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7 Software Engineering Disciplines: Which Career Path Should You Choose?

7 Software Engineering Disciplines_
7 Software Engineering Disciplines_

Learning software engineering opens the door to many job opportunities, but how do you know which one to pursue? 

This article explores 7 software engineering disciplines, the skills involved, and the expected salary. We also look at an example of what your career path as a software engineer might look like—but remember, the journey will be different for everyone!

Types of software engineering jobs

Within software engineering are a whole array of disciplines to choose from. Finding programming jobs (and similar) is less daunting when you know what your options are and which discipline might suit you best.

1. Front-End engineer

Front-end engineers are responsible for how the front end of a site or application looks and how users can interact with it. They are responsible for planning, building, and implementing the user interface. 

Front-end engineers must learn HTML, CSS, and JavaScript at a minimum. If you have an eye for design and an aptitude for improving user experience, you may be drawn to a career as a front-end engineer. 
According to Indeed, the average salary for a front-end engineer in the UK is around £53,884.

2. Back-End engineer

If the “front end” is the side that users see and interact with, then a back-end engineer is working behind the scenes on the particulars that make the application work. The “back end” includes server and server-side technologies like database design, data integration, and APIs. 

Back-end engineers use a mix of programming languages, such as Java, Golang, Elixir, Python, and C++. If you’re interested in a career as a back-end engineer, it is also worth strengthening soft skills like problem-solving and collaboration. 

According to Indeed, the average salary for a back-end engineer in the UK is around £60,801.

Screenshot taken from Indeed

3. Full stack engineer

Combine the skills and responsibilities of front-end and back-end software engineers, and what do you get? A full stack engineer. 

A full-stack engineer is a high-level software engineer who is expected to work across the entire system. They are responsible for designing, testing, and implementing various software applications, taking responsibility for APIs and databases as well as UX features and user interfaces. 

You’ll notice that job titles like front-end, back-end, and full-stack engineer are quite general: you could be working on anything. For example, do you know the role of artificial intelligence in transportation? As technology continually develops, the possibilities for making a difference via coding skills are endless.

According to Indeed, the average salary for a full-stack engineer in the UK is around £55,780, although the high end is around £67,026.

4. Mobile app developer

Mobile developers build applications for tablets, smartphones, and other mobile devices. These applications will typically be on either iOS or Android operating systems, and engineers sometimes sub-specialise in one of the two.

Mobile app developers are similar to full-stack developers, since both need to cover the front and back ends of an app. They therefore need to be competent with user interfaces and back-end operations and should be proficient in languages like Java and Swift. Creativity and vision are a must to develop unique apps. 

According to Indeed, the average salary for a mobile developer in the UK is around £40,627.

5. Graphic engineer

Which Career Path Should You Choose?

Graphic engineers usually focus either on video games or computer graphics. They tend to work with 2D/3D graphics, physics, and game mechanics. 

Game development is perhaps one of the most exciting disciplines within graphics, and within  software engineering overall. It is an opportunity to design characters and levels, write a story, and test a game that people can access and buy from games consoles, computers, smart phones, and more. 

Specific requirements will differ depending on the project, but most game developers learn C++. This is a role for an individual with a flair for creativity, a passion for storytelling, and a desire to push boundaries in what video games can be. 

It is more difficult to predict salary in more creative roles, but Glassdoor places the salary range between 20k and 40k in the UK.

6. DevOps engineer

DevOps engineers tend to be responsible for feature-facing work rather than client-facing work. Their job concerns infrastructure, automation, release cycles, and sometimes application health. 

DevOps is short for developmental operations: the combination of practices, tools, and cultural philosophies that help an organization deliver applications and services at high velocity. In practice, that may look like managing the programs that allow an organization to deploy their software to big stacks of clusters. 

To succeed as a DevOps engineer, you need to understand the fundamentals of the software development lifecycle (SDLC), including versioning strategies, source control management systems, and CI/CD pipelines. 

Additionally, DevOPs engineers may be involved in managing programs like call center scheduling software, playing a key role in automating resource allocation and workflow for the organization. 

According to Indeed, the average salary for a DevOps engineer in the UK is around £62,756. 

7. Security engineer

Security engineers keep a company’s data and systems safe, taking responsibility for preventing hackers and cyber attacks while protecting against or removing viruses like ReceiverHelper Mac malware.

