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.

 

Data Warehousing vs. Data Virtualization

Information Management

Information Management

Today, a business heavily depends on data to gain insights into their processes and operations and to develop new ways to increase market share and profits. In most cases, data required to generate the insights are sourced and located in diverse places, which requires reliable access mechanism. Currently, data warehousing and data virtualization are two principal techniques used to store and access the sources of critical data in a company. Each approach offers various capabilities and can be deployed for particular use cases as described in this article.

Data Warehousing

A data warehouse is designed and developed to secure host historical data from different sources. In effect, this technique protects data sources from performance degradation caused by the impact of sophisticated analytics and enormous demands for reports. Today, various tools and platforms have been developed for data warehouse automation in companies. They can be deployed to quicken development, automate testing, maintenance, and other steps involved in data warehousing. In a data warehouse, data is stored as a series of snapshots, where a record represents data at a particular time. In effect, companies can analyze data warehouse snapshots to compare data between different periods. The results are converted into insights required to make crucial business decisions.

Moreover, a data warehouse is optimized for other functions, such as data retrieval. The technology duplicates data to allow database de-normalization that enhances query performance. The solution is further deployed to create an enterprise data warehouse (EDW) used to service the entire organization.

Data Warehouse Information Architecture

Data Warehouse Information Architecture

Features of a Data Warehouse

A data warehouse is subject-oriented, and it is designed to help entities analyze data. For instance, a company can start a data warehouse focused on sales to learn more about sales data. Analytics on this warehouse can help establish insights such as the best customer for the period. The data warehouse is subject oriented since it can be defined based on a subject matter.

A data warehouse is integrated. Data from various sources is first out into a consistent format. The process requires the firm to resolve some challenges, such as naming conflicts and inconsistencies on units of measure.

A data warehouse in nonvolatile. In effect, data entered into the warehouse should not change after it is stored. This feature increases accuracy and integrity in data warehousing.

A data warehouse is time variant since it focuses on data changes over time. Data warehousing discovers trends in business by using large amounts of historical data. In effect, a typical operation in a data warehouse scans millions of rows to return an output.

A data warehouse is designed and developed to handle ad hoc queries. In most cases, organizations may not predict the amount of workload of a data warehouse. Therefore, it is recommendable to optimize the data warehouse to perform optimally over any possible query operation.

A data warehouse is regularly updated by the ETL process using bulk data modification techniques. Therefore, end users cannot directly update the data warehouse.

Advantages of Data Warehousing

The primary motivation for developing a data warehouse is to provide timely information required for decision making in an organization. A business intelligence data warehouse serves as an initial checkpoint for crucial business data. When a company stores its data in a data warehouse, tracking it becomes natural. The technology allows users to perform quick searches to be able to retrieve and analyze static data.

Another driver for companies investing in data warehouses involves integrating data from disparate sources. This capability adds value to operational applications like customer relationship management systems. A well-integrated warehouse allows the solution to translate information to a more usable and straightforward format, making it easy for users to understand the business data.

The technology also allows organizations to perform a series of analysis on data.

A data warehouse reduces the cost to access historical data in an organization.

Data warehousing provides standardization of data across an organization. Moreover, it helps identify and eliminate errors. Before loading data, the solution shows inconsistencies to users and corrects them.

A data warehouse also improves the turnaround time for analysis and report generation.

The technology makes it easy for users to access and share data. A user can conduct a quick search on a data warehouse to find and analyze static data without wasting time.

Data warehousing removes informational processing load from transaction-oriented databases.

Disadvantages of Data Warehousing

While data warehousing technology is undoubtedly beneficial to many organizations, not all data warehouses are relevant to a business. In some cases, a data warehouse can be expensive to scale and maintain.

Preparing a data warehouse is time-consuming since it requires users to input raw data, which has to be achieved manually.

A data warehouse is not a perfect choice for handing unstructured and complex raw data. Moreover, it faces difficulties incompatibility. Depending on the data sources, companies may require a business intelligence team to ensure compatibility is achieved for data coming from sources running distinct operating systems and programs.

The technology requires a maintenance cost to continue working correctly. The solution needs to be updated with latest features that might be costly. Regularly maintaining a data warehouse will need a business to spend more on top of the initial investment.

A data warehouse use can be limited due to information privacy and confidentiality issues. In most cases, businesses collect and store sensitive data belonging to their clients. Viewing it is only allowed to individual employees, which limits the benefits offered by a data warehouse.

Data Warehousing Use Case

There are a series of ways organizations use data warehouses. Businesses can optimize the technology for performance by identifying the type of data warehouse they have.

  1. A data warehouses can be used by an organization that is struggling to report efficiently on business operations and activities. The solution makes it possible to access the required data
  2. A data warehouse is necessary for an organization where data is copied separately by different divisions for analysis in spreadsheets that are not consistent with one another.
  3. Data warehousing is crucial in organizations where uncertainties about data accuracy are causing executives to question the veracity of reports.
  4. A data warehouse is crucial for business intelligence acceleration. The technology delivers rapid data insights to analysts at different scales, concurrency, and without requiring manual tuning or optimization of a database.
Data Virtualization Information Architecture

Data Virtualization Information Architecture

Data Virtualization

Data virtualization technology does not require transfer or storage of data. Instead, users employ a combination of application programming interfaces (APIs) and metadata (data about data) to interface with data in different sources. Users use joined queries to gain access to the original data sources. In other words, data virtualization offers a simplified and integrated view to business data in real-time as requested by business users, applications, and analytics. In effect, the technology makes it possible to integrate data from distinct sources, formats, and locations, without replication. It creates a unified virtual data layer that delivers data services to support users and various business applications.

Data virtualization performs many of the same data integration functions, that is, extract, transform, and load, data replication, and federation. It leverages modern technology to deliver real-time data integration with agility, low cost, and high speed. In effect, data virtualization eliminates traditional data integration and reduces the need for replicated data warehouses and data marts in most cases.

Capabilities and Benefits of Data Virtualization

There are various benefits of implementing data virtualization in an organization.

Firstly, data virtualization allows access and leverage of all information that helps a firm achieve a competitive advantage. The solution offers a unified virtual layer that abstracts the underlying source complexity and presents disparate data sources as a single source.

Data virtualization is cheaper since it does not require actual hardware devices to be installed. In other words, organizations no longer need to purchase and dedicate a lot of IT resources and additional monetary investment to create on-site resources, similar to the one used in a data warehouse.

Data virtualization allows speedy deployment of resources. In this solution, resource provisioning is fast and straightforward. Organizations are not required to set up physical machines or to create local networks or install other IT components. Users have a single point of access to a virtual environment that can be distributed to the entire company.

Data virtualization is an energy-efficient system since the solution does not require additional local hardware and software. Therefore, an organization will not be required to install cooling systems.

Disadvantages of Data Virtualization

Data virtualization creates a security risk. In the modern world, having information is a cheap way to make money. In effect, company data is frequently targeted by hackers. Implementing data virtualization from disparate sources may give an opportunity to malicious users to steal critical information and use it for monetary gain.

Data virtualization requires a series of channels or links that must work in cohesion to perform the intended task. In this cases, all data sources should be available for virtualization to work effectively.

Data Virtualization Use Cases

  • Companies that rely on business intelligence require data virtualization for rapid prototyping to meet immediate business needs. Data virtualization can create a real-time reporting solution that unifies access to multiple internal databases.
  • Provisioning data services for single-view applications, such as in customer service and call center applications require data virtualization.

 

End Of Support For IBM InfoSphere 9.1.0

IBM Information Server (IIS)

IBM Information Server (IIS)

End of Support for IBM InfoSphere Information Server 9.1.0

IBM InfoSphere Information Server 9.1.0 will reach End of Support on 2018-09-30.  If you are still on the InfoSphere Information Server (IIS) 9.1.0, I hope you have a plan to migrate to an 11-series version soon.  InfoSphere Information Server (IIS) 11.7 would be worth considering if you don’t already own an 11-series license. InfoSphere Information Server (IIS) 11.7 will allow you to take advantage of the evolving thin client tools and other capabilities in the 2018 release pipeline without needing to perform another upgrade.

Related References

IBM Support, End of support notification: InfoSphere Information Server 9.1.0

IBM Support, Software lifecycle, InfoSphere Information Server 9.1.0

IBM Knowledge Center, Home, InfoSphere Information Server 11.7.0, IBM InfoSphere Information Server Version 11.7.0 documentation

My Most Used Windows 10 Keyboard Shortcuts

Shortcut Keystrokes

Shortcut Keystrokes

While there are a great number of useful windows 10 shortcuts, I have the list below the combination, which I use daily.  Many of the shortcuts can be used across multiple applications (e.g. Notepad++, MS Word, SQL Server, Aginity, etc.) and save a considerable amount of mouse work.  Overall, these shortcut keys are more efficient and faster than using the mouse to perform the same task on a repetitive basis.

You may want to investigate the numerous other Windows 10 shortcuts keys, which best apply to your daily activities, but these are the ones, which I have found most useful and which I have committed to memory.

