How to know if your Oracle Client install is 32 Bit or 64 Bit

Oracle Database, How to know if your Oracle Client install is 32 Bit or 64 Bit
Oracle Database

 

How to know if your Oracle Client install is 32 Bit or 64 Bit

Sometimes you just need to know if your Oracle Client install is 32 bit or 64 bit. But how do you figure that out? Here are two methods you can try.

The first method

Go to the %ORACLE_HOME%\inventory\ContentsXML folder and open the comps.xml file.
Look for <DEP_LIST> on the ~second screen.

If you see this: PLAT=”NT_AMD64” then your Oracle Home is 64 bit
If you see this: PLAT=”NT_X86” then your Oracle Home is 32 bit.

It is possible to have both the 32-bit and the 64-bit Oracle Homes installed.

The second method

This method is a bit faster. Windows has a different lib directory for 32-bit and 64-bit software. If you look under the ORACLE_HOME folder if you see a “lib” AND a “lib32” folder you have a 64 bit Oracle Client. If you see just the “lib” folder you’ve got a 32 bit Oracle Client.

Related References

 

OLTP vs Data Warehousing

OLTP Versus Data Warehousing

I’ve tried to explain the difference between OLTP systems and a Data Warehouse to my managers many times, as I’ve worked at a hospital as a Data Warehouse Manager/data analyst for many years. Why was the list that came from the operational applications different than the one that came from the Data Warehouse? Why couldn’t I just get a list of patients that were laying in the hospital right now from the Data Warehouse? So I explained, and explained again, and explained to another manager, and another. You get the picture.
In this article I will explain this very same thing to you. So you know  how to explain this to your manager. Or, if you are a manager, you might understand what your data analyst can and cannot give you.

OLTP

OLTP stands for OLine Transactional Processing. With other words: getting your data directly from the operational systems to make reports. An operational system is a system that is used for the day to day processes.
For example: When a patient checks in, his or her information gets entered into a Patient Information System. The doctor put scheduled tests, a diagnoses and a treatment plan in there as well. Doctors, nurses and other people working with patients use this system on a daily basis to enter and get detailed information on their patients.
The way the data is stored within operational systems is so the data can be used efficiently by the people working directly on the product, or with the patient in this case.

Data Warehousing

A Data Warehouse is a big database that fills itself with data from operational systems. It is used solely for reporting and analytical purposes. No one uses this data for day to day operations. The beauty of a Data Warehouse is, among others, that you can combine the data from the different operational systems. You can actually combine the number of patients in a department with the number of nurses for example. You can see how far a doctor is behind schedule and find the cause of that by looking at the patients. Does he run late with elderly patients? Is there a particular diagnoses that takes more time? Or does he just oversleep a lot? You can use this information to look at the past, see trends, so you can plan for the future.

The difference between OLTP and Data Warehousing

This is how a Data Warehouse works:

The data gets entered into the operational systems. Then the ETL processes Extract this data from these systems, Transforms the data so it will fit neatly into the Data Warehouse, and then Loads it into the Data Warehouse. After that reports are formed with a reporting tool, from the data that lies in the Data Warehouse.

This is how OLTP works:

Reports are directly made from the data inside the database of the operational systems. Some operational systems come with their own reporting tool, but you can always use a standalone reporting tool to make reports form the operational databases.

Pro’s and Con’s

Data Warehousing

Pro’s:

  • There is no strain on the operational systems during business hours
    • As you can schedule the ETL processes to run during the hours the least amount of people are using the operational system, you won’t disturb the operational processes. And when you need to run a large query, the operational systems won’t be affected, as you are working directly on the Data Warehouse database.
  • Data from different systems can be combined
    • It is possible to combine finance and productivity data for example. As the ETL process transforms the data so it can be combined.
  • Data is optimized for making queries and reports
    • You use different data in reports than you use on a day to day base. A Data Warehouse is built for this. For instance: most Data Warehouses have a separate date table where the weekday, day, month and year is saved. You can make a query to derive the weekday from a date, but that takes processing time. By using a separate table like this you’ll save time and decrease the strain on the database.
  • Data is saved longer than in the source systems
    • The source systems need to have their old records deleted when they are no longer used in the day to day operations. So they get deleted to gain performance.

