Business Linux Operating Systems

Linux
Linux

Unix and Linux are different operating systems with have some common commands. Source code for Linux is freely available to the public and Unix is not available. Linux operating system is a free/open source and Some versions of Unix are proprietary and others are a free/open source. Linux Operating system can be used for desktop systems and for servers. But the Unix is mainly used in servers, mainframes and high-end computers.

AIX is an operating system based on Unix versions from IBM. It is mainly designed for IBM’s workstations and for the server hardware platforms. And HP-UX is the operating system from HP ( Hewlett Packard ) based on Unix versions.  HP-UX and AIX are stable operating system compare with Linux. HP-UX and AIX are platform dependent and they are limited to their own hardware. But in the case of Linux, it is platform independent and can be used with any hardware. Since HP-UX and AIX are platform dependent, they are optimised for the hardware and the performance is better than Linux operating systems.  AIX is outperforming Linux from 5 to 10 percent.

Unix

AT&T Unix, started in the 1970s at the Bell Labs and newer versions of Unix have developed and some of them are listed below. In 1980, AT&T licensed Unix to third-party vendors and leading to the development of different variants. Some of them are;

  • Berkeley Unix, FreeBSD and its variants
  • Solaris from Sun Microsystem
  • HP-UX from Hewlett-Packard
  • AIX from IBM
  • MacOs from Apple
  • Microsoft’s Xenix

Unix installations are costlier since it requires some special hardware. MacOS needs apple computers, AIX needs IBM hardware and HP-UX needs HP hardware etc.

Linux

Linux is a free and open source operating system based on Unix. Linux kernel was first developed by Linus Torvalds in 1991. Linux was originally developed for personal computers but nowadays it is using personal computers as well as in server systems. Since it is very flexible, it can be installed in any hardware systems. Linux operating system is available for mobile phones, tablets, video game consoles, mainframes and supercomputers. Some of the best distros for small business are;

  • Centos
  • ClearOS
  • OpenSUSE
  • IPFire
  • Ubuntu
  • Manjaro
  • Slackware

Linux Vs Unix

Linux Unix
The Source Code of Linux is freely available to its Users. The Source Code of Unix is not available for the general public.
Linux primarily uses Graphical User Interface with an optional Command Line Interface. Unix primarily uses Command Line Interface.
Linux OS is portable and can be executed in different Hard Drives. Unix is not portable.
Linux is very flexible and can be installed on most of the Home Based Pcs. Unix has a rigid requirement of the Hardware. Hence, cannot be installed on every other machine.
Linux is mainly used in Home Based PC, Mobile Phones, Desktops, etc. Unix is mainly used in Server Systems, Mainframes and High-End Computers.
Different Versions of Linux are: Ubuntu, Debian, OpenSuse, Redhat, Solaris, etc. Different Versions of Unix are: AIS, HP-UX, BSD, Iris, etc.
Linux Installation is economical and doesn’t require much specific and high-end hardware. Unix Installation is comparatively costlier as it requires more specific hardware circuitry.
The Filesystems supported by Linux are as follows: xfs, ramfs, nfs, vfat, cramfsm ext3, ext4, ext2, ext1, ufs, autofs, devpts, ntfs The Filesystems supported by Unix are as follows: zfs, js, hfx, gps, xfs, vxfs.
Linux is developed by an active Linux Community worldwide. Unix is developed by AT&T Developers.

Hardware architecture

Most commercial versions of UNIX distributions are coded for specific hardware. Like HP-UX for PA-RISC (Hewlett-Packard) and Itanium machines (Intel) and AIX is for Power processors ( IBM ). Since these distributions are limited, the developers can optimise their code for these architectures to get maximum utilisation of resources.  Since it uses proprietary hardware, Unix distributions are not cost effective.

  • HP-UX needs HP or Intel hardware
  • AIX needs IBM Hardware

Linux operating system is not dependent on the hardware, so it can be installed in any of the server systems which have a processor. Since the developers cannot assume the hardware architecture and they need to prepare the code for some general hardware specifications and that’s why Linux operating system has less performance than the commercial Unix variants.