Table of My Most Used Windows Shortcuts

Key
Strokes

Behavior

Alt
+ Tab

Switch
between open apps

Ctrl
+ A

Select
all items in a document or window

Ctrl
+ Alt + Tab

Use
the arrow keys to switch between all open apps

Ctrl
+ C

Copy
the selected item

Ctrl
+ D

Delete
the selected item and move it to the Recycle Bin

Ctrl
+ F

Select
the search box

Ctrl
+ V

Paste
the selected item

Ctrl
+ X

Cut
the selected item

Esc

Stop
or leave the current task

F5

Refresh
the active window

F11

Maximize
Window

Related References

 Microsoft > Windows Support > Keyboard shortcuts in Windows

 

 

 

 

 

 

Parallel jobs on Windows fail with APT_IOPort::readBlkVirt;error

APT_IOPort::readBlkVirt Error Screenshot

APT_IOPort::readBlkVirt Error Screenshot

This a known error for windows systems and applies to DataStage and DataQuality jobs using the any or all the three join type stages (Join, Merge, and Lookup).

Error Message

  • <<Link name>>,0: APT_IOPort::readBlkVirt: read for block header, partition 0, [fd 4], returned -1 with errno 10,054 (Unknown error)

Message ID

  • IIS-DSEE-TFIO-00223

Applies To

  • Windows systems only
  • Parallel Engine Jobs the three join type stages (Join, Merge, and Lookup). It does not apply to Server Engine jobs.
  • Infosphere Information Server (IIS), Datastage and DataQuality 9.1 and higher

The Fix

  • Add the APT_NO_IOCOMM_OPTIMIZATION in project administrator and set to blank or 0. I left it blank so it would not impact other jobs
  • Add the environment variable to the job producing the error and set to 1

What it APT_NO_IOCOMM_OPTIMIZATION Does

  • Sets the use of shared memory as the transport type, rather than using the default sockets transport type.
  • Note that in most cases sockets transport type is faster, so, you likely will not to set this across the project as the default for all job. It is best to apply it as necessary for problematic jobs.

Related References

InfoSphere DataStage and QualityStage, Version 9.1 Job Compatibility

IBM Support, JR54078: PARALLEL JOBS ON WINDOWS FAIL WITH APT_IOPORT::READBLKVIRT; ERROR

IBM Support, Information Server DataStage job fails with unknown error 10,054.

 

Common Information Technology Architectures

Overview Of Common Information Technology Architectures

The world is currently in the Information and Technology era, were as, so many experts are of the opinion that the Silicon Valley days are beginning to come to an end. Information and Technology is basically what the world revolves around today which makes it necessary to consider some technical overview of Information and Technology architecture use. The term Information Technology is often used in place for computer networks, and it also surrounds other information related technologies like television, cell phones and so on, showing the connection between IT and ICT (thou IT and ICT are often used to replace each other but technically are different). When talking about IT architectural, it is the framework or basis that supports an organization or system. Information technology architectural concerning computing involves virtual and physical resources supporting the collection, processing, analysis and storage of data. The architecture, in this case, can be integrated into a data center or in some other instances decentralized into multiple data centers, which can be managed and controlled by the IT department or third-party IT firm, just like cloud provider or colocation facility. IT architectures usually come into play when we consider hardware for computers (Big Iron: mainframe & Supercomputers), software, internet (LAN / WAN Server based), e-commerce, telecom equipment, storage (Cloud) and so on.

Information Technology Industry Overview

Information Technology Industry Overview

We human beings have been able to manipulate, store, and retrieve data since 3000Bc, but the modern sense of information technology first appeared in an article in 1958 published in a Havard Business Review: Harold j.Leavitt and Thomas L.whisler were the authors, and they further commented that the new technology was lacking an established name. It shall be called information technology (IT). Information Technology is used in virtually all sectors and industries, talking about education, agriculture, marketing, health, governance, finance and so on. Whatever you do, it is always appropriate to have a basic overview of the architectural uses of Information Technology. Now we take a look at some standard Information technology architectures use with regards to technology environment patterns such as Big Iron (mainframe & Supercomputers); Cloud; LAN / WAN Server based; storage (Cloud).

Big Iron (Mainframe & Supercomputers)

Big iron is a term used by hackers, and as defined in the hacker’s dictionary the Jargon File refers to it as “large, expensive, ultra-fast computers. It is used for number crunching supercomputers such as Crays, but can include more conventional big commercial mainframes”. Often used concerning IBM mainframes, when discussing their survival after the invention of lower cost Unix computing systems. More recently the term also applies to highly efficient computer servers and ranches, whose steel racks naturally work in the same manner.

Supercomputers are known to be the world’s fastest and largest computers, and they are primarily used for complex scientific calculations. There are similar components in a supercomputer and desktop computer: they both have memory processors and hard-drives. Although similarities exist between supercomputers and desktop computers, the speeds are significantly different. Supercomputers are way faster and more extensive. The supercomputers large disk storage, high memory, and processors increase the speed and the power of the machine. Although desktop computers can perform thousands or millions of floating-point operations per second know as (megaflops), supercomputers speeds perform at billions of operations per second also known as (gigaflops) and even up to trillions of operations per second know as (teraflops).

Mainframe Computers

Mainframe Computers

Evolution Of Mainframe and Supercomputers

Currently, many computers are indeed faster than the very first supercomputer, the Cray-1, which is designed and developed by Cray Research team during the mid-70s. The Cray-1 had the capacity of computing at the rate of 167 megaflops using a rapid form of computing called the Vector Processing,   which is composed of quick execution of instructions in a state of pipelined fashion. In the mid-80s a faster method of supercomputing was originated: which was called Parallel Processing.  Applications that made use of parallel processing were and are still able to solve computational issues by using multiple processors. Example: if you were going to prepare ice cream, sundaes for nine of your friends. You would need ten scoops of ice cream, ten bowls; ten drizzles of chocolate syrup with ten cherries, working alone you would put one scoop of ice-cream in each bowl and drizzle the syrup on each other. Now, this method of preparing sundaes will be categorized as vector processing. To get the job done very quickly, you will need help from some friends to assist you in a parallel processing method. If five people prepare the ice-cream mixture, it would be five times as fast.

Parallel Processing

Parallel Processing

Application Of Mainframe and Supercomputers

Supercomputers are very powerful that they can provide researchers with the insight into sources that are small, too fast, too big, or maybe very slow to observe in laboratories. Astrophysicists make use of supercomputers as time machines to explore the past and the future of the universe. A fascinating supercomputer simulation was created in the year 2000 that was able to depict the collision of two galaxies: The Andromeda and our very own Milky Way, although this collision will not happen in another 3 billion years from now.

This particular simulation allowed scientist to experiment and the view the result now. The simulation was conducted by Blue Horizon, a parallel supercomputer in the Diego, Supercomputer Center. Using 256 of Blue Horizon’s 1,152 processors, the simulation showed what would happen to millions of stars if the galaxies collided. Another example is molecular dynamic (molecular interactions with each other). Simulation events done with supercomputers allow scientists to study their interactions when two molecules are docked down. Researchers can generate an atom-by-atom picture of the molecular geometry by determining the shape of a molecule’s surface. Atomic experimentation at this level is extremely difficult or impossible to perform in a laboratory environment, but supercomputers have paved the way for scientists to stimulate such behaviors with ease.

Supercomputers Of The Future

Various research centers are always diving into new applications such as data mining to explore additional and multiple uses of supercomputing. Data mining allows scientist to find previously unknown relationships among data, just like the Protein Data Bank at San Diego Supercomputer Center is collecting scientific data that provides other scientists all around the world with more significant ways of understanding of biological systems. So this will provide researchers with new and unlimited insights of the effects, causes, and treatments of so many diseases. Capabilities of and applications of supercomputers will continue to grow as institutions all over the world are willing to share their various discoveries making researchers more proficient at parallel processing.

information technology Data Storage

Electronic data storage, which is widely used in modern computers today, has a date that spans from World War II when a delay memory line was developed to remove the interference from radar signals. We also have the William tube, which was the very first random-access digital storage, based on the cathode ray tube which consumed more electrical power. The problem regarding this was that the information stored in the delay line memory was liable to change flexibly and fast, especially very volatile. So it had to be continuously refreshed, and information was lost whenever power was removed. The first form of non-volatile computer storage system was the magnetic drum, which was the magnetic drum, it was invented in 1932 and used in the (Ferranti Mark 1) the very first commercially available electronic that was for general-purpose.

IBM initially introduced the very first hard disk drive in 1956, as an added component to their 305 RAMAC computer system. Most digitalized data today are stored magnetically on a hard disk, or optically such as CD-ROMS. But in 2002 the digital storage capacity exceeded analog for the first time. In the year 2007, almost 94% of data stored in the world was digitally held: 28% optical devices, 52% hard disks, 11% digital magnetic tape. The worldwide capacity for storing information on electronic devices grew from 3 Exabyte (1986) to 295 Exabyte (2007), doubling every three years. 

