Oracle Express Edition Error – ORA-65096: invalid common user or role name

While trying to create user in Oracle Database 18c Express Edition I kept getting an “ORA-65096: invalid common user or role name” error, which didn’t make sense to me so after validating my command, that I was signed in as an admin user, and determining that my “CREATE USER” was formatted correctly.  I did some additional research and determined that in the hidden parameter “_ORACLE_SCRIPT” needed to be set to “True” starting with the Oracle Version 12c and higher.

Setting the “_ORACLE_SCRIPT” values

To set the “_ORACLE_SCRIPT” hidden variable to “True” you need to run an “Alter” command. Then you will be able to create the desired user and run your grants commands as usual.

Alter SQL Command

alter session set “_oracle_script”=true;

How to Determine Your Oracle Database Name

Oracle provides a few ways to determine which database you are working in.  Admittedly, I usually know which database I’m working in, but recently I did an Oracle Database Express Edition (XE) install which did not goes has expected and I had reason to confirm which database I was actually in when the SQL*Plus session opened.  So, this lead me to consider how one would prove exactly which database they were connected to.  As it happens, Oracle has a few ways to quickly display which database you are connected to and here are two easy ways to find out your Oracle database name in SQL*Plus:

  • V$DATABASE
  • GLOBAL_NAME

Checking the GLOBAL_NAME table

The First method is to run a quick-select against the GLOBAL_NAME table, which. is publicly available to logged-in users of the database

Example GLOBAL_NAME Select Statement

select * from global_name;

Checking the V$DATABASE Variable

The second method is to run a quick-select a V$database. However, not everyone will have access to the V$database variable.

Example V$database Select Statement

select name from V$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
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

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

What is a Common Data Model (CDM)?

Data Model, Common Data Model, CDM, What is a Common Data Model (CDM)
Data Model

What is a Common Data Model (CDM)?

A Common Data Model (CDM) is a share data structure designed to provide well-formed and standardized data structures within an industry (e.g. medical, Insurance, etc.) or business channel (e.g. Human resource management, Asset Management, etc.), which can be applied to provide organizations a consistent unified view of business information.   These common models can be leveraged as accelerators by organizations form the foundation for their information, including SOA interchanges, Mashup, data vitalization, Enterprise Data Model (EDM), business intelligence (BI), and/or to standardize their data models to improve meta data management and data integration practices.

Related references

IBM, IBM Analytics

IBM Analytics, Technology, Database Management, Data Warehousing, Industry Models

github.com

Observational Health Data Sciences and Informatics (OHDSI)/Common Data Model

Oracle

Oracle Technology Network, Database, More Key Features, Utilities Data Model

Oracle

Industries, Communications, Service Providers, Products, Data Mode, Oracle Communications Data Model

Oracle

Oracle Technology Network, Database, More Key Features, Airline data Model

Databases – Database Isolation Level Cross Reference

Database Type Isolation Levels Cross Reference
Database And Tables

 

Here is a table quick reference of some common database and/or connection types, which use connection level isolation and the equivalent isolation levels. This quick reference may prove useful as a job aid reference, when working with and making decisions about isolation level usage.

Database isolation levels

Data sources

Most restrictive isolation level

More restrictive isolation level

Less restrictive isolation level

Least restrictive isolation level

Amazon SimpleDB

Serializable Repeatable read Read committed Read Uncommitted

dashDB

Repeatable read Read stability Cursor stability Uncommitted read

DB2® family of products

Repeatable read Read stability* Cursor stability Uncommitted read

Informix®

Repeatable read Repeatable read Cursor stability Dirty read

JDBC

Serializable Repeatable read Read committed Read Uncommitted

MariaDB

Serializable Repeatable read Read committed Read Uncommitted

Microsoft SQL Server

Serializable Repeatable read Read committed Read Uncommitted

MySQL

Serializable Repeatable read Read committed Read Uncommitted

ODBC

Serializable Repeatable read Read committed Read Uncommitted

Oracle

Serializable Serializable Read committed Read committed

PostgreSQL

Serializable Repeatable read Read committed Read committed

Sybase

Level 3 Level 3 Level 1 Level 0

 

Related References