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About Creating an Oracle Database

After you plan your database using some of the guidelines presented in this section, you can create the database with a graphical tool or a SQL command. You typically create a database during Oracle Database software installation. However, you can also create a database after installation. Reasons to create a database after installation are as follows: You used Oracle Universal Installer (OUI) to install software only, and did not create a database. You want to create another database (and database instance) on the same host computer as an existing Oracle database . In this case, this chapter assumes that the new database uses the same Oracle home as the existing database. You can also create the database in a new Oracle home by running OUI again. You want to make a copy of (clone) a database. Considerations Before Creating the Database Database creation prepares several operating system files to work together as an Oracle Database. You only need to create a database...
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What is the Difference between Oracle DBA and Oracle APPS DBA?

Normally people start their journey from a software engineer in Information Technology. We all are aware of few fields of IT like testing engineer, data analyst, software engineer, web designer, coder etc. But in information-technology, we have an ocean of different kind of professions.  In IT sectors administration plays an important role in business. without an administrator, IT projects can not reach to its peak point. “ DBA stands for the Database Administrator” . DBA is a backbone of the entire project of any business. When we talk about DBA profile all kind of the center of attraction goes about lots of responsibilities. DBA works 24/7 for the organization to manage the world’s hustle-bustle. Oracle APPS DBA Today we can not perform the business operation without ERP. ERP stands for “ Enterprise resource planning”.  Small scale or large scale business is incomplete without using ERP. There are over 130 Oracle Applications...
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Business Intelligence Lifecycle

While data is captured in complex structures and databases to facilitate specific transaction requirements, organizations and businesses find it difficult to extract and capture the required information from data in transaction systems. Thus, there was a need to develop a system that can dependably take out data from the source systems and restructure the content appropriate for business analysis. The restructuring of data must be done in a way such that the meaningful data and information is provided to business people through the useful tools they are able to access. Nonetheless,  business intelligence  projects are more time consuming and they require a successful methodology to employ all the business-related operations. According to Kimball’s approach, the business intelligence model suggests: Understanding the requirements and delivering the valuable business output Act in accordance with the proven DW Lifecycle. Building and delivering progressively within the...
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Test Automation – Is it a Specialized Career? Can Normal Testers Do Automation Also?

Over the last decade, test automation has undergone multiple facets of changes. Same vendors have introduced new tools, open source tools have come to stay, and still, some vendors are marketing their products as the ultimate solution for Quality. CTOs of the organizations are convinced that automation is going to give them greater benefits in squeezing the cost and time over long run. Service providers have introduced several ultimate frameworks which save the effort of automation testers – right from standard data-driven, Key-word, Hybrid to script-less frameworks whereby business users can create automation scripts without the hassles of Java or VB scripting knowledge. This ultimately led us to the question if a dedicated automation test scripting community is required or the normal testers can do automation also? There are many articles stating that test automation should be group automation – it should not be a dedicated, aligned team working on automation scripts/projec...
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Kubernetes Vs Docker Swarm: A Comparision of Containerizations Platforms

Container orchestration is fast evolving and Kubernetes and Docker Swarm are the two major players in this field. Both Kubernetes and Docker Swarm are important tools that are used to deploy containers inside a cluster. Kubernetes and Docker Swarm have carved respectable niches for themselves, cementing their places in the Docker ecosystem. Let’s briefly look into Kubernetes and Docker Swarm before moving onto see what are the differences between these two container orchestration tools. Overview of Kubernetes Kubernetes is based on years of Google’s experience of running workloads at a huge scale in production. As per Kubernetes website, “Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications.” We covered the basics on Kubernetes in a previous post. You can read it here if you need a more in depth overview of the platform. Overview of Docker Swarm Docker swarm is Docker’s own container’s orchestration. It uses th...
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How to use Performance Recorder to improve performance in tableau Server

Isolating Tableau Server Performance Issues In this post, I’ll be describing a set of steps to follow to isolate the causes of performance issues on Tableau Server. Here are the basic steps: Test the workbook in Tableau Desktop. Does it perform well? If yes: Test the workbook in Tableau Desktop on the Tableau Server machine. Does it perform the same as it did on the previous machine? If yes: Publish the workbook to Tableau Server, and find a time when there is low-to-no usage on the Tableau Server. Go to the published workbook. Did it perform relatively the same as the test in Step 2 (within 1-3 seconds)?  If yes: Test the workbook during a time of high usage on the Tableau Server (either natural or do load testing using Tabjolt) If it is slow in Tableau Desktop, it will be slow in Tableau Server. The main principle we use to isolate the issue is “ If it is slow in Tableau Desktop, it will be slow in Tableau Server “. You might alternatively think of this in te...
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Top Skills You Need to Become a Data Scientist

Introduction Before beginning your career as a data scientist one must know what  data science  means. The branch of science that is concerned with multidisciplinary combination of development of algorithm, inference of data and technology that is required so that the problems which are complex analytically can be solved. To become a data scientist there should be three main qualities in a person which include expertise in mathematics, technological skills which also include hackings and strategic or business knowledge. Also another important fact is that one should not confuse a data scientist with an analyst. The technical skills involved are  For being a data scientist it is very important to maintain a good result through the academic career with at least 88% in master's degree and also have good marks in PhD. The main subjects that they should know are mathematics, statistics, computer science and engineering. *  The knowledge of python coding is very ...
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