Netezza

Netezza Create User Command and Examples

Use the Netezza CREATE USER command to define a new database user account. You can specify the parameters like password, expire date, default priority etc to the user while creating an account. Netezza Create User Command Syntax In the Netezza, username, database name and group name are unique. You cannot have same user as the group name. Syntax for creating a user: CREATE USER username [WITH [PASSWORD {'string' | NULL }] [SYSID uid] [ROWSETLIMIT [integer ] [IN GROUP groupname [, ...] ] [VALID UNTIL 'date' ] [SESSIONTIMEOUT [integer ] [QUERYTIMEOUT…

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May 14, 2017
Netezza

Netezza Regular Expression Functions and Examples

The Netezza regular expression functions identify precise patterns of characters and are useful for extracting string from the data and validation of the existing data, for example, validate date, range checks, checks for characters, and extract specific characters from the data. All these Netezza regular expressions are added in the Netezza SQL extension toolkit. Read: Download and install Netezza SQL toolkit extension IBM Netezza Update Join Syntax and Examples Netezza Extract Functions and Examples Netezza Extract Numbers using Regular Expressions In this article, we will check out some of the…

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May 12, 2017
Netezza

Netezza Extract Numbers using Regular Expressions

IBM has introduced some of new functions in its latest version of Netezza. The new features include bunch of useful regular expression. All these new functions are available as a part of Netezza SQL toolkit extension. In this article, we will discuss about Netezza extract numbers using regular expressions. Read: Download and install Netezza SQL toolkit extension Netezza Pivot Rows to Column With Example Netezza Extract Functions and Examples IBM Netezza Regular Expression Functions and Examples Netezza Extract Numbers using Regular Expressions Below are the some of the examples for Netezza…

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May 12, 2017
Netezza

Netezza Implicit Skew with an Example

Netezza Implicit Skew is the Netezza skew that occurs within the database when processing large data sets. The implicit Netezza skew are very difficult to identify. You can read more on Netezza Skew and How to avoid it. Netezza Implicit Skew Netezza implicit skew is occurs when data get redistributed or broadcasted on some other column to perform join operations. Data will be redistributed or broadcasted to perform co-located joins. The column on which data get redistributed or joined could be skewed that is, most of the redistributed data get inserted to…

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May 5, 2017
BigData

Different Hive Join Types and Examples

Join is a clause that is used for combining specific fields from two or more tables based on the common columns. The joins in the hive are similar to the SQL joins. Joins are used to combine rows from multiple tables. In this article, we will learn about different Hive join types with examples. Read: Hadoop Hive Bucket Concept and Bucketing Examples Hive Create Table Command and Examples Hive Create View Syntax and Examples Below are the tables that we will be using to demonstrate different Join types in Hive:…

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May 4, 2017
Data Warehouse

Data Warehouse Three-tier Architecture in Details

Usually, data warehouse adapts the three-tier architecture. In this article, we will discuss on the data warehouse three-tier architecture. You can read about read about two-tier architecture in my other post 'Data Warehouse Two-tier architecture in details' Data Warehouse Three-tier Architecture Following are the three-tiers of data warehouse architecture: Bottom Tier The bottom tier of the architecture is the data warehouse database server. It is usually the relational database (RDBMS) system. Data from operational databases and external sources are extracted using application program interfaces and ETL/ELT utilities. You generally use…

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May 4, 2017
BigData

Commonly used Hadoop Hive Commands and Examples

If you are already familiar with the SQL then Hive command syntax are easy to understand. In this article, we will discuss on the commonly used Hadoop Hive commands. Read: Cloudera Impala Generate Sequence Numbers without UDF Netezza ROWNUM Pseudo Column Alternative Run Impala SQL Script File Passing argument and Working Example  An Introduction to Hadoop Cloudera Impala Architecture Commonly used Hadoop Hive commands Below are the most commonly used Hadoop Hive commands: Hive Create Database A database is a collection of namespace in Hive. Below is the syntax to…

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May 3, 2017
Data Warehouse

Data Warehouse Physical Data Model

The Data warehouse physical data model describes how the model will be built in the database. A physical database model shows all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between tables. Read: Data Warehouse Project Life Cycle and Design Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Features of Data Warehouse Physical Data Model Following are…

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April 23, 2017
Data Warehouse

Data Warehouse Logical Data Model

A Data warehouse logical data model describes model in more detail compared to the Data Warehouse conceptual data model. A data warehouse logical data model describes the data in as much detail as possible, this model does not describe how the model is implemented. Read: Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Data Warehouse Three-tier Architecture in Details Features of Data Warehouse Logical Data…

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April 23, 2017
Data Warehouse

Data Warehouse Conceptual Data Model

A Data warehouse conceptual data model is nothing but a highest-level relationships between the different entities (in other word different table) in the data model. Read: Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Data Warehouse Three-tier Architecture in Details Features of Data Warehouse Conceptual Data Model Following are the features of conceptual data model: This is initial or high level relation between different entities…

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April 23, 2017