Tasks include:

  • Planning network and hardware updates
  • Implementing and monitoring an application’s security
  • Testing for exploits
  • Collaborating with other departments to safeguard sensitive data (e.g. in an IVR contact center, communications teams and security engineers would work to keep customer data secure). 
  • Responding to security incidents when they occur 

To do this role well, security engineers should be skilled in a variety of coding languages plus penetration and vulnerability testing. 

According to Indeed, the average salary for a security engineer in the UK is around £37,574.

What does a typical software engineer career path look like?

Whether you learn on the job, get a certification, or earn a Bachelor’s degree at a renowned institute like Torrens University Australia, there are numerous directions your career can take.

Let’s dive into an example software engineer career path:

Junior engineer

Most junior engineers are fresh out of university or college, and have little to no experience in professional software development. 

In this role, they’ll gain experience working with a development team, learning new skills, how to contribute to projects, and how to work to project deadlines. You can expect a steep learning curve at this point in your career. 

While you’re still getting a grasp of the fundamentals, it’s best to be open and receptive to feedback from senior developers.

Senior engineer

A senior engineer—typically someone with 5+ years experience—is generally regarded as the expert on the team. They have seen a plethora of different projects and technologies, and have mastered the software lifecycle.

Responsibilities include:

  • Training new engineers
  • Delivering code
  • Working with data structures and algorithms 
  • Helping with larger initiatives
  • Evaluating and executing long-term project goals
  • Finding ways to improve output
  • Ensuring product quality in the long term

At this stage, your career path starts to branch. You may choose a career in management, or you might decide to delve deeper into the technical realm.

Tech lead

This role varies between organizations, but is generally given to a candidate with 5+ years experience. 

A tech lead works more closely with scoping project roadmaps for their teams and helping manage expectations with stakeholders. They’re responsible for making sure the team they are leading can work fluidly, removing any obstacles to productivity and success. 

They may also take responsibility for domain management and testing—for example, if the company operates in multiple territories and has a Qa domain. 

In some organizations this is purely technical/project work alongside leading a team, while in others being a tech lead is considered management experience.

Management 

In a management role (again, 5+ years experience needed) much less time is spent on technical work. Instead, you’d be focusing on developing your team members in their careers, building a team to help meet the goals of the company, and managing stakeholders on projects. 

Let’s look at some specific managerial positions:

Engineering manager (6+ years)

An engineering manager’s job is to manage a team of engineers day-today, building roadmaps and strategies for projects. They focus on building, coaching, and managing a team of high-performing engineers, and ensuring they deliver on expectations.

VP of engineering (10+ years)

This is a senior-level management position. The VP of engineering must lead and grow several teams, oversee project preparation and approval, organize budgets, oversee hiring, creatively problem-solve, and communicate strategies to other leadership. An upper manager may invest in a human resource information system (HRIS) to streamline HR tasks and manage employee data.

CTO (15+ years)

Chief Technology Officers (CTOs) are in charge of every technical aspect of a company, including engineering and other departments. They must be business-minded, with excellent communication and leadership skills. 

Effective leadership development is crucial for CTOs to navigate the complexities of their role, inspire innovation, foster collaboration across teams, and drive strategic initiatives that align with the company’s goals and objectives.

Responsibilities include collaborating with the executive team to identify technical initiatives, developing and leading strategy for these initiatives, researching new technology systems, and reporting on KPIs and budgets.

Tips to get started on a software engineer career path

Practise your coding skills regularly

This should be a given if you’re serious about a career in software engineering. Coding skills are foundational to pretty much any discipline. Practicing on your own hones problem-solving and analytical skills, as well as building proficiency in essential coding languages.

Pursue software engineering courses or a bachelor’s degree in computer science

Before looking for entry-level positions, most software developers undertake a relevant university course, graduate training scheme, college course, or apprenticeship. This will usually be in computer science, information technology, or software development—although maths also has transferable skills.

Network

As in any career, it is important to network and make connections in the industry. If you’re not networking, then you’re likely missing out on roles and opportunities that you won’t find online. Additionally, networking provides a sense of community and keeps you up to date in the scene. 

If you look online for communities and networking events, you’ll find plenty of options. Especially for women in tech, finding like-minded individuals can be everything.

Final thoughts

Whatever your skills and interests, there’s a software development role out there for you. Starting in an entry-level position as a junior software developer, you’ll find many opportunities throughout your career to specialisespecialize and grow. The key is to stay curious, keep learning, and make all the connections you can as you move along your chosen career path.