  • Linux is open to all hardware

Licensing

GNU General Public License (GPL), is a form of copyleft and is used for the Linux kernel and many of the components from the GNU Project. Free software projects, although developed through collaboration, are often produced independently of each other. AIX and HP-UX are using proprietary licenses.

HP-UX

Developer Hewlett-Packard Enterprise
Written in C
OS family Unix (System V)
Initial release 1982; 36 years ago
Kernel type Monolithic with dynamically loadable modules
License Proprietary

 

IBM AIX

Developer IBM
Written in C
OS family Unix
Initial release 1986; 32 years ago
Kernel type Monolithic with dynamically loadable modules
License Proprietary

 

Linux

Developer Community, Linus Torvalds
Written in Primarily C and assembly
OS family Unix-like
Initial release September 17, 1991; 26 years ago
Kernel type Monolithic (Linux kernel)
License GPLv2[7] and other free and open-source licenses (the name “Linux” is a trademark[b])

 

Softwares and Tools

Softwares and tools in Linux are general to all hardware. But in the case of Unix, separate tools and software which leverage to get the maximum performance. So the performance of the systems is higher than the Linux operating system by comparing the hardware configuration. Unix has good performance than Linux systems. While considering the cost estimation, Linux will get more votes.

System Management Interface Tool ( SMIT ) with AIX is the tools used for package management, System Administration Manager (SAM) on HP-UX. Linux operating system uses rpm or dpkg etc. based on the variants.

Software Installation and Patch Management

R H Linux

HP-UX

AIX

Install rpm -i file swinstall –s depot software installp –a [-c] FileSet
Update rpm -U/F file swinstall –s depot software installp –a FileSet
List rpm -q swlist –l product lslpp –L all
Remove rpm -e swremove software installp –u FileSet
Patches rpm -u swinstall installp
List Patches rpm -q -a swlist –l product lslpp –L all
Patch check up2date/yum security_patch_check compare_report

File system

While talking about the file systems, Linux scores more than the other Unix versions. Unix supports two or three file systems locally. But Linux supports almost all the file systems available on any operating system.

 

System Filesystem
AIX jfs, gpfs
HP-UX hfs, vxfs

Kernel

The kernel is the core of the operating system and the source code of the kernel are not freely available for the commercial versions of Unix. For the Linux operating system, the users can check and verify the code and even modify it if required.

Support

The commercial versions of Unix come with a license cost. Since these operating systems are purchased, the vendor will provide technical support to the end users to the smooth running of the operating systems.

In the case of the Linux operating system, we need to use the open source forums and community for getting support from the users and developers around the world or hire some freelancers for fixing the issues.

Related References

DataStage – Netezza Connector Action Column

Over the years have occasionally use the action column feature, however, the last month or so I have found myself using it quite a lot. This is especially true in relation to the tea set and not just in relation to the change capture stage.

The first thing you need to know is, if you want to prevent getting the ‘no action column found’ notice on the target stage, need to ensure that the action column has been coded to be a single character field char (1). Otherwise, the Netezza connector stage will not recognize your field as an action column.

While most developers will commonly work with the action column feature in relation to the change capture stage, it can also be very useful if you have created a field from one or more inputs to tell you what behavior the row requires. I have found that this approach can be very useful and efficient under the right circumstances.

Example Pattern for Action Column Using Multiple Source Selects
Example Pattern for Action Column Using Multiple Source Selects

Action column configuration example

Action Column Field Type
Action Column Field Type

 Change Code Values Mapping To Action Column

  • Here’s a quick reference table to provide the interpretation of the change type code to the actual one character action column value to which it will need to be interpreted.