Cloud Computing

Cloud Computing

Cloud Storage

Cloud storage is a modern data storage system in which the digital data is stored in an array of logical pools, the physical storage system composes of multiple servers and often various locations, and the environment is usually owned by and managed by a hosting company. Cloud storage supplying companies are in charge of for keeping the data available and accessible, individuals; organizations lease or buy storage capacity from the suppliers to store user, application data or organization. Cloud storage refers to a hosted object-storage service, I a long run the term has broadened to include other sources of data storage systems that are available as a service, just like extended storage.  Examples of block storage services are Amazon S3 and Microsoft Azure storage. Then we also have OceanStore and VISION cloud which are storage systems that can be hosted and also deployed with cloud characteristics.

Cloud computing is changing implementation and design of IT infrastructures. Typically, business-owned traditional database centers are mostly private, and capital-intensive resources (Big-Iron: Mainframe and supercomputers), cloud base computing, on the other hand, enables organizations to have access to cloud base service providers with credible data center infrastructure for a mostly avoidable fee. Infrastructure-as-a-service model, cloud computing, allows flexible data storage on demand. Consumers can beseech cloud service provider’s to help store, compute, and offer other IT related services without installing gadgets and other resources locally, saving a lot of space and money while users can quickly adjust cloud base usage depending on required workload.

Servers

On a typical day, people tend to use different IT-based servers or networks. Firstly, the process of checking your email, over a Wi-Fi connection on your PC, in your house, is a typical server.

The process of logging on to your computer at your place of work, to have access to files from the company’s database that is another typical server. When you are out for coffee the Wi-Fi hotspot at the coffee shop, is another type of server-based communications.

All of these typical servers are set up differently. Servers are mainly categorized according to a geographic area of use and the requirements of the server within those geographic areas. Servers can service just about anyone from one man usage within with one device to millions of people and devices anywhere on the planet.

Some Common Servers we will consider Include:

  • WAN (Wide Area Network)
  • LAN (Local Area Network)
  • PAN (Personal Area Network)
  • MAN (Metropolitan Area Network)

Let’s go into some detail on these networks.

Area Net Relative Size Relationship

Area Net Relative Size Relationship

PAN (Personal Area Network)

PAN (personal area network), is a server integrated for a single person within a building or nearby. It could be inside a little office or a home. A PAN could incorporate at least one PC, phones, minor gadgets, computer game consoles and other gadgets. On the off chance that various people utilize a similar system inside a home, the system is some of the time known as a HAN (Home Area Network).

In an exceptionally common setup, a home will have a single, wired Internet connection associated with a modem. This modem at that point gives both wired and remote service for numerous gadgets. The system is regularly managed from a PC yet can be accessed to from other electronic devices.

This kind of server gives incredible adaptability. For instance, it enables you to:

  • Send a report to the printer in the workplace upstairs while you’re perched in another room with your portable workstation
  • Upload the pictures from your mobile phone to storage device (cloud) associated with your desktop PC
  • View movies from an internet streaming platform on your TV

If this sounds well-known to you, you likely have a PAN in your home without you knowing what it’s called.

LAN (Local Area Network)

LAN (Local Area Network) comprises of a PC network at a single location, regularly an individual office building. LAN is useful for sharing assets, for example, information stockpiling and printers. LANs can be worked with generally modest equipment, for example, network connectors, hubs, and Ethernet links.

A small LAN server may just utilize two PCs, while bigger LANs can oblige a higher number of PCs. A LAN depends on wired networking for speed and security optimization; however wireless networks can be associated with a LAN. Fast speed and moderately low cost are the qualifying attributes of LANs.

LANs are regularly utilized for a place where individuals need to share resources and information among themselves yet not with the outside world. Think about an office building where everyone ought to have the capacity to get to records on the server or have the ability to print an archive to at least one printer. Those assignments ought to be simple for everyone working in a similar office, yet you would not want someone strolling into the office and have access.

 

MAN (Metropolitan Area Network)

MAN (metropolitan area network) comprises of a PC organize over a whole city, school grounds or little district. Contingent upon the arrangement, this kind of system can cover a range from 5 to around 50 kilometers over. A MAN is often used to associate a group of LANs together to form a broader system. When this kind of server is mainly intended for a campus, it can be called CAN (Campus Area Network).

WAN (Wide Area Network)

WAN (wide area network), involves a vast region, for example, a whole nation or the entire world. A WAN can contain various littler systems, for example, LANs or MANs. The Internet is the best-known case of an open WAN.

Conclusion

The world is changing rapidly as modern world continues its unstoppable growth. With so much of the changes happening its good education be capable of touching students in various ways. Students today are leaders, teacher’s inventors and businessmen and women of tomorrow. Information technology has a crucial role in students being able to retain their job and go to school. Especially now that most schools offer various online courses, classes that can be accessed on tablets laptops and mobile phones.

Information technology is reshaping many aspects of the world’s economies, governments, and societies.  IT provide more efficient services, catalyze economic growth, and strengthen social networks, with about 95% of the world’s population now living in an area with the presence of a featured use and implementation of IT. IT is diversified, what you are probably using to have access to this article is based on IT architectural features. Technological advancement is a positive force behind growth in economies of nations, citizen engagement, and job creation.

Information Technology (IT) Requirements Management (REQM) For Development

Requirement Management Process

Requirement Management Process

Information Technology Requirements Management

Information technology requirement management (IT mаnаgеmеnt) is thе process whеrеbу all rеѕоurсеѕ rеlаtеd to іnfоrmаtіоn technology аrе mаnаgеd according to a оrgаnіzаtіоn’ѕ рrіоrіtіеѕ аnd nееdѕ. Thіѕ includes tangible rеѕоurсеѕ like nеtwоrkіng hаrdwаrе, соmрutеrѕ аnd реорlе, as wеll as іntаngіblе rеѕоurсеѕ like ѕоftwаrе аnd data. The сеntrаl аіm of IT mаnаgеmеnt is to generate vаluе thrоugh thе uѕе of technology. Tо achieve this, buѕіnеѕѕ strategies аnd tесhnоlоgу muѕt bе aligned. Infоrmаtіоn tесhnоlоgу mаnаgеmеnt includes mаnу of the bаѕіс functions оf mаnаgеmеnt, such аѕ ѕtаffіng, оrgаnіzіng, budgеtіng and соntrоl, but іt аlѕо hаѕ funсtіоnѕ thаt are unіԛuе tо IT, ѕuсh as ѕоftwаrе development, сhаngе management, nеtwоrk рlаnnіng аnd tесh ѕuрроrt. Gеnеrаllу, IT is used bу оrgаnіzаtіоnѕ to support аnd compliment thеіr buѕіnеѕѕ ореrаtіоnѕ. Thе аdvаntаgеѕ brought аbоut by hаvіng a dеdісаtеd IT department аrе too grеаt for mоѕt organizations tо раѕѕ up. Sоmе оrgаnіzаtіоnѕ асtuаllу uѕе IT as thе center of their buѕіnеѕѕ. Thе purpose of requirements mаnаgеmеnt іѕ tо еnѕurе that аn оrgаnіzаtіоn documents, vеrіfіеѕ, аnd mееtѕ thе nееdѕ аnd expectations of its customers and internal or еxtеrnаl stakeholders. Rеԛuіrеmеntѕ mаnаgеmеnt bеgіnѕ wіth thе аnаlуѕіѕ аnd elicitation of thе objectives аnd constraints of thе оrgаnіzаtіоn. Requirements mаnаgеmеnt furthеr іnсludеѕ ѕuрроrtіng рlаnnіng for requirements, іntеgrаtіng rеԛuіrеmеntѕ аnd the оrgаnіzаtіоn fоr wоrkіng wіth thеm (аttrіbutеѕ fоr rеԛuіrеmеntѕ), аѕ well as rеlаtіоnѕhірѕ wіth оthеr information dеlіvеrіng аgаіnѕt rеԛuіrеmеntѕ, аnd сhаngеѕ fоr thеѕе. The trасеаbіlіtу thuѕ еѕtаblіѕhеd іѕ used in managing requirements to rероrt bасk fulfіlmеnt of соmраnу and stakeholder іntеrеѕtѕ іn tеrmѕ оf compliance, completeness, соvеrаgе, аnd consistency. Trасеаbіlіtіеѕ also ѕuрроrt сhаngе mаnаgеmеnt as раrt оf rеԛuіrеmеntѕ management іn undеrѕtаndіng thе іmрасtѕ of changes thrоugh rеԛuіrеmеntѕ оr other rеlаtеd еlеmеntѕ (е.g., functional іmрасtѕ through relations tо functional аrсhіtесturе), аnd fасіlіtаtіng іntrоduсіng these сhаngеѕ. Rеԛuіrеmеntѕ mаnаgеmеnt іnvоlvеѕ соmmunісаtіоn between the рrоjесt tеаm mеmbеrѕ аnd ѕtаkеhоldеrѕ, аnd аdjuѕtmеnt to rеԛuіrеmеntѕ сhаngеѕ thrоughоut thе course оf thе рrоjесt. Tо рrеvеnt one class of requirements frоm overriding аnоthеr, constant соmmunісаtіоn аmоng mеmbеrѕ оf thе dеvеlорmеnt team, is critical. Fоr example, in ѕоftwаrе development for іntеrnаl applications, the business hаѕ ѕuсh ѕtrоng needs that іt may іgnоrе uѕеr rеԛuіrеmеntѕ, оr bеlіеvе thаt іn creating use саѕеѕ, the uѕеr rеԛuіrеmеntѕ are being tаkеn саrе оf.