Jessica True – Senior Director, Marketing Strategy & Operations

Jessica True is the Senior Director for Marketing Strategy and Operations at Dialpad, a modern business communications platform that takes every kind of conversation to the next level—turning conversations into opportunities. Jessica is an expert in collaborating with multifunctional teams to execute and optimize marketing efforts, for both company and client campaigns. She has written for other domains such as Agility PR Solutions and Developer Nation. Here is her LinkedIn.

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10 Benefits of Test-Driven Development to Your DevOps Team

10 Benefits of Test-Driven Development_
10 Benefits of Test-Driven Development_

From JavaScript to HTML/CSS to SQL and beyond, thoroughly testing code before integrating it into any system is a key element to consider in software development. First and foremost, it safeguards the quality and integrity of the code. Compared to development teams that use other methods, TDD has been proven to considerably reduce bugs and deficiencies.

It’s also interesting to note that the DevOps market size is expected to reach $25.5 billion by 2028.

In this article, we’ll explain what test-driven development is, along with the various benefits, and how to effectively integrate test-driven development into your DevOps Team.

Test-Driven Development

What is Test-Driven Development in DevOps? 

First things first, it’s important to understand that the test driven development technique is neither about testing, design, or simply carrying out lots of tests. Test Driven Development (TDD) is a proactive software development method where developers write tests for the code before it’s even been written.

In addition to Test-Driven Development, in the age of digital transformation, digital transformation conferences have become a reliable pool of knowledge for developers to make strategic decisions and foolproof investment choices, too.

Whether you’re a small startup or an established enterprise, implementing test-driven development can significantly enhance your software development process and ensure the quality of your products, ultimately strengthening your business name in the industry.

Moreover, by promoting transparency and accountability in the development cycle, TDD aids in identifying and mitigating potential risks, thus providing clarity regarding the ultimate beneficial owner of code functionality.

Why use Test Driven Development in DevOps?

Test-Driven Development offers a variety of benefits for developers, including:

1. Early Bug Detection & Reduces Bugging Time

Writing tests before making changes or implementing new features helps catch bugs and problems early on. Even better, the likelihood of shortcomings or flaws in the final product is considerably reduced too.

Pinpointing a specific area of code that requires attention when it fails, this reduces the time spent on identifying and rectifying issues that can be spent where it’s needed most.

2. Improved Code Quality

Writing tests not only ensure the code meets specific requirements, it often produces cleaner, more modular and more manageable code. Inevitably, this leads to better code quality.

Test-Driven Development

Emphasizing early testing, maintainability, and confidence in the correctness of the codebase, improving code quality with TDD also offers:

  • Insightful documentation
  • Better software design 
  • Increased developer confidence 
  • Automated regression prevention 
  • Notable time savings in the long run 
  • Seamless CI/CD integration 
  • Improved customer satisfaction.

3. Faster Feedback Cycles

TDD provides software developers with immediate feedback on the precision of their code. Quicker feedback loops save developers valuable time by addressing coding headaches straightaway.

Other key advantages faster feedback cycles offer developers include:

  • Accelerates the overall development speed 
  • Minimizes distractions 
  • Enhances productivity 
  • Developers gain confidence in code changes 
  • Aligns with agile development principles 
  • Promotes incremental development 
  • Swift integration with CI 
  • Fosters a culture of collaboration
  • Shortens the overall feedback loop in the development process.

4. Facilitates Refactoring

Refactoring refers to the process of improving internal structures or code designs without changing its external behavior.

Enabling developers to regularly improve the quality and maintainability of the codebase, refactoring allows developers to reshape and develop code whilst simultaneously eliminating the worry of breaking existing functionality or introducing accidental consequences.

The key steps for refactoring with TDD are:

  • Write a failing test 
  • Run the test 
  • Perform refactoring, e.g. renaming variables, extracting methods, simplifying complex logic, etc. 
  • Run the test again 
  • Write additional tests 
  • Run all tests 
  • Evaluate 
  • Implement changes 
  • CI Integration 
  • Refactoring Documentation, e.g. comments in the code, README files, etc.

To guarantee you codebase’s code health improves with time, it’s worth considering carrying out a code review.

5. Supports Continuous Integration (CI)

In DevOps software development, continuous integration (CI) is where developers routinely add code changes to a central repository. Going hand in hand with TDD, CI enables automated tests, provides quick feedback, maintains code stability, and makes sure any integration issues are identified early on.