Con’s:

  • You always look at the past
    • A Data Warehouse is updated once a night, or even just once a week. That means that you never have the latest data. Staying with the hospital example: you never knew how many patients are in the hospital are right now. Or what surgeon didn’t show up on time this morning.
  • You don’t have all the data
    • A Data Warehouse is built for discovering trends, showing the big picture. The little details, the ones not used in trends, get discarded during the ETL process.
  • Data isn’t the same as the data in the source systems
    • Because the data is older than those of the source systems it will always be a little different. But also because of the Transformation step in the ETL process, data will be a little different. It doesn’t mean one or the other is wrong. It’s just a different way of looking at the data. For example: the Data Warehouse at the hospital excluded all transactions that were marked as cancelled. If you try to get the same reports from both systems, and don’t exclude the cancelled transactions in the source system, you’ll get different results.

online transactional processing (OLTP)

Pro’s

  • You get real time data
    • If someone is entering a new record now, you’ll see it right away in your report. No delays.
  • You’ve got all the details
    • You have access to all the details that the employees have entered into the system. No grouping, no skipping records, just all the raw data that’s available.

Con’s

  • You are putting strain on an application during business hours.
    • When you are making a large query, you can take processing space that would otherwise be available to the people that need to work with this system for their day to day operations. And if you make an error, by for instance forgetting to put a date filter on your query, you could even bring the system down so no one can use it anymore.
  • You can’t compare the data with data from other sources.
    • Even when the systems are similar. Like an HR system and a payroll system that use each other to work. Data is always going to be different because it is granulated on a different level, or not all data is relevant for both systems.
  • You don’t have access to old data
    • To keep the applications at peak performance, old data, that’s irrelevant to day to day operations is deleted.
  • Data is optimized to suit day to day operations
    • And not for report making. This means you’ll have to get creative with your queries to get the data you need.

So what method should you use?

That all depends on what you need at that moment. If you need detailed information about things that are happening now, you should use OLTP.
If you are looking for trends, or insights on a higher level, you should use a Data Warehouse.

 Related References

Netezza / PureData – Table Describe SQL

Netezza Puredata Table Describe SQL
Netezza / Puredata Table Describe SQL

If you want to describe a PureData / Netezza table in SQL, it can be done, but Netezza doesn’t have a describe command.  Here is a quick SQL, which will give the basic structure of a table or a view.  Honestly, if you have Aginity Generating the DDL is fast and more informative, at least to me.  If you have permissions to access NZSQL you can also use the slash commands (e.g. \d).

Example Netezza Table Describe SQL

select  name as Table_name,

owner as Table_Owner,

Createdate as Table_Created_Date,

type as Table_Type,

Database as Database_Name,

schema as Database_Schema,

attnum as Field_Order,

attname as Field_Name,

format_type as Field_Type,

attnotnull as Field_Not_Null_Indicator,

attlen as Field_Length

from _v_relation_column

where

name='<<Table Name Here>>’

Order by attnum;

 

Related References

IBM Knowledge Center, PureData System for Analytics, Version 7.2.1

IBM Netezza database user documentation, Command-line options for nzsql, Internal slash options

IBM Knowledge Center, PureData System for Analytics, Version 7.2.1

IBM Netezza getting started tips, About the Netezza data warehouse appliance, Commands and queries, Basic Netezza SQL information, Commonly used nzsql internal slash commands

IBM Knowledge Center, PureData System for Analytics, Version 7.2.1

IBM Netezza database user documentation, Netezza SQL introduction, The nzsql command options, Slash options

 

 

Netezza / PureData – Substring Function Example

SQL (Structured Query Language), Netezza PureData – Substring Function Example, Substr
Netezza / PureData – Substring Function Example

The function Substring (SUBSTR) in Netezza PureData provides the capability parse character type fields based on position within a character string.