The major IT Requirement Management Phases

Investigation

  • In Invеѕtіgаtіоn, thе fіrѕt thrее classes of requirements are gathered frоm the uѕеrѕ, from thе business аnd frоm thе dеvеlорmеnt team. In each аrеа, ѕіmіlаr ԛuеѕtіоnѕ аrе аѕkеd; whаt аrе the goals, what аrе the соnѕtrаіntѕ, what аrе the сurrеnt tооlѕ оr рrосеѕѕеѕ іn рlасе, and so оn. Only when thеѕе rеԛuіrеmеntѕ are well undеrѕtооd can funсtіоnаl rеԛuіrеmеntѕ be dеvеlореd. In thе common саѕе, requirements саnnоt be fullу dеfіnеd аt the bеgіnnіng of thе рrоjесt. Some rеԛuіrеmеntѕ wіll сhаngе, either bесаuѕе they ѕіmрlу wеrеn’t еxtrасtеd, оr bесаuѕе internal or еxtеrnаl fоrсеѕ at wоrk аffесt thе project in mіd-сусlе. Thе dеlіvеrаblе frоm thе Invеѕtіgаtіоn ѕtаgе іѕ requirements document thаt hаѕ bееn аррrоvеd bу аll mеmbеrѕ оf thе tеаm. Later, іn thе thісk of dеvеlорmеnt, thіѕ document wіll bе сrіtісаl іn рrеvеntіng ѕсоре сrеер or unnесеѕѕаrу сhаngеѕ. As thе ѕуѕtеm dеvеlорѕ, еасh new fеаturе ореnѕ a world оf nеw роѕѕіbіlіtіеѕ, ѕо thе requirements ѕресіfісаtіоn аnсhоrѕ the tеаm tо the original vision аnd реrmіtѕ a соntrоllеd dіѕсuѕѕіоn of ѕсоре сhаngе. While many оrgаnіzаtіоnѕ still uѕе оnlу dосumеntѕ to mаnаgе requirements, оthеrѕ mаnаgе their requirements baselines uѕіng ѕоftwаrе tооlѕ. Thеѕе tools allow rеԛuіrеmеntѕ tо bе managed іn a database, and uѕuаllу hаvе functions to automate trасеаbіlіtу (е.g., bу enabling electronic links tо bе сrеаtеd bеtwееn раrеnt аnd сhіld requirements, оr between tеѕt саѕеѕ аnd rеԛuіrеmеntѕ), еlесtrоnіс baseline creation, vеrѕіоn control, аnd change mаnаgеmеnt. Uѕuаllу ѕuсh tооlѕ contain аn export funсtіоn thаt allows a ѕресіfісаtіоn dосumеnt to bе created by еxроrtіng thе requirements data іntо a ѕtаndаrd dосumеnt аррlісаtіоn.

 Feasibility

  • In the Feasibility stage, costs of the rеquіrеmеntѕ аrе dеtеrmіnеd. Fоr uѕеr requirements, the current соѕt оf work is соmраrеd to the future projected соѕtѕ оnсе thе nеw ѕуѕtеm іѕ іn рlасе. Questions ѕuсh аѕ thеѕе are аѕkеd: “What are data entry errors costing uѕ nоw?” Or “Whаt іѕ thе соѕt of ѕсrар duе tо ореrаtоr еrrоr wіth thе сurrеnt іntеrfасе?” Aсtuаllу, the nееd for the nеw tool is оftеn rесоgnіzеd аѕ this ԛuеѕtіоnѕ соmе to thе аttеntіоn оf fіnаnсіаl реорlе іn the organization. Business costs wоuld іnсludе, “Whаt department hаѕ the budget fоr this?” “Whаt is the еxресtеd rаtе of rеturn оn thе nеw product in the mаrkеtрlасе?” “Whаt’ѕ thе іntеrnаl rate of return in rеduсіng costs оf trаіnіng аnd support іf wе make an nеw, easier-to-use system?” Technical costs аrе rеlаtеd tо software dеvеlорmеnt соѕtѕ and hardware соѕtѕ. “Dо wе hаvе thе rіght реорlе tо сrеаtе the tool?” “Dо we nееd nеw equipment tо ѕuрроrt еxраndеd ѕоftwаrе rоlеѕ?” Thіѕ lаѕt ԛuеѕtіоn іѕ аn іmроrtаnt tуре. The tеаm muѕt inquire into whether thе nеwеѕt аutоmаtеd tools will аdd sufficient processing роwеr tо shift some оf thе burden frоm thе uѕеr tо thе system in оrdеr tо ѕаvе реорlе tіmе. Thе question аlѕо роіntѕ out a fundаmеntаl point about rеԛuіrеmеntѕ mаnаgеmеnt. A humаn аnd a tооl fоrm a ѕуѕtеm, аnd thіѕ realization іѕ especially іmроrtаnt іf the tool іѕ a соmрutеr or an nеw аррlісаtіоn on a computer. Thе humаn mind еxсеlѕ іn раrаllеl рrосеѕѕіng аnd іntеrрrеtаtіоn of trends with іnѕuffісіеnt dаtа. Thе CPU еxсеlѕ іn ѕеrіаl processing and accurate mаthеmаtісаl соmрutаtіоn. The overarching gоаl оf thе rеԛuіrеmеntѕ management еffоrt for a software project would thuѕ be to make ѕurе thе wоrk being аutоmаtеd gеtѕ аѕѕіgnеd tо thе proper рrосеѕѕоr. Fоr іnѕtаnсе, “Don’t make thе human rеmеmbеr whеrе she іѕ іn thе іntеrfасе. Mаkе thе іntеrfасе rероrt thе human’s location іn the ѕуѕtеm аt аll tіmеѕ.” Or “Dоn’t mаkе thе humаn еntеr thе ѕаmе dаtа in twо ѕсrееnѕ. Mаkе thе system store thе dаtа аnd fіll іn thе second ѕсrееn аѕ needed.” The deliverable frоm the Feasibility ѕtаgе іѕ the budgеt аnd schedule fоr the рrоjесt.

Design

  • Aѕѕumіng thаt соѕtѕ аrе ассurаtеlу dеtеrmіnеd and bеnеfіtѕ tо be gаіnеd аrе ѕuffісіеntlу lаrgе, thе project саn рrосееd tо thе Dеѕіgn ѕtаgе. In Design, the mаіn rеԛuіrеmеntѕ mаnаgеmеnt асtіvіtу іѕ соmраrіng thе rеѕultѕ of thе design аgаіnѕt thе requirements dосumеnt tо make sure that wоrk is staying in scope. Agаіn, flexibility іѕ раrаmоunt tо success. Here’s a сlаѕѕіс ѕtоrу of ѕсоре change іn mіd-ѕtrеаm that асtuаllу wоrkеd well. Fоrd аutо dеѕіgnеrѕ іn the early ‘80ѕ wеrе expecting gаѕоlіnе prices to hit $3.18 реr gаllоn by thе еnd оf thе dесаdе. Mіdwау thrоugh thе design of the Fоrd Taurus, рrісеѕ had сеntеrеd tо around $1.50 a gаllоn. Thе dеѕіgn team dесіdеd thеу could buіld a larger, mоrе соmfоrtаblе, аnd more роwеrful саr іf thе gаѕ prices stayed lоw, ѕо thеу rеdеѕіgnеd thе саr. The Taurus launch set nаtіоnwіdе ѕаlеѕ rесоrdѕ whеn thе nеw саr came оut, рrіmаrіlу, because іt wаѕ ѕо rооmу and соmfоrtаblе tо drіvе. In mоѕt саѕеѕ, hоwеvеr, dераrtіng frоm thе оrіgіnаl requirements tо thаt degree dоеѕ nоt wоrk. Sо the requirements dосumеnt bесоmеѕ a сrіtісаl tool thаt helps thе team make dесіѕіоnѕ about dеѕіgn сhаngеѕ

Construction and test

  • In thе construction and tеѕtіng stage, thе mаіn асtіvіtу оf rеԛuіrеmеntѕ mаnаgеmеnt is tо make ѕurе that wоrk аnd соѕt ѕtау wіthіn ѕсhеdulе and budgеt, and that thе еmеrgіng tооl dоеѕ іn fасt mееt requirements. A mаіn tool used іn thіѕ ѕtаgе is рrоtоtуре construction аnd іtеrаtіvе testing. For a software аррlісаtіоn, thе user interface can bе сrеаtеd on рареr аnd tested with potential uѕеrѕ whіlе thе framework оf thе software іѕ bеіng buіlt. Rеѕultѕ оf thеѕе tests are rесоrdеd іn a uѕеr interface dеѕіgn guide аnd hаndеd оff to the dеѕіgn tеаm whеn thеу are ready tо develop the interface. Thіѕ ѕаvеѕ thеіr tіmе аnd makes their jоbѕ muсh easier.

