IBM Netezza Tutorials
Welcome to IBM Netezza Tutorials. The objective of these tutorials is to provide in depth understand of IBM Netezza.
In addition to free IBM Netezza Tutorials, we will cover common interview questions, issues and how to’s of IBM Netezza.
Netezza designs and markets high-performance data warehouse appliances and advanced analytics applications for uses including enterprise data warehousing, business intelligence, predictive analytics and business continuity planning.
IBM Netezza appliances are now part of IBM PureSystems - expert integrated systems with built in expertise, integration by design and a simplified user experience. Part of the Pure Data family, the Netezza appliance is now known as the Pure Data System for Analytics. It has the same key design tenets of simplicity, speed, scalability and analytics power that was fundamental to Netezza appliances.
With simple deployment, out-of-the-box optimization, no tuning and minimal on-going maintenance, the IBM Pure Data System for Analytics has the industry’s fastest time-to-value and lowest total-cost-of-ownership.
The Database Accelerator along with the other components of the IBM Netezza appliance was discussed during a short high-level overview of the architecture. This overview was presented at the beginning of the workshop during a brief presentation. The presentation also included the basic usage on how to administer and maintain a Netezza database. The concepts covered in the presentation were reinforced by getting hands on experience using a Netezza appliance. Instead of using an actual IBM Netezza appliance a virtualized environment was provided with a lab manual outlining the steps and commands to run.
What is Netezza used for ?
The IBM Netezza appliance also includes a SQL dialect called Netezza Structured Query Language (NZSQL). You can use SQL commands to create and manage your Netezza databases, user access, and permissions for the databases, as well as to query and modify the contents of the databases.
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IBM Netezza Analytics
Analytics is an embedded, purpose-built, advanced analytics platform — delivered with every IBM Netezza appliance — that empowers analytic enterprises to meet and exceed their business demands.
-Predict with more accuracy
-Deliver predictions faster
-Respond rapidly to changes
IBM Netezza Analytics’ advanced technology fuses data warehousing and in-database analytics into a scalable, high-performance, massively parallel advanced analytic platform that is designed to crunch through petascale data volumes. This allows users to ask questions of the data that could not have been contemplated on other architectures. IBM Netezza Analytics is designed to quickly and effectively provide better and faster answers to the most sophisticated business questions.
IBM Netezza Analytics is IBM Netezza’s most powerful advanced analytics platform that provides the technology infrastructure to support enterprise deployment of in-database analytics. The analytics platform allows integration of its robust set of built-in analytics with leading analytic tools from such vendors as Revolution Analytics, SAS, IBM SPSS®, Fuzzy Logix, and Zementis, on IBM Netezza’s core data warehouse appliances. IBM Netezza pioneered the modern data warehouse appliance and has customers worldwide that have realized the value of combining data warehousing and analytics into a single, high- performance integrated system. IBM Netezza Analytics enables analytic enterprises to realize significant business value from new business models and helps companies realize both top-line revenue growth and bottom- line cost savings.
IBM Netezza Analytics capabilities
-Data exploration and discovery
IBM Netezza Architecture
The IBM Netezza data warehouse appliance — a powerful parallel computing platform — is fully exploited by IBM Netezza Analytics to deliver high-speed, scalable analytics processing. The appliance uses the high-speed throughput of the Asymmetric Massively Parallel Processing (AMPP) architecture to maximize speed and efficiency for in-database analytics processing. The AMPP architecture is a blade-based streaming architecture that uses commodity blades and storage, combined with IBM Netezza’s patented data filtering using field programmable gate arrays (FPGAs), to deliver large data, high speed analytics. IBM Netezza has consolidated all analytics activity in a powerful and simple appliance.
IBM Netezza Analytics is purpose-built to simplify the building and deploying of models for analytic enterprises that demand the highest performance on large, complex volumes of data.
Easy to use
The IBM Netezza data warehouse appliance is easy-to-use and dramatically accelerates the entire analytic process. The programming interfaces and parallelization options make it straightforward to move a majority of analytics inside the appliance, regardless of whether they are being performed using tools from such vendors as IBM SPSS, SAS, or Revolution Analytics, or written in languages such as Java, Lua, Perl, Python, R or Fortran. Additionally, IBM Netezza data warehouse appliances are delivered with a built-in library of parallelized analytic functions, purpose-built for large data volumes, to kick-start and accelerate any analytic application development and deployment.
The simplicity and ease of development is what truly sets IBM Netezza apart. It is the first appliance of its kind – packing the power and scalability of hundreds of processing cores in an architecture ideally suited for parallel analytics. Instead of a fragmented analytics infrastructure with multiple systems where data is replicated, IBM Netezza Analytics consolidates all analytics activity in a powerful appliance. It is easy to deploy and requires minimal ongoing administration, for an overall low total cost of ownership.
Simplifying the process of exploring, calculating, modeling and scoring data are key drivers for successful adoption of analytics company wide. With IBM Netezza, business users can run their own analytics in near real time, which helps analytics-backed, data-driven decisions to become pervasive throughout an enterprise.
Check out the top Netezza Interview Questions now!
Netezza SQL functional categories
All SQL commands belong to one of the following functional categories:
- Data Definition Language (DDL)
- Data Control Language (DCL)
- Data Manipulation Language (DML)
- Transaction Control
- Miscellaneous commands
Data Definition Language (DDL)
Use the IBM Netezza SQL Data Definition Language (DDL) to define, modify, and delete databases objects, such as databases, tables, and views.
Data Control Language (DCL)
As database security administrator, you use Data Control Language (DCL) SQL commands to control user access to database objects and their contents.
Data Manipulation Language (DML)
Use Data Manipulation Language (DML) of SQL to access and modify database data by using the select, update, insert, delete, truncate, begin, commit, and rollback commands.
Transaction control enforces database integrity by ensuring that batches of SQL operations run completely or not at all. The transaction control commands are BEGIN, COMMIT, and ROLLBACK.
Functions and operators
IBM Netezza SQL provides many functions and operators. Functions are operations that take a value, whereas operators are symbols.
In many cases, you can use functions and operations to do the same task, so the difference is commonly with the syntax.
Netezza SQL supports the following types of functions:
Performs mathematical operations on numeric data
Manipulates strings of text
Date and time
Manipulates date and time values and extracts specific components from these values
Returns information specific to the RDBMS being used
Fuzzy search and phonetic matching
Provides approximate string matching that is based on defined techniques or algorithms.
Performs actions that are defined by the function developer
IBM Data Warehousing and Analytics Solutions
IBM provides the broadest and most comprehensive portfolio of data warehousing, information management and business analytic software, hardware and solutions to help customers maximize the value of their information assets and discover new insights to make better and faster decisions and optimize their business outcomes.