The CI process typically includes these steps:

  • Version Control System (VCS) 
  • Code Changes 
  • Automated Build 
  • Automated Testing 
  • Static Code Analysis 
  • Artifact Generation 
  • Deployment to Staging 
  • Environment Automated 
  • Acceptance Testing 
  • Manual testing 
  • Code Review 
  • Feedback and Notifications 
  • Merge to Main/Master Branch.

6. Enables Continuous Delivery (CD)

Quite simply, continuous delivery (CD) automates the building, testing, and deploying of software. Making sure it’s always in a deployable state, combined with CI/CD techniques, TDD supports the frequent release of software updates.

Closely related to CI, the key steps in the CD process are:

  • Version Control 
  • Continuous Integration (CI) 
  • Automated Testing 
  • Artifact Generation 
  • Configuration Management 
  • Deployment to Testing/Staging Environment 
  • Automated Acceptance Testing 
  • Manual Testing 
  • Approval Gates 
  • Deployment to Production 
  • Monitoring and Logging 
  • Rollback Plan 
  • Post-Deployment Testing 
  • Documentation and Release Notes

7. Better Collaboration Reduces Debugging Times

TDD provides a clear understanding of the expected behavior of the code. It fosters a culture of collaboration among team members, facilitating virtual collaboration sessions where developers can discuss test results, code implementations, and potential improvements, regardless of their physical locations.

It also helps reduce debugging times by promoting collaboration in the form of clear specifications, collective code ownership, and regular code reviews. 

Reducing debugging times is beneficial for DevOps teams for various reasons:

  • Increased efficiency 
  • Faster time to market 
  • Cost savings 
  • Enhanced morale and motivation 
  • Higher-quality software 
  • Iterative development.

Resulting in better-quality software, faster turnaround for fixing issues, and happier development teams, reducing debugging times is essential for maintaining a seamless development process from start to finish.

8. Increased Confidence in Changes

Acting as the ultimate safeguard, if developers can ensure the tests pass they can be confident in the knowledge that the changes haven’t introduced any setbacks. Test-Driven Development (TDD) also aligns well with modern infrastructure practices like utilizing dedicated hosts, where the isolation and predictability they offer can further bolster confidence in code changes.

Just like software development, Enterprise Architecture (EA) is constantly evolving in this fast-paced market. So, if you like the idea of quicker change and innovation, achieving greater value within the market, and accomplishing your objectives, it’s worth looking into the latest EA trends for further insight.

Test-Driven Development

9. Positively Impacts Data Handling

By writing tests that validate data inputs and outputs, TDD ensures that data is processed accurately, providing a reliable foundation for developers to make an informed inference about the behavior and performance of their code under various conditions. This leads to improved data quality and reduces the likelihood of inconsistencies and errors.

TDD ensures accurate data handling by:

  1. Requirement Clarification 

Clarifying the types of data that need to be handled, how they should be processed, and determining the expected outcomes.

2. Test Writing 

Developers write test cases covering various scenarios related to data handling, e.g. input data, expected output, and any specific conditions or constraints to consider.

3. Test Execution (Red Phase) 

Examine failing tests to start writing the code to handle the data.

4. Code Implementation (Green Phase) 

Write the minimum amount of code needed to make failing tests pass.

5. Refactoring (Blue Phase) 

Once the tests pass and the code works, it’s time to refactor the code to improve structure, readability and efficiency.

6. Regression Testing

To maintain data accuracy, developers run an existing test suite to ensure changes haven’t introduced any regressions.
Increasingly driven by automation, call center data, campaigns, and dialling plans are prime examples that can all benefit from implementing modern test-driven development strategies.

10. Cost savings

By catching problems early, TDD can reduce the time and resources spent on fixing bugs and addressing issues in later stages of development or production.

Boosting both financial performance and competitiveness in your industry, saving costs allows development teams to deliver projects much faster, with fewer resources.

If you’re looking to take back control of your software development investments, it’s worth delving deeper into application portfolio management best practices to learn more.

Are there any alternatives to Test-Driven Development (TDD)?

Acceptance test-driven development (ATDD)

Acceptance Test-Driven Development (ATDD) is an agile software development process that incorporates acceptance tests into the development stage.

