GROUP BY



HAVING
AND
OR



ORDER BY




Hope this helped!

Tuesday, October 1, 2013

Business Intelligence Project Information Gathering Template

      Please note that this templates are created in MS OneNote.
      This template illustrates a basic technical information gathering for a new BI project.

      Contact
      File Transfer Process
      File Name
        1. John Doe
      Sample_File.csv

      Project/Metric Name
      Source File Name
      Data-Load Frequency
      Data-Load Type
      File Transfer Method
      Product Count
      SampleFile.csv
      Daily
      Full / Increment
      Upload

      • Database Table:
        • T_DIMENSION_TABLE_D
        • T_FACT_TABLE_F
      • Date Column:
        • ACTIVITY_DATE
          • Measures the metrics over certain period of time. i.e. MTD, QTD, YTD
      • Hierarchy / Drills:
        • Country > State > County > City > Zip Code
      • Filters:
        • Zip Code, First_Name, Last_Name etc...
      • Subject Area / Cube Name:
        • Sample Sales
      • Metrics:
        • A / B = C

      • Meeting Minutes:
        YYYY-MM-DD
        • Abc
        • Action item needs to be completed and colored into black
        YYYY-MM-DD
        • Abc