Change Code Type

Change Type Code

Action Column Value

Copy (Data Without Changes)

0

No
value for this Change Type

Insert

1

I

Delete

2

D

Update

3

U

Example Transformer Stage, Derivation

  •  Here is a quick transformer stage derivation coding example to take advantage of the action call capabilities. If you haven’t already handled the removal of the copy rows, you may also want to add a constraint.
  • The combination I most frequently find myself using is the insert and update combination.
if Lnk_Out_To_Tfm.change_code=1 then ‘I’

Else if Lnk_Out_To_Tfm.change_code=2 then ‘D’

Else if Lnk_Out_To_Tfm.change_code=3 then ‘U’

Related References

Home > InfoSphere Information Server 11.7.0 > InfoSphere DataStage and QualityStage > Developing parallel jobs > Introduction to InfoSphere DataStage Balanced Optimization > Job design considerations  > Specific considerations for the Netezza connector

Netezza / PureData – List of Views against a table

PureData Powered by Netezza
PureData Powered by Netezza

I have found myself using this simple, but useful SQL time in recent weeks to research different issues and to help with impact analysis.  So, I thought I would post it while I’m thinking about it.  It just gives a list of views using a table, which can be handy to know.  This SQL is simple and could be converted to an equi-join.  I used the like statement mostly because I sometimes want to know if there are other views a similar nature in the same family (by naming convention) of tables.

Select All Fields From The _V_View

This is the simplest form of this SQL to views, which a table.

Select * from _v_view

where DEFINITION like ‘%<<TABLE_NAME>>%’ ;

Select Minimal Fields From The _V_View

This is the version of the SQL, which I normally use, to list the views, which use a table.

Select VIEWNAME, OWNER from _v_view

where DEFINITION like ‘%<<TABLE_NAME>>%’;

Related References

Major Cloud Computing Models

Cloud Computing
Cloud Computing

Cloud computing enables convenient, ubiquitous, measures, and on-demand access to a shared pool of scalable and configurable resources, such as servers, applications, databases, networks, and other services. Also, these resources can be provisioned and released rapidly with minimum interaction and management from the provider.

The rapidly expanding technology is rife with obscure acronyms, with major ones being SaaS, PaaS, and IaaS. These acronyms distinguish the three major cloud computing models discussed in this article. Notably, cloud computing virtually meets any imaginable IT needs in diverse ways. In effect, the cloud computing models are necessary to show the role that a cloud service provides and how the function is accomplished. The three main cloud computing paradigms can be demonstrated on the diagram shown below.

The three major cloud computing models
The three major cloud computing models

Infrastructure as a Service (IaaS)

In infrastructure as a service model, the cloud provider offers a service that allows users to process, store, share, and user other fundamental computing resources to run their software, which can include operating systems and applications. In this case, a consumer has minimum control over the underlying cloud infrastructure, but has significant control over operating systems, deployed applications, storage, and some networking components, such as the host firewalls.

Based on its description, IaaS can be regarded as the lowest-level cloud service paradigm, and possibly the most crucial one. With this paradigm, a cloud vendor provides pre-configured computing resources to consumers via a virtual interface. From the definition, IaaS pertains underlying cloud infrastructure but does not include applications or an operating system. Implementation of the applications, operating system, and some network components, such as the host firewalls is left up to the end user. In other words, the role of the cloud provider is to enable access to the computing infrastructure necessary to drive and support their operating systems and application solutions.

In some cases, the IaaS model can provide extra storage for data backups, network bandwidth, or it can provide access to enhanced performance computing which was traditionally available using supercomputers. IaaS services are typically provided to users through an API or a dashboard.

Features of IaaS

  • Users transfer the cost of purchasing IT infrastructure to a cloud provider
  • Infrastructure offered to a consumer can be increased or reduced depending on business storage and processing needs
  • The consumer will be saved from challenges and costs of maintaining hardware
  • High availability of data is in the cloud
  • Administrative tasks are virtualized
  • IaaS is highly flexible compared to other models
  • Highly scalable and available
  • Permits consumers to focus on their core business and transfer critical IT roles to a cloud provider
Infrastructure as a Service (IaaS)
Infrastructure as a Service (IaaS)

IaaS Use Cases

A series of use cases can explore the above benefits and features afforded by IaaS. For instance, an organization that lacks the capital to own and manage their data centers can purchase an IaaS offering to achieve fast and affordable IT infrastructure for their business. Also, the IaaS can be expanded or terminated based on the consumer needs. Another set of companies that can deploy IaaS include traditional organizations seeking large computing power with low expenditure to run their workloads. IaaS model is also a good option for rapidly growing enterprises that avoid committing to specific hardware or software since their business needs are likely to evolve.