Behavior driven development (BDD)

Behavior-Driven Development (BDD) encourages collaboration amongst a diverse mix of stakeholders to enhance communication. It also ensures software meets the desired behavior and business requirements.

How do you implement Test-Driven Development?

A typical TDD workflow typically includes the following steps:

1. Write a Test

Write a test to define the expected behavior of the code.

2. Run the Test

Carry out the test and make sure it fails. The code hasn’t been implemented yet, so you want the test to fail and show the test is working properly by accurately reflecting the missing functionality.

3. Write the Code

Create the minimum amount of code needed to pass the test. Fulfill the requirements and nothing more.

4. Run the Refractor test (if needed)

Reducing complexities and strengthening readability, refactoring improves the code by making small tweaks without altering the code’s external behavior.

5. Repeat the Process

Repeat the cycle for each new piece of functionality or changes that need to be made.

Helping to better understand your domain as you develop it, and building robust and scalable apps aligned with your business domain is incredibly important too. For example, you could register domain .ai if you work in the world of machine learning or if you have a store based in Anguilla to boost brand awareness.

Final Thoughts

Instilling true value and lowering costs across the board, it’s clear to see (when used right), the TDD method presents an array of benefits to savvy software development teams.

Allowing developers to build a safe environment to unearth all the bugs before harming the whole system, if you’re looking for a methodology renowned for consistent quality and flexibility, test-driven development is the way forward.

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State of Developer Wellness report 2024

Developer Wellness
Developer Wellness

In 2023, we ran our first ever Developer Wellness survey with the aim of better understanding developers, levels of their well-being and happiness. Last year’s report sparked crucial conversations about well-being in the developer community, shedding light on the challenges developers were facing in their careers.

This year, we return with an even larger survey (nearly 1,000 developers from 86 countries!) to dive deeper. The survey was live for fifteen days during March 2024. More than half of the developers who participated were aged between 18 and 44 years old but we also had 10 developers younger than 18 and 7% were above 55 years old. 

83% of developers reported feeling burnout at some point in their careers

Burnout, characterised by exhaustion, energy depletion, increased distance from your job and reduced efficiency, is a significant concern in the developer world. The demanding nature of the work, coupled with factors like tight deadlines, constant learning curves, and potential isolation, can contribute to this state.

However, despite the concerning figure, many developers have started focusing on their well-being now more than ever. More than half of the developers have access to wellness tech through their employers in 2024 – fitness trackers, mindfulness apps, etc. – and more developers have started prioritising their physical and mental health amidst the stiff competition and uncertainty surrounding the tech industry. 

We found that 34% of developers exercise once or twice per week while 40% exercise at least 3 times per week! More than half of developers also manage to kick in 6-7 hours of sleep every night, although there is definitely some room for improvement.

In 2024, 84% of developers had to work overtime at least occasionally

Overtime is a common experience for many developers, although the frequency varies. While more than half (53%) find it acceptable, 39% express dissatisfaction without deeming it a deal-breaker, and 8% consider it overwhelming. 

This distribution suggests a spectrum of attitudes towards work-life balance among developers. It underscores the importance for companies to foster environments that prioritise employee well-being while acknowledging the demands of the tech industry.

But what are the tips/strategies that could help you improve your well-being and start waking up full of energy? How can you set clear boundaries at work?

We got you! You’ll find your well-being toolkit in our Wellness report. On top of that, you’ll also discover:

  • Workplace Perceptions: the workplace setup, employer support, sense of purpose
  • From Burnout to Balance: Are you on the struggle bus? Learn how developers are conquering burnout and achieving work-life harmony. ‍
  • Beyond the Code: work-life balance, recharging & well-being
  • Developer Wellness Champions: Unveiling the secrets to developer well-being, straight from our Developer Nation community members!

and more!

Ready to join a thriving developer community that prioritises well-being?

Download the report today, share it with your network, and let’s build a culture of wellness that promotes the mental, physical and emotional well-being of the developer industry! 

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Driving Digital Transformation: The Crucial Impact of Data Analytics

Driving Digital Transformation: The Crucial Impact of Data Analytics
Driving Digital Transformation: The Crucial Impact of Data Analytics

Picture yourself sailing a boat out on the ocean. Seeing the shore is impossible as thick fog sweeps in over the waves. Fortunately, you can see a lighthouse’s beam warning you to stay clear of the jagged cliffs up ahead. The ship’s captain waves you off when you point out this light and says he’d rather trust his instincts. 