Substring Functions Basic Syntax

SUBSTRING Function Syntax

SUBSTRING(<<CharacterField>>,<< StartingPosition integer>>, <<for Number of characters Integer–optional>>)

 

SUBSTR Function Syntax

SUBSTR((<>,<< StartingPosition integer>>, <>)

 

Example Substring SQL

Netezza / PureData Substring Example
Netezza / PureData Substring Example

Substring SQL Used In Example

SELECT  LOCATIONTEXT

— From the Left Of the String

— Using SUBSTRING Function

,’==SUBSTRING From the Left==’ as Divider1

,SUBSTRING(LOCATIONTEXT,1,5) as Beggining_Using_SUBSTRING_LFT

,SUBSTRING(LOCATIONTEXT,7,6) as Middle_Using_SUBSTRING_LFT

,SUBSTRING(LOCATIONTEXT,15) as End_Using_SUBSTRING_LFT

,’==SUBSTR From the Left==’ as Divider2

—Using SUBSTR Function

 

,SUBSTR(LOCATIONTEXT,1,5) as Beggining_Using_SUBSTR_LFT

,SUBSTR(LOCATIONTEXT,7,6) as Middle_Using_SUBSTR_LFT

,SUBSTR(LOCATIONTEXT,15) as End_Using_SUBSTR_LFT

— From the right of the String

,’==SUBSTRING From the Right==’ as Divider3

,SUBSTRING(LOCATIONTEXT,LENGTH(LOCATIONTEXT)-18, 8) as Beggining_Using_SUBSTRING_RGT

,SUBSTRING(LOCATIONTEXT,LENGTH(LOCATIONTEXT)-9, 6) as Middle_Using_SUBSTRING_RGT

,SUBSTRING(LOCATIONTEXT,LENGTH(LOCATIONTEXT)-1) as End_Using_SUBSTRING_RGT

,’==SUBSTR From the right==’ as Divider4

,SUBSTR(LOCATIONTEXT,LENGTH(LOCATIONTEXT)-18, 8) as Beggining_Using_SUBSTR_RGT

,SUBSTR(LOCATIONTEXT,LENGTH(LOCATIONTEXT)-9, 6) as Middle_Using_SUBSTR_RGT

,SUBSTR(LOCATIONTEXT,LENGTH(LOCATIONTEXT)-1) as End_Using_SUBSTR_RGT

FROM BLOG.D_ZIPCODE

where STATE = ‘PR’

AND CITY = ‘REPTO ROBLES’;

Related References

IBM Knowledge Center, PureData System for Analytics, Version 7.2.1

IBM Netezza database user documentation, Netezza SQL basics, Netezza SQL extensions, Character string functions

IBM Knowledge Center, PureData System for Analytics, Version 7.1.0

IBM Netezza Database User’s Guide, Netezza SQL basics, Functions and operators, Functions, Standard string functions

IBM Knowledge Center, PureData System for Analytics, Version 7.2.1

IBM Netezza database user documentation, Netezza SQL command reference, Functions

Netezza / PureData – Substring Function On Specific Delimiter

SQL (Structured Query Language), Netezza / PureData - Substring Function On Specific Delimiter, substr
Netezza / PureData – Substring Function On Specific Delimiter

The function Substring (SUBSTR) in Netezza PureData provides the capability parse character type fields based on position within a character string.  However, it is possible, with a little creativity, to substring based on the position of a character in the string. This approach give more flexibility to the substring function and makes the substring more useful in many cases. This approach works fine with either the substring or substr functions.  In this example, I used the position example provide the numbers for the string command.

 

Example Substring SQL

Netezza PureData Substring Function On Specific Character In String, substring, substr
Netezza PureData Substring Function On Specific Character In String

 

Substring SQL Used In Example

select LOCATIONTEXT

,position(‘,’ in LOCATIONTEXT) as Comma_Postion_In_String

—without Adjustment

,SUBSTRING(LOCATIONTEXT,position(‘,’ in LOCATIONTEXT)) as Substring_On_Comma

—Adjusted to account for extra space

,SUBSTRING(LOCATIONTEXT,position(‘,’ in LOCATIONTEXT)+2) as Substring_On_Comma_Ajusted

,’==Breaking_Up_The_Sting==’ as Divider

— breaking up the string

,SUBSTRING(LOCATIONTEXT,1, position(‘ ‘ in LOCATIONTEXT)-1) as Beggining_of_String

,SUBSTRING(LOCATIONTEXT,position(‘ ‘ in LOCATIONTEXT)+1, position(‘ ‘ in LOCATIONTEXT)-1) as Middle_Of_String

