Netezza Tutorial

For applications like enterprise data warehousing, business intelligence, predictive analytics, and business continuity planning, Netezza designs and markets high-performance data warehouse equipment and sophisticated analytics applications. To track the market share of over 12,500 technology items, including Data Storage Hardware, we use the latest indexing strategies combined with advanced data science. You can gain deep insights into any organization by analyzing billions of public records, with an average of over 100 data fields per company. Netezza has a market share of around 3.8% in the Data Storage Hardware segment. This Netezza tutorial incorporates the topics of introduction to Netezza, what is Netessa and why is Netezza, IBM Netezza Analytics, the architecture of IBM Netezza, easy to use. In this Netezza Online Course Tutorial, you will also learn the concepts such as SQL functional categories of Netezza, functions and operators, function developer actions, key features of Netezza. By the end of this Netezza Online Tutorial, you will be able to clearly understand the concepts such as Netezza host architecture, Field Programmable Gate arrays, Snippet Blades, Disk Enclosure, Netezza data warehouse, etc. Let us now start digging into the concepts of the Netezza Tutorial Course in detail.

Introduction to Netezza

The Netezza Structured Query Language (SQL), which resides on the Netezza data warehouse appliance, is Netezza SQL. The phrase SQL refers to Netezza's SQL implementation in this document. For applications like enterprise data warehousing, business intelligence, predictive analytics, and business continuity planning, Netezza designs and markets high-performance data warehouse appliances and advanced analytics applications.

IBM Netezza appliances are now part of IBM Pure Systems, an advanced systems specialist with built-in skills, design integration, and a streamlined user interface. The Netezza appliance is now known as the Pure Data Framework for Analytics, part of the Pure Data family. It has the same main design principles that were essential to Netezza appliances in terms of simplicity, speed, scalability, and analytical capacity. The IBM Pure Data System for Analytics has the fastest time-to-value and lowest overall cost-of-ownership in the industry with its quick implementation, out-of-the-box optimization, no tuning, and minimal ongoing maintenance.

During a brief high-level overview of the architecture, the Database Accelerator and the other modules of the IBM Netezza appliance were discussed. During a brief lecture, this summary was given at the start of the session. The presentation also included the basic usage of how to administer and maintain a Netezza database. By having hands-on experience using a Netezza appliance, the topics discussed in the presentation were improved. A virtualized environment with a lab manual detailing the steps and commands to run was given instead of using an actual IBM Netezza appliance.

What is Netezza?

The IBM Netezza is a test and development framework appliance which packs the performance and simplicity of Netezza's specific architecture into a small footprint. The IBM Netezza appliances provide an economic framework for customers to build and test their Business Intelligence (BI) and sophisticated analytical applications. It also shares the same characteristics of flexibility, ease of deployment and use, and hardware-based acceleration of analytic queries and workloads as its enterprise-class equivalent.

A SQL dialect called Netezza Structured Query Language(NZSQL) also included in the IBM Netezza appliance. SQL commands could be used to build and manage Netezza databases, user access, and database permissions, as well as to query and update database content.

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 Why Netezza?

Businesses use Netezza because of considering the following factors which provide simplicity, performance, and value.

Simplicity:

The IBM Netezza is an easy-to-use appliance requiring minimal tuning and administration, accelerating the development of applications. For instant data loading and query execution, it is distributed ready-to-go and blends with leading ETL, BI, and analytical applications through standard ODBC, JDBC, and OLE DB interfaces.

Performance:

The performance benefit of the IBM Netezza framework comes from the special Asymmetric Massively Parallel Processing (AMPP) architecture of IBM, which uses Field Programmable Gate Arrays to combine free, blade-based servers with commodity disk storage and patented data filtering (FPGAs).

The IBM Netezza is suitable for use as a test and development scheme for high-performance BI applications as an appliance that shares the same software and hardware architecture with other members of the IBM Netezza data warehouse appliance family.

Value

As a commodity-based appliance, IBM Netezza is a very affordable analytic option, delivering up to 10 TB of user data capacity in a compact physical and environmental footprint. For an overall low cost of ownership, the IBM Netezza appliance requires limited ongoing administration, both in internal resources as well as implementation costs. There are no expenses that are covered.

Inside the IBM Netezza

Using commodity blade servers and storage, the IBM Netezza appliance is designed, turbocharged by FPGAs that filter out superfluous data as it flows off the disk. Each appliance includes a Snippet Blade(or S-Blade), which is responsible for handling SQL queries through 8 pairs of Intel CPU cores and FPGA cores in parallel. This power is packed in a compact 7 rack unit chassis by Skimmer, while still providing up to 10 TB of user data space.

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IBM Netezza Analytics

Analytics is an embedded, purpose-built, advanced analytics platform distributed with every IBM Netezza appliance that enables analytical companies to meet their business requirements and surpass them.

  • Forecast with more precision.
  • Deliver faster forecasts.
  • React quickly to modifications.

The innovative technology of IBM Netezza Analytics fuses data warehousing and in-database analytics into a scalable, high-performance, massively parallel, an advanced analytical framework designed to crack petascale data volumes. This makes it possible for users to address questions about data that other architectures might not have considered. IBM Netezza Analytics is designed to provide reliable and rapid responses to the most advanced business questions rapidly and efficiently.

IBM Netezza Analytics is the most strong advanced analytics framework for IBM Netezza that offers the technology infrastructure to enable in-database analytics enterprise deployment. The analytics platform makes it possible to combine its comprehensive range of built-in analytics with leading analytical tools on the core data warehouse equipment of IBM Netezza from suppliers such as Revolution Analytics, SAS, IBM SPSS, Fuzzy Logix, and Zementis.

The modern data warehouse appliance was established by IBM Netezza and has clients worldwide who have recognized the importance of integrating data warehousing and analytics into a single, integrated, high-performance device. IBM Netezza Analytics allows analytical companies from emerging business models to realize tremendous business value and helps businesses realize both top-line sales growth and cost savings in the bottom-line.

Capabilities of IBM Netezza Analytics
  • Data exploration and discovery.
  • Data transformation.
  • Model building.
  • Model diagnostics.
  • Model scoring.

Architecture of IBM Netezza

IBM Netezza Analytics totally exploits the IBM Netezza data warehouse appliance, a powerful parallel computing platform, to deliver high-speed, scalable analytics processing. To optimize performance and reliability for in-database analytics processing, the appliance utilizes the high-speed throughput of the Asymmetric Massively Parallel Processing (AMPP) architecture. The AMPP architecture is a blade-based streaming architecture that uses commodity blades and storage to provide huge data and high-speed analytics, combined with IBM Netezza proprietary data filtering using field-programmable gate arrays (FPGAs). In a strong and quick appliance, IBM Netezza has integrated all analytics activities.

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BM Netezza Analytics is designed to simplify the creation and implementation of models for analytical companies requiring the highest output on massive, complex data volumes.

Easy to use

The IBM Netezza data warehouse appliance is simple-to-use and speeds up the entire analytical process significantly. It is easy to transfer a majority of analytics within the system using programming interfaces and parallelization options, regardless of whether they are performed using tools from suppliers such as IBM SPSS, SAS, or Revolution Analytics, or written in languages such as Java, Lua, Perl, Python, R or Fortran. In addition, IBM Netezza data warehouse appliances are supplied with a built-in library of parallelized analytical functions, purpose-built for massive data volumes, to kick-start and speed up the development and deployment of any analytical application.

What really sets IBM Netezza apart is the simplicity and ease of development. It is the first system of its kind, packaging hundreds of processing cores with power and scalability in an architecture uniquely suited for parallel analytics. IBM Netezza Analytics consolidates all analytics operations into a strong appliance instead of a fragmented analytics platform with various systems where data is replicated. For an overall low total cost of ownership, it is simple to deploy and requires minimal ongoing administration.

Simplifying the process of data discovery, estimation, modeling, and scoring are key factors for the effective business-wide adoption of analytics. Business users can run their own analytics in near real-time with IBM Netezza, which allows analytics-backed, data-driven decisions to become widespread across an organization.

Netezza SQL Functional Categories

Each of the following functional groups belongs to all SQL commands:

  • Data Definition Language (DDL).
  • Data Control Language (DCL).
  • Data Manipulation Language (DML).
  • Transaction Control.
  • Miscellaneous commands.
Data Definition Language (DDL)

To describe, alter, and delete database objects, such as databases, tables, and views, use IBM Netezza's SQL Data Definition Language (DDL).

Data Control Language (DCL)

You use Data Control Language (DCL) SQL commands as a database security administrator to control the user's access to database objects and their contents.

Data Manipulation Language (DML)

Use the SQL Data Manipulation Language (DML) to select, update, insert, delete, truncate, begin, commit, and rollback commands to access and change database data.

Transaction Control

Transaction control enforces the integrity of the database by ensuring that batches of SQL operations run entirely or not at all. The control commands for transactions are BEGIN, COMMIT, and ROLLBACK.

Functions and Operators

There are several functions and operators given by IBM Netezza SQL. Functions are operations that take on a value, while symbols are operators.

In several instances, functions and operations can be used to perform the same job, so the syntax differential is common.

The following types of functions are provided by Netezza SQL:

  • Numeric: Performs mathematical operations on numeric data.
  • Text: Manipulates text strings.
  • Date and time: Manipulates the date and time values and extracts from those values of particular components.
  • System: Returns unique data to the RDBMS that is being used.
  • Fuzzy and phonetic matching quest: Provides approximate matching of strings based on given algorithms or techniques
  • User-defined: Performs activities described by the developer of functions.

    All these actions are  specified by the developer of the functionality.

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IBM Data Warehousing and Analytics Solutions

IBM offers the largest and most extensive range of applications, hardware, and solutions for data warehousing, knowledge management, and business analytics to help clients leverage the value of their information assets and uncover new insights to make smarter and quicker decisions and improve their business performance.

Netezza Architecture – Hosts

The hosts of Netezza are high-performance Linux servers that are set up for high availability in active-passive mode. The passive host will take over the processing tasks in the case of an active server failure. It just takes a very short amount of time for the passive node to take over.

The active host is an interface for external software such as BI, ETL, JDBC, ODBC tools, and client applications. Through ODBC/JDBC, the client submits SQL requests. To apply the SQL query to the Netezza host, a range of tools such as Aginity, Squirrel, and nzsql utility are used. Netezza compiles them into executable code fragments called snippets (usually C/C++ codes) and, by distributing the snippets to all execution nodes, generates streamlined query plans. The necessary data is retrieved by the FPGA and snippet execution takes place.

Field Programmable Gate Arrays – FPGA

The FPGA is a proprietary hardware tool developed by Netezza to filter out unnecessary data as soon as possible when sending SQL queries to hosts. When reading from disks, the data would be eliminated as early as possible. This data elimination process eliminates IO bottlenecks and frees additional data from the processing of downstream components such as the CPU, memory, and network, thus dramatically improving performance. 

In order to remove unnecessary data, the FPGA often relies on zone maps. Zone maps are generated during certain Netezza operations for each column in the tables.

 Snippet Blades (S-Blades)

S-Blades are smart processing nodes that make up the Netezza data warehouse appliance's MPP engine. Each S-Blade is an independent server containing powerful multi-core CPUs, multi-engine FPGAs, and RAM gigabytes, all of which function to deliver high performance in parallel. In each S-blade, FPGA is significant hardware for the Netezza architecture that improves performance.

Disk Enclosure

High-performance discs are another significant Netezza architecture hardware. There are high-density and high-performance disks in the disk enclosures that are RAID secured. In a database table, each disk contains a slice of the data. The host would use either a hash or a random algorithm to spread the data uniformly across all disks. If mirroring is allowed, a mirror copy of each slice of data will be maintained on a separate disk drive.

The disk enclosures are connected through high-speed interconnects to the S-Blades, allowing all the disks to stream data to the S-Blades at the highest possible rate simultaneously. The distribution of information and the storage area is focused on the distribution key that we use when constructing the table.

Netezza Data Warehouse

Most vendors of Business Intelligence solutions expand their systems before they reach an unsustainable size and thereby become impractical for everyday use. Netezza is different because Netezza's specialists leave it up to the customer's will by supplying customers with the capabilities of constructing data warehouse equipment, how their appliances will look, and what they will be used for.

As a result, the time necessary for each operation is reduced. There is a different data warehouse designed for each issue that needs to be solved instead of collecting data from the largest data warehouses covering the needs of the entire organization, while the entire system is operated from within the Netezza TwinFin 4 platform.

Netezza TwinFin performance

The Netezza TwinFin solution's untypical architecture made it possible to boost efficiency even up to 100 times. An easily manageable device that combines three elements - storage, server, and the database - is responsible for such a speed. In addition, adequate attention is paid to integrated data enforcement and critical data protection (Netezza is the first company to use these two in common appliances). Finally, the company managers of TwinFin get an insight into who is accessing the data and for what reason.

The fact that all hardware, applications, and storage appliances are pre-configured is what simplifies implementation. Therefore, as soon as it is turned on, the solution is ready to be used. The "ready to go" definition allows users of TwinFin to immediately start data loading and query execution. The below figure illustrates the overview of TwinFin architecture of Netezza.

IMG

TwinFin simplicity:

Performance is easily accompanied by all analytical operations where the data stored is centralized. The i-Class technology promotes the use of different resources (SAS, R, Java, Python, Fortran) by enabling them to function simultaneously with engines and libraries.

 Key features of Netezza

The ones below are selected from the most important characteristics of TwinFin:

  • Market Intelligence, as well as advanced analytics, help both.
  • Scalable (10-100x) performance at petascale.
  • Effective, even if thousands of users use it at the same time.
  • Use of i-Class technologies for analytical development.
  • Streaming Blades-based architecture.
  • Ubiquitous simplicity of deployment and management.
  • Compliant with data.
  • Compatible with the most common Business Intelligence and analytical tools.
  • SQL, ODBC, JDBC, and OLE DB standard interfaces.
  • Reliability and availability at 99,99% uptime level.
  • Low cooling and power specifications supporting green orientation.
  • High speed of load: over 2 TB of data per hour.
  • High speed of backup creation: more than 4 TB of data per hour.

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Conclusion

IBM offers the broadest and most extensive range of applications, hardware, and solutions for data warehousing, knowledge processing, and business analytics to help clients maximize the value of their data assets and uncover fresh insights to make smarter and quicker decisions and improve their business performance.

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Gayathri
Gayathri
Research Analyst
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.