Forrester found that 50% of the decisions made by companies are intuitive and subjective. In the same way that a tall lighthouse warns sailors of danger with its powerful light beam, big data analytics could provide the direction and concentration needed to drive digital transformation.  

With the unexpected disruption in the market, companies needed to digitize their interactions with staff and clients. Organizations of all shapes, sizes, and sectors have had to reinvent their business strategies and procedures, regardless of whether their objective is to compete, survive, or disrupt.

What Does Digital Transformation Mean?

The term “digital transformation” is frequently used, although it doesn’t have a universal definition, and different companies may interpret it in various ways. For a retail store, this could entail modifying its business plan to include online sales to its physical location. 

However, a business already has a digital commerce set up, it could entail implementing new digital technologies or altering existing procedures to analyze consumer behavior and attributes to hyper-personalize its product offerings and enhance the customer experience.

Whatever definition you choose, a successful digital transformation requires the proper infrastructure, technology, and data strategy to modernize your business and make it a more competitive and agile player in the market. Technology, digital workflows, and organizational transformation are all necessary. Because data analytics is an essential catalyst for any digital transformation endeavor, they must be prioritized.

Why Does Data Analytics Drive Digital Transformation?

Businesses must adapt as consumers’ expectations and technologies change. A company’s ability to make fast, well-informed, data-driven decisions will decide its ability to compete and prosper. Data can also be sold as a product, allowing your business to compete in the digital market. It can also be utilized to develop new products or enhance current ones.

Analytics provide the insights that result in well-informed decision-making, whereas data supplies the facts. Using data analytics as the backbone of your digital transformation, both at the beginning and during, you may get past important contemporary business issues that can otherwise stand in the way of your successful data projects.

1. Problems with Data Quality

Good data can support a company in achieving its goals. However, failing to take action to guarantee data quality might result in expensive errors and lost opportunities. 

When you value data as an asset for the entire organization, you can take ownership, management, and security of the data into consideration. These factors will improve decision-making throughout the organization by fostering transparency and trust in the data.

2. Data Silos

Segmenting and siloing data across many platforms, technologies, and business divisions can make it more difficult for enterprises to integrate the data, extract insights from it, and maximize its value. 

You can consider technologies and techniques that enable merging data from many sources and systems to view the big picture when you use data analytics as the main driver of your digital transformation.

3. Complications with Legacy Applications and Systems

Legacy systems can be a security risk to the company since they are frequently expensive to update and maintain, and they can sometimes be difficult to integrate with other, more contemporary components of your infrastructure. 

They make it harder for you to compete in the digital market. By approaching this from a data analytics perspective, you may choose technologies appropriate for the job and promote user adoption, agility, improved security, and peak performance.

4. Fulfilling Expectations of Customers

Today’s customers demand flawless end-to-end experiences, and businesses depend on data to help deliver them. Data enables you to gain insight into your target audience’s demographics, requirements, and preferences as well as what, when, and how often they buy and interact.

To satisfy evolving customer expectations, a digital transformation that prioritizes data analytics can facilitate the implementation of technology and procedures that will assist in gathering and analyzing that data.

Benefits of Using Data Analytics in Digital Transformation

There are obvious advantages to following a lighthouse when sailing wide waters. The same holds for data analytics. These are but a handful of the explanations for why you ought to incorporate them into your everyday activities, particularly as you head toward digital transformation.

1. Enhances the Efficiency of Digital Transformation

Utilizing data to examine user or client behavior allows you to make adjustments that will boost productivity and hasten the achievement of your objectives. For instance, you may utilize statistics to pinpoint the issue when customers are dumping the purchase process and modify it accordingly to boost the chance that they will finish it.

2. Assists in Formulating an Effective Digital Transformation Plan

A clearly defined plan is necessary before you start your digital transformation journey. You may be sure you don’t behave in a vacuum by basing decisions on the facts. Along the road, you can also keep note of the successes and setbacks of your strategy to figure out where, if necessary, to change course.

3. Allows Complex Process Automation

Automation lowers the possibility of error while also saving time and money. You can determine which procedures can be automated and gain insights into the most efficient ways to do it with data analytics. It will also be essential to the automation process, enabling the highest level of efficacy for any AI and machine learning algorithms.