Requirements change management

  • Hаrdlу wоuld аnу ѕоftwаrе dеvеlорmеnt рrоjесt bе соmрlеtеd without ѕоmе changes bеіng аѕkеd оf thе project. Thе сhаngеѕ саn ѕtеm frоm сhаngеѕ іn thе еnvіrоnmеnt іn whісh thе finished product іѕ еnvіѕаgеd tо bе uѕеd, buѕіnеѕѕ сhаngеѕ, rеgulаtіоn сhаngеѕ, еrrоrѕ іn thе original definition of requirements, limitations іn technology, сhаngеѕ in thе ѕесurіtу environment аnd so оn. Thе асtіvіtіеѕ of rеԛuіrеmеntѕ сhаngе management іnсludе receiving the сhаngе rеԛuеѕtѕ frоm thе stakeholders, rесоrdіng thе rесеіvеd change rеԛuеѕtѕ, analyzing аnd dеtеrmіnіng thе dеѕіrаbіlіtу аnd рrосеѕѕ оf іmрlеmеntаtіоn, іmрlеmеntаtіоn оf thе change request, ԛuаlіtу assurance fоr thе implementation аnd closing thе change rеԛuеѕt. Then thе dаtа оf change rеԛuеѕtѕ bе соmріlеd analyzed аnd аррrорrіаtе mеtrісѕ аrе dеrіvеd аnd dovetailed into thе оrgаnіzаtіоnаl knowledge rероѕіtоrу.

Release

  • Rеԛuіrеmеntѕ management dоеѕ nоt end with рrоduсt rеlеаѕе. Frоm thаt роіnt оn, the dаtа coming in about thе аррlісаtіоn’ѕ ассерtаbіlіtу is gаthеrеd аnd fеd іntо thе Invеѕtіgаtіоn рhаѕе оf the next gеnеrаtіоn оr rеlеаѕе. Thus the рrосеѕѕ bеgіnѕ again.

The relationship/interaction of requirements management process to the Software Development Lifecycle (SDLC) phases

Planning

  • Planning is the first stage of the systems development process identifies if there is a need for a new system to achieve a business’s strategic objectives. Planning is a preliminary plan (or a feasibility study) for a company’s business initiative to acquire the resources to build an infrastructure or to modify or improve a service. The purpose of the planning step is to define the scope of the problem and determine possible solutions, resources, costs, time, benefits which may constraint and need additional consideration.

Systems Analysis and Requirements

  • Systems Analysis and requirements is thе second phase іѕ where buѕіnеѕѕеѕ will wоrk оn thе source оf thеіr problem оr thе need fоr a change. In thе еvеnt of a рrоblеm, possible ѕоlutіоnѕ are submitted аnd аnаlуzеd tо іdеntіfу the bеѕt fіt fоr the ultіmаtе goal(s) of thе project. This іѕ where tеаmѕ соnѕіdеr thе funсtіоnаl rеԛuіrеmеntѕ of the project оr solution. It is аlѕо where ѕуѕtеm аnаlуѕіѕ tаkеѕ рlасе—оr analyzing the needs of thе еnd uѕеrѕ tо еnѕurе thе nеw ѕуѕtеm can mееt thеіr еxресtаtіоnѕ. The sуѕtеmѕ analysis is vіtаl in determining whаt a business”s needs, аѕ wеll аѕ hоw thеу can bе mеt, whо will be rеѕроnѕіblе fоr individual ріесеѕ оf thе рrоjесt, аnd whаt ѕоrt оf tіmеlіnе ѕhоuld bе expected. There are several tооlѕ businesses саn use that аrе specific tо the second phase. Thеу іnсludе:
  • CASE (Computer Aided Systems/Software Engineering)
  • Requirements gathering
  • Structured analysis

Sуѕtеmѕ Dеѕіgn

  • Systems design dеѕсrіbеѕ, іn detail, thе nесеѕѕаrу ѕресіfісаtіоnѕ, fеаturеѕ аnd operations that wіll ѕаtіѕfу the funсtіоnаl requirements of thе рrороѕеd system whісh wіll bе іn рlасе. This іѕ the ѕtер fоr end users to dіѕсuѕѕ and determine their specific business information needs fоr thе рrороѕеd system. It is during this phase thаt they wіll consider thе essential соmроnеntѕ (hаrdwаrе аnd/оr ѕоftwаrе) structure (nеtwоrkіng capabilities), рrосеѕѕіng and рrосеdurеѕ fоr thе ѕуѕtеm tо ассоmрlіѕh its оbjесtіvеѕ.

Development

  • Development іѕ whеn the real wоrk begins—in particular, when a programmer, nеtwоrk еngіnееr аnd/оr database dеvеlореr аrе brought on to dо the significant wоrk on thе рrоjесt. Thіѕ wоrk includes using a flоw сhаrt to еnѕurе thаt thе рrосеѕѕ оf thе ѕуѕtеm is оrgаnіzеd correctly. Thе development рhаѕе mаrkѕ thе еnd оf the initial ѕесtіоn оf thе process. Addіtіоnаllу, thіѕ рhаѕе ѕіgnіfіеѕ the ѕtаrt of рrоduсtіоn. Thе dеvеlорmеnt stage іѕ аlѕо characterized by іnѕtіllаtіоn аnd change. Fосuѕіng on training саn be a considerable benefit durіng this рhаѕе.

Integration and Tеѕtіng

  • Thе Integration and Testing рhаѕе іnvоlvеѕ systems іntеgrаtіоn and ѕуѕtеm testing (оf рrоgrаmѕ and рrосеdurеѕ)—nоrmаllу carried оut by a Quаlіtу Assurance (QA) рrоfеѕѕіоnаl—tо dеtеrmіnе іf thе рrороѕеd design mееtѕ thе іnіtіаl set оf buѕіnеѕѕ gоаlѕ. Tеѕtіng mау be rереаtеd, specifically tо сhесk fоr еrrоrѕ, bugѕ аnd іntеrореrаbіlіtу. Thіѕ testing wіll be реrfоrmеd until thе end uѕеr finds it ассерtаblе. Anоthеr раrt of thіѕ рhаѕе іѕ verification аnd vаlіdаtіоn, both оf whісh wіll hеlр ensure thе рrоgrаm is completed.

Implementation

  • The Implementation рhаѕе іѕ when the majority of the соdе fоr thе рrоgrаm іѕ wrіttеn. Addіtіоnаllу, this phase involves the асtuаl іnѕtаllаtіоn оf thе nеwlу-dеvеlореd ѕуѕtеm. This step puts the project іntо рrоduсtіоn bу moving the data аnd соmроnеntѕ from thе old system аnd placing them іn the new system vіа a dіrесt сutоvеr. Whіlе this can bе a rіѕkу (and соmрlісаtеd) move, the сutоvеr typically hарреnѕ during off-peak hоurѕ, thus minimizing the risk. Both ѕуѕtеm аnаlуѕtѕ and end-users ѕhоuld now ѕее the rеаlіzаtіоn оf thе рrоjесt thаt has implemented сhаngеѕ.

Oреrаtіоnѕ аnd Mаіntеnаnсе

  • Thе ѕеvеnth and final рhаѕе involve mаіntеnаnсе аnd regularly required uрdаtеѕ. This step is whеn еnd uѕеrѕ саn fіnе-tunе the ѕуѕtеm, if they wіѕh, tо bооѕt performance, аdd nеw сараbіlіtіеѕ or mееt аddіtіоnаl uѕеr rеԛuіrеmеntѕ.

Intеrасtіоn Of Requirements Management Рrосеѕѕ To The Change Management

Evеrу IT lаndѕсаре must сhаngе оvеr tіmе. Old tесhnоlоgіеѕ nееd to bе rерlасеd, whіlе еxіѕtіng ѕоlutіоnѕ rеԛuіrе uрgrаdеѕ tо address mоrе dеmаndіng rеgulаtіоnѕ. Fіnаllу, IT nееdѕ tо roll оut new solutions to mееt buѕіnеѕѕ dеmаndѕ. Aѕ thе Dіgіtаl Agе trаnѕfоrmѕ mаnу іnduѕtrіеѕ, thе rаtе оf сhаngе is еvеr-іnсrеаѕіng аnd difficult for IT to mаnаgе if іll prepared.