,SUBSTRING(LOCATIONTEXT,position(‘,’ in LOCATIONTEXT)+2) as End_Of_String

 

FROM Blog.D_ZIPCODE

where STATE = ‘PR’

AND CITY = ‘REPTO ROBLES’

Related References

IBM Knowledge Center, PureData System for Analytics, Version 7.2.1

IBM Netezza database user documentation, Netezza SQL basics, Netezza SQL extensions, Character string functions

IBM Knowledge Center, PureData System for Analytics, Version 7.1.0

IBM Netezza Database User’s Guide, Netezza SQL basics, Functions and operators, Functions, Standard string functions

IBM Knowledge Center, PureData System for Analytics, Version 7.2.1

IBM Netezza database user documentation, Netezza SQL command reference, Functions

Netezza / PureData – Position Function

SQL (Structured Query Language), Netezza PureData Position Function, SQL, Position Function
Netezza / PureData Position Function

 

The position function in Netezza is a simple enough function, it just returns the number of a specified character within a string (char, varchar, nvarchar, etc.) or zero if the character not found. The real power of this command is when you imbed it with character function, which require a numeric response, but the character may be inconsistent from row to row in a field.

The Position Function’s Basic Syntax

position(<<character or Character String>> in <<CharacterFieldName>>)

 

Example Position Function SQL

Netezza PureData Position Function, SQL, Position Function
Netezza PureData Position Function

 

Position Function SQL Used in Example

select LOCATIONTEXT, CITY

,’==Postion Funtion Return Values==’ as Divider

,position(‘,’ in LOCATIONTEXT) as Postion_In_Nbr_String

,position(‘-‘ in LOCATIONTEXT) as Postion_Value_Not_Found

,’==Postion Combined with Substring Function==’ as Divider2

,SUBSTRING(LOCATIONTEXT,position(‘,’ in LOCATIONTEXT)+2) as Position_Used_in_Substring_Function

FROM Blog.D_ZIPCODE  where STATE = ‘MN’ AND CITY = ‘RED WING’ limit 1;

 

 

Related References

IBM Knowledge Center, PureData System for Analytics, Version 7.1.0

IBM Netezza Database User’s Guide, Netezza SQL basics, Functions and operators, Functions, Standard string functions

IBM Knowledge Center, PureData System for Analytics, Version 7.2.1

IBM Netezza database user documentation, Netezza SQL command reference, Functions

 

Data Modeling – Column Data Classification

Data Modeling, Column Data Classification, Field Data Classification
Data Modeling

Column Data Classification

When analyzing individual column data, at its most foundational level, column data can be classified by their fundamental use/characteristics.  Granted, when you start rolling up the structure into multiple columns, table structure and table relationship, then other classifications/behaviors, such as keys (primary and foreign), indexes, and distribution come into play.  However, many times when working with existing data sets it is essential to understand the nature the existing data to begin the modeling and information governance process.

Column Data Classification

Generally, individual columns can be classified into the classifications:

  • Identifier — A column/field which is unique to a row and/or can identify related data (e.g., Person ID, National identifier, ). Basically, think primary key and/or foreign key.
  • Indicator — A column/field, often called a Flag, that has a binary condition (e.g., True or False, Yes or No, Female or Male, Active or Inactive). Frequently used to identify compliance with complex with a specific business rule.
  • Code — A column/field that has a distinct and defined set of values, often abbreviated (e.g., State Code, Currency Code)
  • Temporal — A column/field that contains some type date, timestamp, time, interval, or numeric duration data
  • Quantity — A column/field that contains a numeric value (decimals, integers, etc.) and is not classified as an Identifier or Code (e.g., Price, Amount, Asset Value, Count)
  • Text — A column/field that contains alphanumeric values, possibly long text, and is not classified as an Identifier or Code (e.g., Name, Address, Long Description, Short Description)
  • Large Object (LOB)– A column/field that contains data traditional long text fields or binary data like graphics. The large objects can be broadly classified as Character Large Objects (CLOBs), Binary Large Objects (BLOBs), and Double-Byte Character Large Object (DBCLOB or NCLOB).

Related References