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DevOps (development and operations) is a collection of tools and technologies combined to carry out various business processes. It aims to bridge the gap between two of the most significant departments in any IT organization, the development department and the operations department. This blog will help you get an overview of the numerous concepts that play a significant role in defining DevOps.
Before DevOps came into the limelight, our traditional ol’ IT had two separate teams in an organization – the Development team and the Operations team.
The development team worked on the software, developing it and making sure that the code worked perfectly. After hours of hardwork and a lot of trial and error, the team releases a code which has to be executed by the Operations team which is responsible for the release and operation of the code.
The operations team will be checking the application and its performance and reporting back any bugs, if present.
As simple and planned out as it may sound, the two major teams always had a conflict when it came to execution.
For instance, let us say, the development team developed a code using an i7 processor, 8GB RAM, OS as Ubuntu, and php 5.6 scripting language, where as the Operations team ran the same code using i5 processor, 16GB RAM, OS as Centos and php 7.0 programming language.
When the operations team ran the same code, it wouldn’t work.
The reason for this could be the difference in the system environment or any missing software library.
The operations team flagged this code as faulty, even though the problem could exist in their own system. This resulted in a lot of back and forth between the Developers and the Operations team.
To bridge this gap, Development(‘Dev’) team and Operations (‘Ops’) team collaborated giving rise to DevOps.
For example, to solve the above problem, the Development team encapsulated their code in a container which is a lightweight software environment.
This software environment had all the required software encapsulated in it, which the code or the application will require to run as expected.
When the developers were done with their work, they would simply pass on this container along with the code to the operations team. The Ops will run this container, along with the code, and it worked as expected!
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Separation of concerns
Understand the master-slave approach
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Learn how and when to use a With…End With-statement
Take part in designing the initial stages of the date picker.
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What design? How to Measure? How to Analyze?
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ANALYZE your data correctly using free and easy-to-use software even if you have zero knowledge of statistics.
DRAW compelling conclusions that you can feel confident about and that you can defend against criticism.
What TOOLS will you receive?
FULL RESEARCH CHECKLIST to ensure that your research is complete and criticism-proof.
CONFOUNDER CHECKLIST to address all holes in your research design.
DATA ANALYSIS DECISION CHART to easily select the correct data analysis technique.
Links to free and easy-to-use SOFTWARE for data analysis and sample size calculation.
QUIZZES to solidify and deepen your understanding of each section.
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The Three Elements of an Intriguing Research Question.
How to Formulate an Intriguing Research Question.
A Simple Template For Your Research Question.
How to Define The Variables in Your Research Question.
The Four Elements of Strong Methodology.
Which Methodology to Use? Qualitative vs Quantitative.
How Your Research Design Affects the Interpretation of Your Findings.
Inferring Causation - How to Avoid This Common Mistake!
The Most Effective Way to Rule out Alternative Explanations.
Types of Research Designs.
How to Avoid Unnecessary Complexity in Your Research Design.
Why You Need to Randomize (And How).
Experimental And Non-experimental Designs.
Confounders in Research Designs.
How to Design Successful Non-Experimental Studies.
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The Two Elements of an Effective Measurement.
A Simple Trick To Boost The Reliability of Your Measurements.
Types of Measurements - Which One is Right for You?
What to Do If Your Variables Cannot Be Measured Accurately (Simple Hack).
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Data Collection Strategies And When To Use Which.
Sample size - How Many Participants Do You Need?
The Three Steps of Data Analysis.
Master Free And Easy-to-use Software for Data Analysis (JASP).
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How to Analyze Data with a Numerical IV and Numerical DV (Step-by-step).
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Generalizing a Correlation.
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Generalizing a Difference Between Moments in Time (RM ANOVA).
Generalizing in a Mixed Design (RM ANOVA).
Generalizing a Continuous Effect on Probabilities (Logistic Regression).
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Avoid Mistakes! Two Common Mistakes and How to Prevent Them.
The Two Types of Statistical Errors - And How to Minimize Them.
How to Select The Right Sample Size (Power Analysis).
Master Free and Easy-to-use Software For Sample Size Calculation (G*Power).
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Sample Size Calculation For Logistic Regression.
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Three Simple Strategies to Maximize Your Statistical Power.
How to Test the Reliability of Your Measurements.
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Conducting Your Research: Essential Parts That You Do Not Want to Miss in Any Research Study.
Recruiting: How To Find Participants.
Incentives: How To Motivate People to Participate.
The Four Steps of Interpreting Findings.
How to Interpret Results in an Experimental Design.
How to Interpret Results in a Quasi-Experiment.
How to Interpret Results in a Non-Experimental Design (Categorical IV).
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After you click on the red button, the lectures will guide you through the research process.
You will also receive tools such as checklists, decision charts, and free software to make your work easier.
In addition, you can ask me questions anytime and can have my full support on every step of the way.
