Netezza

Netezza Date Format and Conversions

Date data types are used to store the date and time fraction values. Netezza Date format includes four data types, and are used to store the date with time details: DATE = for year, month, day storage. TIME = for hour, minute, second, fraction with (includes 6 decimal positions). TIME WITH TIME ZONE / TIMETZ = same as TIME, also includes time zone information. TIMESTAMP = for year, month, day, hour, minute, second, fraction ( includes 6 decimal positions). Netezza Date Format Conversion Netezza can convert quoted date strings values…

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September 22, 2016
Netezza

Netezza String Functions and its Usage with Examples

Netezza String Functions are used primarily for string manipulation. An IBM Netezza also supports some of the standard string function along with the PostgreSQL specific functions. Netezza String Function Usage Below is the list of Netezza String functions supported: Function Name Description ascii(s) Returns the numeric ASCII value of the first character in the text string. btrim(s) Trims spaces from both ends of the string. btrim(s,t) Trims occurrences of the characters in string t from bothe ends of string s chr(n) Returns the character with the specified ASCII value. initcap(s) Capitalizes the…

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September 21, 2016
Netezza

Netezza Extract Function Usage and Examples

Netezza extract function extracts the sub field represented by units from the date/time value, interval, or duration specified for column. This function is equivalent to Netezza date_part() function. Netezza Extract Function Syntax extract(units FROM col) Netezza Extract Function Usage Below table represents the descriptions of the different units used in extract function: Unit Value Description epoch The number of seconds since 1970-01-01 00:00:00-00. The value can be positive or negative. millennium/millenniums The millennium value. century/centuries The number of full 100-year periods represented by the year. decade/decades The number of full 10-year periods represented by the…

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September 20, 2016
Netezza

Netezza Hadoop Integration and different types of Ingestion

Big Data and Netezza are two terms you hear lot about when you are working with loads of data. You want to process bunch of data and perform analytics on same. Sometimes it comes to raw data as well; you may get requirement to perform the analytics on the semi-structured data or unstructured data. Netezza Hadoop Integration comes into picture. So now question is how can you perform low latency data analytics on above mentioned data sets?Answer is Netezza Hadoop integration. Process the semi-structured or unstructured data in Hadoop and ingest…

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September 19, 2016
Netezza

Netezza Hadoop Connector and its Usage

Netezza Hadoop connector for Sqoop is an implementation of the Sqoop connector interfaces for accessing a Netezza data warehouse appliance from Hadoop cluster. Yom can export and import he data to a Hadoop cluster from various Netezza data warehouse environment. Netezza Hadoop connector is designed to use Netezza high-throughput data-transfer mechanisms to import and export data to Hadoop HDFS. This Connector for Netezza is a standard Sqoop extension that allows Sqoop to inter operate with Netezza Data warehouse appliance through Netezza JDBC drivers. This connector is already Cloudera Hadoop distribution…

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September 19, 2016
Netezza

Explicit and implicit Netezza Type Casting With Examples

Netezza type casting is converting the value with one data type to other. There are two type of type casting: Implicit and Explicit type casting. Implicit Netezza Type Casting In an implicit type casting Netezza uses internal rules and try to evaluate the process statements that contain the mixed data types. This will be very helpful when using a function that expects a particular data type, or when issuing a query that compares values that are similar but of different data types. For example, Consider the below query that compare…

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September 17, 2016
Data Warehouse

Various Data Warehouse Design Approaches:Top-Down and Bottom-Up

Data Warehouse design approaches are very important aspect of building data warehouse. Selection of right data warehouse design could save lot of time and project cost. There are two different Data Warehouse Design Approaches normally followed when designing a Data Warehouse solution and based on the requirements of your project you can choose which one suits your particular scenario. These methodologies are a result of research from Bill Inmon and Ralph Kimball. Bill Inmon - Top-down Data Warehouse Design Approach “Bill Inmon” is sometimes also referred to as the "father…

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September 17, 2016
Data Warehouse

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