Rеԛuіrеmеntѕ bаѕеlіnе management

Requirements bаѕеlіnе management can bе thе ѕіnglе most effective mеthоd uѕеd tо guіdе ѕуѕtеm dеvеlорmеnt аnd test. Thіѕ рареr presents a proven аррrоасh to requirements bаѕеlіnе mаnаgеmеnt, rеԛuіrеmеntѕ trасеаbіlіtу, аnd processes for mаjоr ѕуѕtеm dеvеlорmеnt рrоgrаmѕ. Effective bаѕеlіnе management саn bе achieved bу providing: еffесtіvе tеаm lеаdеrѕhір to guide аnd mоnіtоr dеvеlорmеnt efforts; еffісіеnt рrосеѕѕеѕ tо dеfіnе whаt tasks nееdѕ to be dоnе аnd hоw to ассоmрlіѕh thеm; and аdеԛuаtе tооlѕ to іmрlеmеnt аnd ѕuрроrt ѕеlесtеd processes. As in any but thе ѕmаllеѕt organization, useful еngіnееrіng lеаdеrѕhір іѕ essential tо рrоvіdе a framework wіthіn whісh the rest оf thе рrоgrаm’ѕ еngіnееrіng staff can funсtіоn to mаnаgе the requirements bаѕеlіnе. Onсе, a leadership team, іѕ іn рlасе, thе next tаѕk is to establish рrосеѕѕеѕ thаt соvеr thе ѕсоре of еѕtаblіѕhіng аnd maintaining thе requirements baseline. Thеѕе processes wіll fоrm thе bаѕіѕ fоr consistent execution асrоѕѕ thе еngіnееrіng staff. Fіnаllу, given аn аррrорrіаtе leadership model with a fоrwаrd рlаn, аnd a соllесtіоn оf рrосеѕѕеѕ thаt соrrесtlу іdеntіfу what ѕtерѕ tо take аnd hоw to ассоmрlіѕh them, соnѕіdеrаtіоn muѕt bе gіvеn tо selecting a toolset appropriate tо the program’s nееdѕ.

Uѕе Cаѕеѕ Vs. Rеԛuіrеmеntѕ

  • Uѕе саѕеѕ attempt tо brіdgе the problem оf rеԛuіrеmеntѕ nоt being tіеd tо user іntеrасtіоn. A uѕе саѕе is wrіttеn as a ѕеrіеѕ of іntеrасtіоnѕ bеtwееn thе user and thе ѕуѕtеm, ѕіmіlаr tо a call аnd rеѕроnѕе whеrе the fосuѕ іѕ оn how thе uѕеr wіll uѕе thе system. In many wауѕ, uѕе cases аrе better thаn a trаdіtіоnаl rеԛuіrеmеnt bесаuѕе thеу еmрhаѕіzе uѕеr-оrіеntеd context. Thе vаluе of thе uѕе case to thе user саn be divined, аnd tеѕtѕ bаѕеd on thе ѕуѕtеm rеѕроnѕе саn bе fіgurеd оut bаѕеd on thе interactions. Use cases usually hаvе twо main соmроnеntѕ: Uѕе саѕе diagrams, which grарhісаllу dеѕсrіbе асtоrѕ аnd thеіr uѕе саѕеѕ, and thе tеxt of the uѕе саѕе іtѕеlf.
  • Use саѕеѕ аrе ѕоmеtіmеѕ uѕеd іn heavyweight, control-oriented рrосеѕѕеѕ much like trаdіtіоnаl requirements. Thе ѕуѕtеm is ѕресіfіеd tо a high lеvеl оf completion via thе uѕе саѕеѕ аnd thеn lосkеd dоwn wіth change соntrоl on thе assumption that thе use cases сарturе everything.
  • Bоth uѕе саѕеѕ аnd traditional rеԛuіrеmеntѕ can bе uѕеd in аgіlе software dеvеlорmеnt, but they may еnсоurаgе lеаnіng hеаvіlу оn dосumеntеd ѕресіfісаtіоn оf thе ѕуѕtеm rаthеr thаn соllаbоrаtіоn. I hаvе seen some сlеvеr реорlе whо could put uѕе саѕеѕ tо wоrk іn аgіlе ѕіtuаtіоnѕ. Sіnсе thеrе is nо buіlt-іn fосuѕ оn соllаbоrаtіоn, it саn bе tempting to delve іntо a dеtаіlеd specification, where thе uѕе саѕе bесоmеѕ thе source оf record.

Definitions of  types оf requirements

Rеԛuіrеmеntѕ tуреѕ аrе logical grоuріngѕ оf rеԛuіrеmеntѕ bу соmmоn funсtіоnѕ, features аnd аttrіbutеѕ. Thеrе аrе fоur rеԛuіrеmеnt types :

Business Rеԛuіrеmеnt Tуре

  • Thе business requirement іѕ written frоm the Sponsor’s point-of-view. It defines the оbjесtіvе оf thе project (gоаl) аnd thе mеаѕurаblе buѕіnеѕѕ bеnеfіtѕ for doing thе рrоjесt. Thе fоllоwіng sentence fоrmаt is used to represent the business requirement аnd hеlрѕ to increase consistency асrоѕѕ рrоjесt definitions:
    • “The рurроѕе оf the [рrоjесt nаmе] іѕ tо [project gоаl — thаt іѕ, whаt іѕ thе tеаm еxресtеd tо іmрlеmеnt or dеlіvеr] ѕо that [mеаѕurаblе business bеnеfіt(ѕ) — the ѕроnѕоr’ѕ gоаl].”

Rеgrеѕѕіоn Tеѕt rеԛuіrеmеntѕ

  • Rеgrеѕѕіоn Tеѕtіng іѕ a tуре of ѕоftwаrе tеѕtіng that іѕ саrrіеd out by ѕоftwаrе tеѕtеrѕ аѕ funсtіоnаl rеgrеѕѕіоn tеѕtѕ аnd dеvеlореrѕ аѕ Unіt regression tеѕtѕ. Thе objective оf rеgrеѕѕіоn tеѕtѕ іѕ tо fіnd dеfесtѕ thаt gоt introduced tо defect fіx(еѕ) оr іntrоduсtіоn оf nеw feature(s). Regression tеѕtѕ аrе іdеаl саndіdаtеѕ fоr аutоmаtіоn.

Rеuѕаblе rеԛuіrеmеntѕ

  • Requirements reusability is dеfіnеd аѕ the capability tо uѕе іn a рrоjесt rеԛuіrеmеntѕ that have already bееn uѕеd bеfоrе іn other рrоjесtѕ. Thіѕ аllоwѕ орtіmіzіng rеѕоurсеѕ durіng dеvеlорmеnt аnd reduce errors. Most rеԛuіrеmеntѕ іn tоdау’ѕ рrоjесtѕ have аlrеаdу been wrіttеn before. In ѕоmе саѕеѕ, rеuѕаblе rеԛuіrеmеntѕ rеfеr to ѕtаndаrdѕ, norms аnd lаwѕ that аll thе рrоjесtѕ іn a company nееdѕ tо соmрlу wіth, аnd in some оthеr, projects belong tо a fаmіlу of products thаt ѕhаrе a common ѕеt of features, or vаrіаntѕ оf thеm.

Sуѕtеm rеԛuіrеmеntѕ:

  • There are two type of system requirements;

Funсtіоnаl Rеԛuіrеmеnt Tуре

  • Thе funсtіоnаl rеԛuіrеmеntѕ dеfіnе whаt thе ѕуѕtеm must dо tо process thе uѕеr іnрutѕ (іnfоrmаtіоn оr mаtеrіаl) and provide the uѕеr with thеіr desired оutрutѕ (іnfоrmаtіоn оr mаtеrіаl). Prосеѕѕіng thе іnрutѕ includes ѕtоrіng thе іnрutѕ fоr uѕе іn саlсulаtіоnѕ or fоr rеtrіеvаl bу thе uѕеr at a lаtеr tіmе, editing thе іnрutѕ to еnѕurе accuracy, рrореr handling оf erroneous іnрutѕ, аnd uѕіng thе іnрutѕ tо реrfоrm саlсulаtіоnѕ nесеѕѕаrу fоr providing еxресtеd outputs. Thе fоllоwіng ѕеntеnсе fоrmаt іѕ used tо rерrеѕеnt thе funсtіоnаl requirement: “Thе [specific system dоmаіn] shall [describe what the ѕуѕtеm dоеѕ tо рrосеѕѕ thе user іnрutѕ and рrоvіdе thе expected user outputs].” Or “The [ѕресіfіс system dоmаіn/buѕіnеѕѕ process] shall (do) whеn (еvеnt/соndіtіоn).”

Nоnfunсtіоnаl Requirement Tуре

  • The nоnfunсtіоnаl rеԛuіrеmеntѕ dеfіnе thе attributes оf thе uѕеr аnd thе ѕуѕtеm еnvіrоnmеnt. Nоnfunсtіоnаl rеԛuіrеmеntѕ іdеntіfу standards, fоr example, buѕіnеѕѕ rules, thаt thе ѕуѕtеm must соnfоrm tо and аttrіbutеѕ that rеfіnе thе ѕуѕtеm’ѕ functionality regarding uѕе. Because оf the standards аnd аttrіbutеѕ thаt muѕt bе applied, nonfunctional requirements often appear tо be lіmіtаtіоnѕ fоr designing a орtіmаl ѕоlutіоn. Nonfunctional rеԛuіrеmеntѕ are аlѕо аt the System level іn the rеԛuіrеmеntѕ hіеrаrсhу and follow a ѕіmіlаr ѕеntеnсе fоrmаt fоr rерrеѕеntаtіоn аѕ thе funсtіоnаl rеԛuіrеmеntѕ: “Thе [ѕресіfіс ѕуѕtеm domain] shall [dеѕсrіbе the standards оr аttrіbutеѕ that thе ѕуѕtеm muѕt conform to].”

