DWgeek.com

Different Extraction Methods in Data Warehouse

Extraction is the first step of the ETL(Extract, Transform and Load) process. Once the data is extracted, you can transform it and load to target data warehouse. Extraction is the process of extracting data from the source system for further use in the data warehouse environment. Related Reading: Data Warehouse Fact Constellation Schema and Design Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Data warehouse is an OLAP system, typically source system includes the transaction business processing application. For example, it could be sales order entry…

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September 16, 2016

Data Warehouse

Types of Dimension Tables in a Data Warehouse

A dimension is something that qualifies the quantity or measures. Dimensions store the textual descriptions of the business. With help of dimension you can easily identify the measures.The different types of dimension tables are available as below: Read: Types of Fact Tables in a Data Warehouse Data Warehouse Fact Constellation Schema and Design Data Warehouse Three-tier Architecture in Details Types of Dimension Tables in a Data Warehouse Below are the commonly used dimension tables in data warehouse: Conformed Dimension A conformed dimension is the dimension that is shared across multiple data…

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September 16, 2016

Data Warehouse

Types of Fact Tables in a Data Warehouse

A fact table stores quantitative information for analysis and is often denormalized. A fact table holds the measures, metrics and other quantifiable information. The different types of fact tables are as explained below: Read: Data Warehouse fact-less fact and Examples Slowly changing dimension Types of Dimension Tables in a Data Warehouse Types of Facts There are three types of facts: Additive Facts Additive facts can be used with any aggregation function like Sum(), Avg() etc. Example is Quantity, sales amount etc. Semi Additive Facts Semi-additive facts are those where only…

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September 16, 2016

Data Warehouse

Rapidly Changing Dimension (RCD) in Data Warehouse

A dimension is a fast changing or rapidly changing dimension if one or more of its attributes in the table changes very fast and in many rows. Handling rapidly changing dimension in data warehouse is very difficult because of many performance implications. As you know slowly changing dimension type 2 is used to preserve the history for the changes. But the problem with type 2 is, with each and every change in the dimension attribute, it adds new row to the table. If in case there are dimensions that are…

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September 16, 2016

Netezza

Netezza Hash Function Usage and Examples

You can use Netezza hash function to encode the data by transforming the input into a hash code or hash values. If you use the hash function, no two inputs have same hash values. The hash algorithm is designed to minimise the collision (two inputs having same hash value). You can use the hash function to speed up the data record retrieval by using simple one way lookup. Hash code is used as an index into a hash table which has a pointer to data records. Netezza hash function also…

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September 14, 2016

Netezza

Netezza Correlated Subquery and its Restrictions

Netezza Correlated subquery is a query within a query that refer the columns from the parent or outer query. Netezza does support the regular and correlated subqueries. In most cases, the Netezza correlated subqueries are used to improve the SQL query performance. Netezza Correlated Subquery Example For example, consider query, “check if department is already exists in the patient table before selecting it from patient department table”. We can correlate both tables and write sql query. You can write the SQL as below: SELECT T1.DEPT_CD , DATA_SRC_CD , T1.DEPT_NM FROM…

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September 14, 2016

Netezza

Download and Configure Netezza Linux ODBC Driver

This section describes you about downloading, configuring and testing Netezza Linux ODBC driver. This process is tested on the Ubuntu 14.04. Netezza Linux ODBC Drivers are requires to connect to Netezza server from any client machine. Read: Install vmware player and Netezza emulator on Ubuntu Connecting Netezza using Python pyodbc - Working ExampleNetezza FPGA nzsql Command and its Usage nzload command and its Usage Download Netezza Linux ODBC Driver You can download Netezza Linux ODBC Driver from Netezza fix central page. Below is url for your reference: https://www-945.ibm.com/support/fixcentral/ Note that, IBM…

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September 14, 2016

Netezza

Netezza Synonyms Best Practices and Examples

Netezza synonyms allow you to create easily typed names (you can remember and type easily) for long table, view names, or any other objects. You can create SQL synonyms as an alternate way of referencing tables or views that present in the current or other databases on the Netezza same system. Netezza Synonyms allow you to create another level of abstraction for the database objects (tables, views) and that allows and enable you to swap the underlying database objects without modifying the code that references these objects. You can even…

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September 13, 2016

Netezza

Netezza WITH Clause Syntax and Examples

If you have requirement to reuse the piece of query result in same query, then you can use the Netezza WITH clause. You can also use the WITH clause to improve the speed for Netezza complex sub-queries and improve overall performance. This is also called the sub query factoring and you can use it when sub query is called multiple times. You can use the WITH Clause command to run multiple sub queries in a SELECT statement. The main advantage of Netezza WITH clause is, you can use it wherever…

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September 12, 2016

General

Netezza Pivot Rows to Column With Example

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September 11, 2016