4. Aids in Performance Monitoring

A thorough understanding of the success of your digital transformation can be obtained through data analytics. You can monitor important indicators to ensure your plan works, like page views, sales, and consumer involvement. This enables you to continue the forward motion of your digital transformation by making timely modifications.

5. Boosts Agility

Making judgments fast and correctly is made possible by data analytics, and this is a crucial component of agility in the digital transformation process. You are better equipped to react to shifts in the market or client expectations when you have access to the relevant data when required.

Challenges of Employing Data Analytics in Digital Transformation

Data analytics is a great tool, but it comes with some obstacles. To ensure data is reliable and helpful, you must first learn how to collect it and then appropriately evaluate it. Here are several challenges to be aware of.

1. Data Quality

Data analytics in the digital transformation strategy won’t function successfully without best-in-class data. Accordingly, the data must be precise, current, and reliable. The information also has to be unbiased and free of biases, having been gathered from trustworthy sources. This may present a hurdle, but accurate data analysis requires it.

2. Scalability

Data analytics is not a static process; it must be scalable to meet a company’s growing needs. As more data is generated, analytics must meet the demand. This can be pricey, but it is essential to ensure data analytics is effective and efficient.

3. Security

Addressing security concerns is crucial in data analytics. Because data is acquired from numerous sources, it is critical to guarantee that information is secure and that no illegal access occurs. As firms become reliant on sensitive data, implementing a data governance and management strategy becomes increasingly important.

Best Practices of Data Analytics in Digital Transformation

Knowing where a lighthouse is does not guarantee that your boat will arrive safely on shore. You must have a plan in place for how to use the information. Here are some best practices to follow when you embark on your data analytics journey.

1. Data Visualization

Data visualization is an effective tool for data analysis. It makes data easier to understand by presenting it visually. It can assist in discovering patterns, trends, and connections, allowing for more educated judgments.

Stormboard is a tool that brings all of these principles together. This data-driven collaboration platform delivers data to everyone on your team in an understandable format and automates the reporting process, ensuring that all stakeholders are updated on your processes.

2. Data democratization

Data democratization is making data available to all stakeholders that require it. This includes internal teams, customers, partners, and the general public. Data democratization allows companies to guarantee that all parties have access to the information they need to make the best decisions possible.

For data to be useful to your team, they must understand where to get it and how to use it. This procedure will necessitate training and updating your team’s analytical capabilities.

3. Automation

One essential element of data analytics is automation. Automation makes it possible to gather, process, and disseminate data quickly and effectively. Organizations may ensure accuracy and consistency while saving money, time, and resources by automating procedures. 

The importance of data analytics in digital transformation has been highlighted by navigating through its domain. Businesses can make better judgments by using predictive modeling and historical data analysis. Data engineers’ skills and automation are making real-world applications easier to manage. Large volumes of occasionally unstructured data are being used.

Conclusion

The importance of data analytics in digital transformation has been highlighted by navigating through its domain. Businesses can make better judgments by using predictive modeling and historical data analysis. Managing large amounts of occasionally unstructured data to create real-world applications is becoming easier thanks to automation and the knowledge of data engineering services. 

There has never been a stronger physical link between skillful data analytics and skillful business planning. In a time when strategic foresight is based primarily on data, effectively managing a business requires having a firm knowledge of data analytics. The capacity to interpret data not only reveals the state of the market but also encourages businesses to anticipate and react to emerging trends, resulting in the development of resilient, flexible, and forward-thinking strategies.

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Boosting Developer Productivity: Tools and Techniques for Efficient Coding

Tools and Techniques for Efficient Coding
Tools and Techniques for Efficient Coding

From ensuring that our smartphones operate efficiently to creating the software that runs large enterprise systems, developers are the brains behind much of the technological advancements we’re seeing today. However, software development is neither quick nor easy. And neither does it come cheap.

In fact, it takes around 4.5 months for the average software development project to be completed at an average cost of $36,000. With demand for such projects at an all-time high, developers need to get into a flow and experience deep focus to be productive.

That’s why using the right tools and techniques to enhance coding efficiency are so crucial. And that’s exactly what we explore in more detail below. Let’s dive in.

Understanding Developer Productivity

Developer productivity can be understood by exploring some of the objectives and key results (OKRs) against which their work is measured. Some of these are time-to-completion, bug rate, and code coverage.

Despite working toward clear OKRs, achieving these goals is sometimes hindered by common challenges that developers face that hinder their productivity. Examples of these include:

  • