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

Netezza / PureData – How to add comments on a field

Netezza / PureData Table Documentation Practices
Netezza / PureData Table Documentation Practices

The ‘Comment on Column’ provides the same self-documentation capability as ‘Comment On table’, but drives the capability to the column field level.  This provides an opportunity to describe the purpose, business meaning, and/or source of a field to other developers and users.  The comment code is part of the DDL and can be migrated with the table structure DDL.  The statement can be run independently, or working with Aginity for PureData System for Analytics, they can be run as a group, with the table DDL, using the ‘Execute as a Single Batch (Ctrl+F5) command.

Basic ‘COMMENT ON field’ Syntax

  • The basic syntax to add a comment to add a comment to a column is:

COMMENT ON COLUMN <<Schema.TableName.ColumnName>> IS ‘<<Descriptive Comment>>’;

Example ‘COMMENT ON Field’ Syntax

  • This is example syntax, which would need to be changed and applied to each column field:

COMMENT ON COLUMN time_dim.time_srky IS ‘time_srky is the primary key and is a surrogate key derived from the date business/natural key’;

Related References

What are the Factor Affecting the Selection of Data Warehouse Naming Convention?

Data Warehouse Naming Convention
Database Table

The primary factors affecting the choices in the creation of Data Warehouse (DW) naming convention policy standards are the type of implementation, pattern of the implementation, and any preexisting conventions.

Type of implementation

The type of implementation will affect your naming convention choices. Basically, this boils down to, are you working with a Commercial-Off-The-Shelf (COTS) data warehouse or doing a custom build?

Commercial-Off-The-Shelf (COTS)

If it is a Commercial-Off-The-Shelf (COTS) warehouse, which you are modifying and or enhancing, then it is very strongly recommended that you conform to the naming conventions of the COTS product.  However, you may want to add an identifier to the conventions to identify your custom object.

Using this information as an exemplar:

  • FAV = Favinger, Inc. (Company Name – Custom Identifier)
  • GlobalSales = Global Sales (Subject)
  • MV = Materialized View (Object Type)

Suffix Pattern Naming Convention

<<Custom Identifier>>_<<Object Subject Name>>_<<Object Type>>

Example:  FAV_GlobalSales_MV

Prefix Pattern Naming Convention

<<Object Type>>_<<Custom Identifier>>_<<Object Subject Name>>

Example: MV_FAV_GlobalSales

Custom Data Warehouse Build

If you are creating a custom data warehouse from scratch, then you have more flexibility to choose your naming convention.  However, you will still need to take into account a few factors to achieve the maximum benefit from you naming conventions.

  • What is the high level pattern of you design?
  • Are there any preexisting naming conventions?

Data Warehouse Patterns

Your naming convention will need to take into account the overall intent and design pattern of the data warehouse, the objects and naming conventions of each pattern will vary, if for no other reason than the differences in the objects, their purpose, and the depth of their relationships.

High level Pattern of the Data Warehouse Implementation

The high level pattern of you design whether an Operational Data Store (ODS), Enterprise Data Warehouse (EDW), Data Mart (DM) or something else, will need to guide your naming convention, as the depth of logical and/or processing zone of each pattern will vary  and have some industry generally accepted conventions.

Structural Pattern of the Data Warehouse Implementation

The structural pattern of your data warehouse design whether, Snowflake, 3rd Normal Form, or Star Schema, will  need to guide your naming convention, as the depth of relationships each pattern will vary, have some industry generally accepted conventions, and will relate directly to you High level Data Warehouse pattern.

Preexisting Conventions

Often omitted factor of data warehouse naming conventions are the sources of preexisting conventions, which can have significant impacts both from an engineering and political point of view. The sources of these conventions can vary and may or may not be formally documented.

A common source naming convention conflict is with preexisting implementations, which may not even be document.  However, system objects and consumers are familiar will be exposed to these conventions, will need to be taken into account when accessing impacts to systems, political culture, user training, and the creation of a standard convention for your data warehouse.

The Relational Database Management System (RDBMS) in which you intend to build the data warehouse may have generally accepted conventions, which consumers may be familiar and have a preconceived expectations whether expressed or intended).

Change Management

Whatever data warehouse naming convention you chose, the naming conventions along with the data warehouse design patterns assumptions, should be well documented and placed in a managed and readily accessible, change management (CM) repository.

Related Reference

PureData – Table Effective Practices

IBM Netezza PureData Table Effective Practices
Database Table

Here a few tips, which can make a significant difference in the efficiency and effectiveness of developers and users, making information available to them when developing and creating analytic objects.  This information can, also, be very help to data modelers.  While some of these recommendations are not enforced by Netezza/PureData, this fact makes them no less helpful to your community.

Alter table to Identify Primary Keys (PK)

  • Visually helps developers and users to know what the keys primary keys of the table are
  • Primary key information can, also, be imported as metadata by other IBM tools (e.g. InfoSphere, Datastage, Data Architect, Governance Catalog, Aginity, etc.)
  • The query optimizer will use these definitions to define efficient query execution plans

Alter table to Identify Foreign Keys (FK)

  • Illustrate table relationships for developers and users
  • Foreign key information can, also, be imported as metadata by other IBM tools (e.g. InfoSphere, Datastage, Data Architect, Governance Catalog, Aginity, etc.)
  • The query optimizer will use these definitions to define efficient query execution plans

Limit Distribution Key to Non-Updatable Fields

  • This one seems obvious, but this problem occurs regularly if tables and optimizations are not properly planned; Causing an error will be generated, if an update is attempted against a field contained in the distribution of a table.

Use Null on Fields

  • Using ‘Not Null’ whenever the field data and ETL transformation rules can enforce it, helps improve performance by reducing the number of null condition checks performed and reduces storage.

Use Consistant Field Properties

  • Use the same data type and field length in all tables with the same field name reduces the amount of interpretation/conversion required by the system, developers, and report SQL.

Schedule Table Optimizations

  • Work with your DBA’s to determine the best scheduling time, system user, and priority of groom and generate statistics operations. Keep in mind the relationship to when the optimizations occur in relation to when users need to consume the data. All too often, this operation is not performed before users need the performance and/or is driven by DBA choice, without proper consideration to other processing performance needs.  This has proven, especially true, in data warehousing when the DBA does not have Data warehousing experience and/or does not understand the load patterns of the ETL/ELT process.

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