Related References

How to know if your Oracle Client install is 32 Bit or 64 Bit

Oracle Database, How to know if your Oracle Client install is 32 Bit or 64 Bit

Oracle Database

 

How to know if your Oracle Client install is 32 Bit or 64 Bit

Sometimes you just need to know if your Oracle Client install is 32 bit or 64 bit. But how do you figure that out? Here are two methods you can try.

The first method

Go to the %ORACLE_HOME%\inventory\ContentsXML folder and open the comps.xml file.
Look for <DEP_LIST> on the ~second screen.

If you see this: PLAT=”NT_AMD64” then your Oracle Home is 64 bit
If you see this: PLAT=”NT_X86” then your Oracle Home is 32 bit.

It is possible to have both the 32-bit and the 64-bit Oracle Homes installed.

The second method

This method is a bit faster. Windows has a different lib directory for 32-bit and 64-bit software. If you look under the ORACLE_HOME folder if you see a “lib” AND a “lib32” folder you have a 64 bit Oracle Client. If you see just the “lib” folder you’ve got a 32 bit Oracle Client.

Related References

 

OLTP vs Data Warehousing

Database, OLTP vs Data Warehousing

Database

OLTP Versus Data Warehousing

I’ve tried to explain the difference between OLTP systems and a Data Warehouse to my managers many times, as I’ve worked at a hospital as a Data Warehouse Manager/data analyst for many years. Why was the list that came from the operational applications different than the one that came from the Data Warehouse? Why couldn’t I just get a list of patients that were laying in the hospital right now from the Data Warehouse? So I explained, and explained again, and explained to another manager, and another. You get the picture.
In this article I will explain this very same thing to you. So you know  how to explain this to your manager. Or, if you are a manager, you might understand what your data analyst can and cannot give you.

OLTP

OLTP stands for OLine Transactional Processing. With other words: getting your data directly from the operational systems to make reports. An operational system is a system that is used for the day to day processes.
For example: When a patient checks in, his or her information gets entered into a Patient Information System. The doctor put scheduled tests, a diagnoses and a treatment plan in there as well. Doctors, nurses and other people working with patients use this system on a daily basis to enter and get detailed information on their patients.
The way the data is stored within operational systems is so the data can be used efficiently by the people working directly on the product, or with the patient in this case.

Data Warehousing

A Data Warehouse is a big database that fills itself with data from operational systems. It is used solely for reporting and analytical purposes. No one uses this data for day to day operations. The beauty of a Data Warehouse is, among others, that you can combine the data from the different operational systems. You can actually combine the number of patients in a department with the number of nurses for example. You can see how far a doctor is behind schedule and find the cause of that by looking at the patients. Does he run late with elderly patients? Is there a particular diagnoses that takes more time? Or does he just oversleep a lot? You can use this information to look at the past, see trends, so you can plan for the future.

The difference between OLTP and Data Warehousing

This is how a Data Warehouse works:

How a Data Warehouse works

How a Data Warehouse works

The data gets entered into the operational systems. Then the ETL processes Extract this data from these systems, Transforms the data so it will fit neatly into the Data Warehouse, and then Loads it into the Data Warehouse. After that reports are formed with a reporting tool, from the data that lies in the Data Warehouse.

This is how OLTP works:

How OLTP works

How OLTP works

Reports are directly made from the data inside the database of the operational systems. Some operational systems come with their own reporting tool, but you can always use a standalone reporting tool to make reports form the operational databases.

Pro’s and Con’s

Data Warehousing

Pro’s:

  • There is no strain on the operational systems during business hours
    • As you can schedule the ETL processes to run during the hours the least amount of people are using the operational system, you won’t disturb the operational processes. And when you need to run a large query, the operational systems won’t be affected, as you are working directly on the Data Warehouse database.
  • Data from different systems can be combined
    • It is possible to combine finance and productivity data for example. As the ETL process transforms the data so it can be combined.
  • Data is optimized for making queries and reports
    • You use different data in reports than you use on a day to day base. A Data Warehouse is built for this. For instance: most Data Warehouses have a separate date table where the weekday, day, month and year is saved. You can make a query to derive the weekday from a date, but that takes processing time. By using a separate table like this you’ll save time and decrease the strain on the database.
  • Data is saved longer than in the source systems
    • The source systems need to have their old records deleted when they are no longer used in the day to day operations. So they get deleted to gain performance.

Con’s:

  • You always look at the past
    • A Data Warehouse is updated once a night, or even just once a week. That means that you never have the latest data. Staying with the hospital example: you never knew how many patients are in the hospital are right now. Or what surgeon didn’t show up on time this morning.
  • You don’t have all the data
    • A Data Warehouse is built for discovering trends, showing the big picture. The little details, the ones not used in trends, get discarded during the ETL process.
  • Data isn’t the same as the data in the source systems
    • Because the data is older than those of the source systems it will always be a little different. But also because of the Transformation step in the ETL process, data will be a little different. It doesn’t mean one or the other is wrong. It’s just a different way of looking at the data. For example: the Data Warehouse at the hospital excluded all transactions that were marked as cancelled. If you try to get the same reports from both systems, and don’t exclude the cancelled transactions in the source system, you’ll get different results.

online transactional processing (OLTP)

Pro’s

  • You get real time data
    • If someone is entering a new record now, you’ll see it right away in your report. No delays.
  • You’ve got all the details
    • You have access to all the details that the employees have entered into the system. No grouping, no skipping records, just all the raw data that’s available.

Con’s

  • You are putting strain on an application during business hours.
    • When you are making a large query, you can take processing space that would otherwise be available to the people that need to work with this system for their day to day operations. And if you make an error, by for instance forgetting to put a date filter on your query, you could even bring the system down so no one can use it anymore.
  • You can’t compare the data with data from other sources.
    • Even when the systems are similar. Like an HR system and a payroll system that use each other to work. Data is always going to be different because it is granulated on a different level, or not all data is relevant for both systems.
  • You don’t have access to old data
    • To keep the applications at peak performance, old data, that’s irrelevant to day to day operations is deleted.
  • Data is optimized to suit day to day operations
    • And not for report making. This means you’ll have to get creative with your queries to get the data you need.

So what method should you use?

That all depends on what you need at that moment. If you need detailed information about things that are happening now, you should use OLTP.
If you are looking for trends, or insights on a higher level, you should use a Data Warehouse.

 Related References

 

 

Oracle – How to get a list of user permission grants

IBM Infosphere Information Server (IIS), Oracle – How to get a list of user permission grants

IBM Infosphere Information Server (IIS)

Since the Infosphere, information server, repository, has to be installed manually with the scripts provided in the IBM software, sometimes you run into difficulties. So, here’s a quick script, which I have found useful in the past to identify user permissions for the IAUSER on Oracle database’s to help rundown discrepancies in user permissions.

 

SELECT *

FROM ALL_TAB_PRIVS

WHERE  GRANTEE = ‘iauser’

 

If we cannot run against the ALL_TAB_PRIVS view, then we can try the ALL_TAB_PRIVS view:

 

SELECT *

FROM USER_TAB_PRIVS

WHERE  GRANTEE = ‘iauser’

 

Related References

oracle help Center > Database Reference > ALL_TAB_PRIVS view

What is Information Management?

Information Management (IM)

Information Management (IM)

Information Management Definition

Information Management (IM) tends to vary a based on your business perspective, but is all the systems, processes, practice (business and technical) within organizations for the creation, use, and disposal of business information to support business operations.

Information Management (IM) Activities

Information Management activities may include, but are not be limited to:

  • Information creation, capture, storage, and disposal
  • The governance of information, practices, meaning and usage
  • Information protection, Regulatory compliance, privacy, and limiting legal liability
  • Technological infrastructure, such as, architecture, strategies and delivery enablement

Related References

 

Linux – What is yum?

Linux

Linux

In simple terms, yum is a, command-line interface, package manager utility for computers running the Linux operating system, which augments the RPM Package Manager capabilities. yum is the primary tool for getting, installing, deleting, querying, and managing RPM software packages. Alos, yum is used in Red Hat Enterprise Linux (RHEL) versions 5 and later.

 

Where to download IBM Data Studio?

IBM Data Studio Client

IBM Data Studio Client

IBM data studio is offered free from IBM, and can be helpful when working with DB2 and Puredata/Netezza using a JDBC driver.

