Netezza

Netezza Advanced Date Functions and Examples

Netezza Advanced Date Functions are available in Netezza SQL extensions tool kit. You have to download it from IBM fix central and install it in required database. You cannot access these functions otherwise. Read: how to download and Install Netezza SQL extensions toolkit These advanced functions are different than standard Netezza data time functions. You can read about the standard Netezza date functions from below post: Netezza Data Functions and Examples You may want to read: nzsql command and its Usage Netezza Analytics Functions and Examples IBM Netezza Conversion Functions…

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September 24, 2016
Netezza

Netezza Interval Data Types and Conversion Examples

Netezza interval data types are supported in different way. Netezza accepts the interval syntax, but ignores the unit specification. All intervals are the same, and can contain values of any combination of units. Netezza server internally normalizes all intervals to units of seconds. It considers a month to be 30 days for the purposes of interval comparisons and this approximation may lead to inaccuracy. Netezza Interval Data Types Implementation IBM Netezza implements the INTERVAL data type in a different way compared to other databases. Netezza allows you to specify interval…

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September 23, 2016
Netezza

Netezza Internal DataTypes: rowid, createxid, deletexid and datasliceid

Netezza supports internal datatypes that are used by system to perform internal tasks on the table records. Netezza internal datatypes includes: rowid, transaction ID (createxid, deletexid), and dataslice. Shows Netezza internal Datatypes: Internal Datatype Column Name rowid rowid transaction ID Createxid deletexid dataslice datasliceid Netezza Internal DataTypes: Rowid Identifies a specific and unique record in the database. These row numbers are not necessarily sequential within a table. Usually, the initial rowid value is 100,000. The Netezza host assigns a range of sequential rowids to each SPU in the system. When…

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September 23, 2016
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