Popular IaaS Services

Major IT companies are offering popular IaaS services that are powering a significant portion of the Internet even without users realizing it.

Amazon EC2: Offers scalable and highly available computing capacity in the cloud. Allows users to develop and deploy applications rapidly without upfront investment in hardware

IBM’s SoftLayer: Cloud computing services offering a series of capabilities, such as computing, networking, security, storage, and so on, to enable faster and reliable application development. The solution features bare-metal, hypervisors, operating systems, database systems, and virtual servers for software developers.

NaviSite: offers application services, hosting, and managed cloud services for IT infrastructure

ComputeNext: the solution empowers internal business groups and development teams with DevOps productivity from a single API.

Platform as a Service (PaaS)

Platform as a service model involves the provision of capabilities that allow users to create their applications using programming languages, tools, services, and libraries owned and distributed by a cloud provider. In this case, the consumer has minimum control over the underlying cloud computing resources such as servers, storage, and operating system. However, the user has significant control over the applications developed and deployed on the PaaS service.

In PaaS, cloud computing is used to provide a platform for consumers to deploy while developing, initializing, implementing, and managing their application. This offering includes a base operating system and a suite of development tools and solutions. PaaS effectively eliminates the needs for consumers to purchase, implement and maintain the computing resources traditionally needed to build useful applications. Some people use the term ‘middleware’ to refer to PaaS model since the offering comfortably sits between SaaS and IaaS.

Features of PaaS

  • PaaS service offers a platform for development, tasking, and hosting tools for consumer applications
  • PaaS is highly scalable and available
  • Offer cost effective and simple way to develop and deploy applications
  • Users can focus on developing quality applications without worrying about the underlying IT infrastructure
  • Business policy automation
  • Many users can access a single development service or tool
  • Offers database and web services integration
  • Consumers have access to powerful and reliable server software, storage capabilities, operating systems, and information and application backup
  • Allows remote teams to collaborate, which improves employee productivity
Platform as a Service (PaaS)
Platform as a Service (PaaS)

PaaS Use Cases

Software development companies and other enterprises that want to implement agile development methods can explore PaaS capabilities in their business models. Many PaaS services can be used in application development. PaaS development tools and services are always updated and made available via the Internet to offer a simple way for businesses to develop, test, and prototype their software solutions. Since developers’ productivity is enhanced by allowing remote workers to collaborate, PaaS consumers can rapidly release applications and get feedback for improvement. PaaS has led to the emergence of the API economy in application development.

Popular PaaS Offerings

There exist major PaaS services that are helping organizations to streamline application development. PaaS offering is delivered over the Internet and allows developers to focus more on creating quality and highly functional application while not worrying about the operating system, storage, and other infrastructure.

Google’s App Engine: the solution allows developers to build scalable mobile and web backends in any language in the cloud. Users can bring their own language runtimes, third-party libraries, and frameworks

IBM BlueMix: this PaaS solution from IBM allows developers to avoid vendor lock-in and leverage the flexible and open cloud environment using diverse IBM tools, open technologies, and third-party libraries and frameworks.

Heroku: the solution provides companies with a platform where they can build, deliver, manage, and scale their applications while abstracting and bypassing computing infrastructure hassles

Apache Stratos: this PaaS offering offers enterprise-ready quality service, security, governance, and performance that allows development, modification, deployment, and distribution of applications.

Red Hat’s OpenShift: a container application platform that offers operations and development-centric tools for rapid application development, easy deployment, scalability, and long-term maintenance of applications

Software as a Service (SaaS)

Software as a service model involves the capabilities provided to users by using a cloud vendor’s application hosted and running on a cloud infrastructure. Such applications are conveniently accessible from different platforms and devices through a web browser, a thin client interface, or a program interface. In this model, the end user has minimum control of the underlying cloud-based computing resources, such as servers, operating system, or the application capabilities

SaaS can be described as software licensing and delivery paradigm that features a complete and functional software solutions provided to users on a metered and subscription basis. Since users access the application via browsers or thin client and program interfaces, SaaS makes the host operating system insignificant in the operation of the product. As mentioned, the service is metered. In this case, SaaS customers are billed based on their consumption, while others pay a flat monthly fee.

Features of SaaS

  • SaaS providers offer applications via subscription structure
  • User transfer the need to develop, install, manage, or upgrade applications to SaaS vendors
  • Applications and data is securely stored in the cloud
  • SaaS is easily managed from a central location
  • Remote serves are deployed to host the application
  • Users can access SaaS offering from any location with Internet access
  • On-premise hardware failure does not interfere with an application or cause data loss
  • Users can reduce or increase use of cloud-based resources depending on their processing and storage needs
  • Applications offered via SaaS model are accessible from any location and almost all Internet-enabled devices
Software as a Service (SaaS)
Software as a Service (SaaS)

SaaS Use Cases

SaaS use case is a typical use case for many companies seeking to benefit from quality application usage without the need to develop, maintain and upgrade the required components. Companies can acquire SaaS solutions for ERP, mail, office applications, collaboration tool, among others. SaaS is also crucial for small companies and startups that wish to launch e-commerce service rapidly but lack the time and resource to develop and maintain the software or buy servers for hosting the platform. SaaS is also used by companies with short-term projects that require collaboration from different members located remotely.

Popular SaaS Services

SaaS offerings are more widespread as compared to IaaS and PaaS. In fact, a majority of consumers use SaaS services without realizing it.

Office365: the cloud-based solution provides productivity software for subscribed consumers. Allows users to access Microsoft Office tools on various platforms, such as Android, MacOS, and Windows, etc.

Box: the SaaS offers secure file storage, sharing, and collaboration from any location and platform

Dropbox: modern application designed for collaboration and for creating, storing, and accessing files, docs, and folders.

Salesforce: the SaaS is among the leading customer relationship management platform that offers a series of capabilities for sales, marketing, service, and more.

Today, cloud computing models have revolutionized the way businesses deploy and manage computing resources and infrastructure. With the advent and evolution of the three major cloud computing models, that it IaaS, PaaS, and SaaS, consumers will find a suitable cloud offering that satisfies virtually all IT needs. These models’ capabilities coupled with competition from popular cloud computing service providers will continue availing IT solutions for consumers demanding for availability, enhanced performance, quality services, better coverage, and secure applications.

Consumers should review their business needs and do a cost-benefit analysis to approve the best model for their business. Also, consumers should conduct thorough workload assessment while migrating to a cloud service.

Big Data vs. Virtualization

Big Data Information Approaches
Big Data Information Approaches

Globally, organizations are facing challenges emanating from data issues, including data consolidation, value, heterogeneity, and quality. At the same time, they have to deal with the aspect of Big Data. In other words, consolidating, organizing, and realizing the value of data in an organization has been a challenge over the years. To overcome these challenges, a series of strategies have been devised. For instance, organizations are actively leveraging on methods such as Data Warehouses, Data Marts, and Data Stores to meet their data assets requirements. Unfortunately, the time and resources required to deliver value using these legacy methods is a distressing issue. In most cases, typical Data Warehouses applied for business intelligence (BI) rely on batch processing to consolidate and present data assets. This traditional approach is affected by the latency of information.

Big Data

As the name suggests, Big Data describes a large volume of data that can either be structured or unstructured. It originates from business processes among other sources. Presently, artificial intelligence, mobile technology, social media, and the Internet of Things (IoT) have become new sources of vast amounts of data. In Big Data, the organization and consolidation matter more than the volume of the data. Ultimately, big data can be analyzed to generate insights that can be crucial in strategic decision making for a business.