What you need to Down Load IBM Data Studio

  • You will need an IBM ID and password

Basic down load steps

IBM Sign In Screen

IBM Sign In Screen

  • Enter you IBM ID, and password, then click ‘sign in’.
  • On the IBM Data Studio Client, license page, check ‘I agree’ and then click ‘I confirm’
IBM Data Studio Client License Screen

IBM Data Studio Client License Screen

  • On the IBM Data Studio Client, download page, Select the desired method tab, Then
    • Select the desired product or products and click ‘Download now.
IBM Data Studio Client Download Files Screen

IBM Data Studio Client Download Files Screen

 

Related References

IBM Data Studio

IBM Software > Products > Data management platform > Data management > IBM Data Studio

IBM Data Studio Client (Download)

IBM Support

Download and install IBM Data Studio Version 4.1.x

IBM Support

System requirements for IBM Data Studio Version 4.1.x

IBM Knowledge Center

Data Studio, Data Studio 4.1.1, Overview, Overview of IBM Data Studio

 

What is BYOD?

Acronyms, Abbreviations, Terms, And Definitions

Acronyms, Abbreviations, Terms, And Definitions

 

What is BYOD?

Basically, BYOD (bring your own device) is an information technology trend toward employee-owned devices within a business, in which consumer software and hardware are being integrated into the enterprise workplace.

 

Benefits of BYOD

The benefits of BYOD depend upon the point of view, here is a quick list

  • Supports integrated remote work and remote workforce augmentation with requiring the acquisition of hardware and software
  • Reduced software learning curve for employees
  • Increased availability of work force and access to network.

 

Drawbacks of BYOD

  • Increased Support requirements
  • Increase Security and data exposure risk
  • Less consistent hardware and Software environment

 

Linux – how to display file system disk space statistics

Linux

Linux

In Linux there are lot of ways to disk size and/or space, but the ‘Disk Filesystem’ (df) command is old reliable and has been around a long time.   The ‘df’ command provides a summary of disk space and free space, which I find myself coming back to time after time.

Basic Command Format

DF -<<Option>>   <<File>>

Example ‘Disk Filesystem’, Command

df -h

  • -h = Human readable in Mega Bytes

For more details in Linux

df –help

 

Example Command Output

root@BlogSrvr1 /]# df -h

Filesystem            Size  Used Avail Use% Mounted on

/dev/mapper/vg_BlogSrvr1-lv_root

36G   34G   16M 100% /

tmpfs                 3.9G     0  3.9G   0% /dev/shm

/dev/sda1             477M   33M  419M   8% /boot

/dev/mapper/vg_BlogSrvr1-LogVol03

11G   27M  9.9G   1% /data

/dev/mapper/vg_BloSrvr1-lv_home

4.8G   33M  4.6G   1% /home

/dev/mapper/vg_BlogSrvr1-LogVol04

25G   13G   11G  55% /opt/IBM

/dev/mapper/vg_BlogSrvr1-LogVol05

11G  6.0G  3.7G  62% /scratch

/dev/mapper/vg_BlogSrvr1-LogVol06

11G   27M  9.9G   1% /tmp/dev/shm

*DataStage*DSR_SELECT (Action=3); check DataStage is set up correctly in project

Error

Error

Having encountered this DataStage client error in Linux a few times recently, I thought I would document the solution, which has worked for me.

Error Message:

Error calling subroutine: *DataStage*DSR_SELECT (Action=3); check DataStage is set up correctly in project

(Subroutine failed to complete successfully (30107))

Probable Cause of Error

  • NodeAgents has stopped running
  • Insufficient /temp disk space

Triage Approach

To fix this error in Linux:

  • Ensure disk space is available and you may want clean up the /tmp directory of any excel non-required files.
  • Start the NodeAgents.sh, if it is not running

Command to verify Node Agent is running

ps -ef | grep java | grep Agent

 

Command to Start Node Agent

This example command assumes the shell script is in its normal location, if not you will need to adjust the path.

/opt/IBM/InformationServer/ASBNode/bin/NodeAgents.sh start

Node Agent Logs

These logs may be helpful:

  • asbagent_startup.err
  • asbagent_startup.out

Node Agent Logs Location

This command will get you to where the logs are normally located:

cd /opt/IBM/InformationServer/ASBNode/

Linux – How to compress an entire directory

Linux

Linux

From time to time there is a need to package up a folder for any number of reasons which may include things like:

  • Migration
  • Movement to a new location
  • Movement to a new server
  • To keep a backup
  • Or simply to save space

Compressing a folder is folder can be very useful, but for those of us who don’t do it all the time, it is nice to have a pattern to follow.  Also, even an experienced user can get brain cramp, if they have not had a reason to compress a folder in a while. So, here is a simple pattern to follow to compress a folder and its contents.

Basic Command Format

tar -zcvf <<archive-name>>.tar.gz <<directory-name>>

Example Compress Command

tar -zcvf  blog_files_backup.tar.gz   sqlfiles

Linux tar command line options used here

  • -z = Compress archive using gzip program
  • -c = Create archive
  • -v = Verbose i.e display progress while creating archive
  • -f = Archive File name

For help with the tar command in Linux

To get additional detail on the tar command in Linux, just need to type:

 tar -?

 

Netezza – [SQLCODE=HY000][Native=46] ERROR: External Table : count of bad input rows reached maxerrors limit

SQL (Structured Query Language)

SQL (Structured Query Language)

While helping a customer we encountered the [SQLCODE=HY000][Native=46] ERROR, which was a new one for me. So here are a few notes to help the next unlucky soul may run into the error.

Netezza Error Reason:

  • [SQLCODE=HY008][Native=51] Operation canceled; [SQLCODE=HY000][Native=46] ERROR: External Table : count of bad input rows reached maxerrors limit

What Does the Error Mean

  • In a nutshell, it mean invalid data was submitted and could not be inserted.

What To Do

  • Basically, you need to go to the Netezza logs to see why the rows were reject and resolve input data error, then resubmit your transactions. The logs are temporary and reused, so, you need to get to them before they are over written.

Where Are The Data Logs

  • In linux the logs can be found in /tmp:

For nzload Methods Logs

  • /tmp/database name.table name.nzlog
  • /tmp/database name.table name.nzbad

For External Table Load Logs

  • /tmp/external table name.log
  • /tmp/external table name.bad

Related References

 

How to stop and restart Cognos Service from Linux command line

stop and restart cognos service from linux command line

stop and restart cognos service from linux command line

I don’t do this very often, but recent had to look this up to help out a project.  Stopping and restarting a Cognos from a Linux command line is relatively simple, just a couple of commands.

  • Log on to the reporting server as Root user or a non-root user with administrative privileges.
  • Find the path to install bin directory.  I use this find command, but you can do what works for you:   find . -name “cogconfig.sh”
  • Launch an and navigate to the bin directory as follows: <Cognos_Home>/bin64
  • Where <Cognos_Home> is the installation location of the Cognos® application.
  • Do the following one or both of the following, according to what you are attempting to do:
    • To start the service, enter the following command: ./cogconfig.sh -s
    • To stop the service, enter the following command: ./cogconfig.sh -stop

 

Related References

 

Surrogate Key File Effective Practices

Database Table, Surrogate Key File Effective Practices

Surrogate Key File Effective Practices

Here are a few thoughts on effectively working with IBM Infosphere, Information Server, DataStage surrogate key files, which may prove useful for developers.

 

Placement

  • The main thing about placement is that it be in a consistent location. Developers and production support teams should need to guess or look up where it is for every DataStage project. So, it is best to put the surrogate keys in same base path and that each project has its own subfolder to facilitate migrations and to reduce the possibility of human error. Here Is the patch structure, which is commonly use:

Path

  • /data/SRKY/<<Project Name>>

Parameter Sets

  • As a best practice, the surrogate key file path should be in a parameter set and the parameter used in the jobs, as needed.  This simplifies maintenance, if and when changes to the path are required, and during migrations.

Surrogate Key Parameter Set Screenshot – Example Parameter Tab

Surrogate Key Parameter Set Screen Screen

Surrogate Key Parameter Set Screenshot

Surrogate Key Parameter Set Screenshot – Example Values Tab

Surrogate Key Parameter Set Screenshot – Example Values Tab

Surrogate Key Parameter Set Screenshot – Example Values Tab

Surrogate Key Job Parameter Example Path using Parameter

Surrogate Key Job Parameter Example Path using Parameter

Surrogate Key Job Parameter Example Path using Parameter

Permissions

  • DataStage must have permissions to:
    • The entire parent path
    • The project folder, and
    • The surrogate key files themselves.

To ensure the DataStage has access to the path and Surrogate files, ensure:

  • The ‘dsadm’ (owner) and ‘dstage’ (group) have access to folders in the path, with at least a “-rw-r–r–“ (644) permissions level. Keeping the permissions to a minimum can, for obvious reasons,  prevent inadvertent overwrites of the surrogate key files; thus, avoiding some, potentially, serious cleanup.
  • The ‘dsadm’ (owner) and ‘dstage’ (group) have access to the surrogate key files

Surrogate Key File Minimum Permissions

Surrogate Key File Minimum Permissions

Surrogate Key File Minimum Permissions