Features of Big Data

The term Big Data is relatively new. However, the process of collecting and preserving vast amounts of information for different purposes has been there for decades. Big Data gained momentum recently with the three V’s features that include volume, velocity, and variety.

Volume: First, businesses gather information from a set of sources, such as social media, day-to-day operations, machine to machine data, weblogs, sensors, and so on. Traditionally, storing the data was a challenge. However, the requirement has been made possible by new technologies such as Hadoop.

Velocity: Another defining nature of Big Data is that it flows at an unprecedented rate that requires real-time processing. Organizations are gathering information from RFID tags, sensors, and other objects that need timely processing of data torrents.

Variety: In modern enterprises, information comes in different formats. For instance, a firm can gather numeric and structured data from traditional databases as well as unstructured emails, video, audio, business transactions, and texts.

Complexity: As mentioned above, Big Data comes from diverse sources and in varying formats. In effect, it becomes a challenge to consolidate, match, link, cleanse, or modify this data across an organizational system. Unfortunately, Big Data opportunities can only be explored when an organization successfully correlates relationships and connects multiple data sets to prevent it from spiraling out of control.

Variability: Big Data can have inconsistent flows within periodic peaks. For instance, in social media, a topic can be trending, which can tremendously increase collected data. Variability is also common while dealing with unstructured data.

Big Data Potential and Importance

The vast amount of data collected and preserved on a global scale will keep growing. This fact implies that there is more potential to generate crucial insights from this information. Unfortunately, due to various issues, only a small fraction of this data actually gets analyzed. There is a significant and untapped potential that businesses can explore to make proper and beneficial use of this information.

Analyzing Big Data allows businesses to make timely and effective decisions using raw data. In reality, organizations can gather data from diverse sources and process it to develop insights that can aid in reducing operational costs, production time, innovating new products, and making smarter decisions. Such benefits can be achieved when enterprises combine Big Data with analytic techniques, such as text analytics, predictive analytics, machine learning, natural language processing, data mining and so on.

Big Data Application Areas

Practically, Big Data can be used in nearly all industries. In the financial sector, a significant amount of data is gathered from diverse sources, which requires banks and insurance companies to innovate ways to manage Big Data. This industry aims at understanding and satisfying their customers while meeting regulatory compliance and preventing fraud. In effect, banks can exploit Big Data using advanced analytics to generate insights required to make smart decisions.

In the education sector, Big Data can be employed to make vital improvements on school systems, quality of education and curriculums. For instance, Big Data can be analyzed to assess students’ progress and to design support systems for professors and tutors.

Healthcare providers, on the other hand, collect patients’ records and design various treatment plans. In the healthcare sector, practitioners and service providers are required to offer accurate and timely treatment that is transparent to meet the stringent regulations in the industry and to enhance the quality of life. In this case, Big Data can be managed to uncover insights that can be used to improve the quality of service.

Governments and different authorities can apply analytics to Big Data to create the understanding required to manage social utilities and to develop solutions necessary to solve common problems, such as city congestion, crime, and drug use. However, governments must also consider other issues such as privacy and confidentiality while dealing with Big Data.

In manufacturing and processing, Big Data offers insights that stakeholders can use to efficiently use raw materials to output quality products. Manufacturers can perform analytics on big data to generate ideas that can be used to increase market share, enhance safety, minimize wastage, and solve other challenges faster.

In the retail sector, companies rely heavily on customer loyalty to maintain market share in a highly competitive market. In this case, managing big data can help retailers to understand the best methods to utilize in marketing their products to existing and potential consumers, and also to sustain relationships.

Challenges Handling Big Data

With the introduction of Big Data, the challenge of consolidating and creating value on data assets becomes magnified. Today, organizations are expected to handle increased data velocity, variety, and volume. It is now a business necessity to deal with traditional enterprise data and Big Data. Traditional relational databases are suitable for storing, processing, and managing low-latency data. Big Data has increased volume, variety, and velocity, making it difficult for legacy database systems to efficiently handle it.

Failing to act on this challenge implies that enterprises cannot tap the opportunities presented by data generated from diverse sources, such as machine sensors, weblogs, social media, and so on. On the contrary, organizations that will explore Big Data capabilities amidst its challenges will remain competitive. It is necessary for businesses to integrate diverse systems with Big Data platforms in a meaningful manner, as heterogeneity of data environments continue to increase.

Virtualization

Virtualization involves turning physical computing resources, such as databases and servers into multiple systems. The concept consists of making the function of an IT resource simulated in software, making it identical to the corresponding physical object. Virtualization technique uses abstraction to create a software application to appear and operate like hardware to provide a series of benefits ranging from flexibility, scalability, performance, and reliability.

Typically, virtualization is made possible using virtual machines (VMs) implemented in microprocessors with necessary hardware support and OS-level implementations to enhance computational productivity. VMs offers additional convenience, security, and integrity with little resource overhead.

Benefits of Virtualization

Achieving the economics of wide-scale functional virtualization using available technologies is easy to improve reliability by employing virtualization offered by cloud service providers on fully redundant and standby basis. Traditionally, organizations would deploy several services to operate at a fraction of their capacity to meet increased processing and storage demands. These requirements resulted in increased operating costs and inefficiencies. With the introduction of virtualization, the software can be used to simulate functionalities of hardware. In effect, businesses can outstandingly eliminate the possibility of system failures. At the same time, the technology significantly reduces capital expense components of IT budgets. In future, more resources will be spent on operating, than acquisition expenses. Company funds will be channeled to service providers instead of purchasing expensive equipment and hiring local personnel.

Overall, virtualization enables IT functions across business divisions and industries to be performed more efficiently, flexibly, inexpensively, and productively. The technology meaningfully eliminates expensive traditional implementations.

Apart from reducing capital and operating costs for organizations, virtualization minimizes and eliminates downtime. It also increases IT productivity, responsiveness, and agility. The technology provides faster provisioning of resources and applications. In case of incidents, virtualization allows fast disaster recovery that maintains business continuity.

Types of Virtualization

There are various types of virtualization, such as a server, network, and desktop virtualization.

In server virtualization, more than one operating system runs on a single physical server to increase IT efficiency, reduce costs, achieve timely workload deployment, improve availability and enhance performance.

Network virtualization involves reproducing a physical network to allow applications to run on a virtual system. This type of virtualization provides operational benefits and hardware independence.

In desktop virtualization, desktops and applications are virtualized and delivered to different divisions and branches in a company. Desktop virtualization supports outsourced, offshore, and mobile workers who can access simulate desktop on tablets and iPads.

Characteristics of Virtualization

Some of the features of virtualization that support the efficiency and performance of the technology include:

Partitioning: In virtualization, several applications, database systems, and operating systems are supported by a single physical system since the technology allows partitioning of limited IT resources.

Isolation: Virtual machines can be isolated from the physical systems hosting them. In effect, if a single virtual instance breaks down, the other machine, as well as the host hardware components, will not be affected.

Encapsulation: A virtual machine can be presented as a single file while abstracting other features. This makes it possible for users to identify the VM based on a role it plays.

Data Virtualization – A Solution for Big Data Challenges

Virtualization can be viewed as a strategy that helps derive information value when needed. The technology can be used to add a level of efficiency that makes big data applications a reality. To enjoy the benefits of big data, organizations need to abstract data from different reinforcements. In other words, virtualization can be deployed to provide partitioning, encapsulation, and isolation that abstracts the complexities of Big Data stores to make it easy to integrate data from multiple stores with other data from systems used in an enterprise.

Virtualization enables ease of access to Big Data. The two technologies can be combined and configured using the software. As a result, the approach makes it possible to present an extensive collection of disassociated and structured and unstructured data ranging from application and weblogs, operating system configuration, network flows, security events, to storage metrics.

Virtualization improves storage and analysis capabilities on Big Data. As mentioned earlier, the current traditional relational databases are incapable of addressing growing needs inherent to Big Data. Today, there is an increase in special purpose applications for processing varied and unstructured big data. The tools can be used to extract value from Big Data efficiently while minimizing unnecessary data replication. Virtualization tools also make it possible for enterprises to access numerous data sources by integrating them with legacy relational data centers, data warehouses, and other files that can be used in business intelligence. Ultimately, companies can deploy virtualization to achieve a reliable way to handle complexity, volume, and heterogeneity of information collected from diverse sources. The integrated solutions will also meet other business needs for near-real-time information processing and agility.

In conclusion, it is evident that the value of Big Data comes from processing information gathered from diverse sources in an enterprise. Virtualizing big data offers numerous benefits that cannot be realized while using physical infrastructure and traditional database systems. It provides simplification of Big Data infrastructure that reduces operational costs and time to results. Shortly, Big Data use cares will shift from theoretical possibilities to multiple use patterns that feature powerful analytics and affordable archival of vast datasets. Virtualization will be crucial in exploiting Big Data presented as abstracted data services.

 

Netezza / Puredata – How to replace or trim CHAR(0) is NULL characters in a field

PureData Powered by Netezza
PureData Powered by Netezza

Occasionally, one runs into the problem of hidden field values breaking join criteria.  I have had to clean up bad archive and conversion data with hidden characters serval times over the last couple of weeks, so, I thought I might as well capture this note for future use.

I tried the Replace command which is prevalent for Netezza answers to this issue on the web, but my client’s version does not support that command.  So, I needed to use the Translate command instead to accomplish it.  It took a couple of searches of the usual bad actors to find the character causing the issue, which on this day was chr(0).  Here is a quick mockup of the command I used to solve this issue.

Example Select Statement

Here is a quick example select SQL to identify problem rows.

SELECT TRANSLATE(F.BLOGTYPE_CODE, CHR(0), ”) AS BLOGTYPE_CODE, BT.BLOG_TYP_ID, LENGTH(BT.BLOG_TYP_ID) AS LNGTH_BT, LENGTH(F.BLOGTYPE_CODE) AS LNGTH_ BLOGTYPE

FROM  BLOGS_TBL F,  BLOG_TYPES BT WHERE TRANSLATE(F.BLOGTYPE_CODE, CHR(0), ”) =  BT.BLOG_TYP_ID AND LENGTH(BT.BLOG_TYP_ID) <>Length(LENGTH(F.BLOGTYPE_CODE) ;

 

Example Update Statement

Here is a quick shell update statement to remove the Char(0) characters from the problem field.

Update <<Your Table Name>> A

Set A.<<Your Field Name>> = TRANSLATE(A.<<Your FieldName>>, CHR(0), ”)

where length(A.<<Your Field Name>>) <> Length(A.<<Your FieldName>>) And << Additional criteria>>;

 

 

 

SQL Server – how to know when a stored procedure ran last

Microsoft SQL Server 2017
Microsoft SQL Server 2017

This week I needed to know if a stored procedure was running when expected during our batch.  So, here is a quick couple of SQL to answer the question:

When a Stored Procedure was run last

This version of the SQL gives the date for the last time the Stored Procure was run:

select distinct   top 1     s.last_execution_time

from  sys.dm_exec_query_stats s

cross apply sys.dm_exec_query_plan (s.plan_handle) p

where  object_name(p.objectid, db_id(‘<<DATABASE_NAME>>’)) = ‘<<STORED_PROCEDURE_NAME>>’

Order by s.last_execution_time desc

Get a list of when Stored Procedure has been run

This version of the SQL provides a list of dates of when Stored Procure has been run:

select distinct   s.last_execution_time

from  sys.dm_exec_query_stats s

cross apply sys.dm_exec_query_plan (s.plan_handle) p

where object_name(p.objectid, db_id(‘<<DATABASE_NAME>>’)) = ‘<<STORED_PROCEDURE_NAME>>’

Order by s.last_execution_time desc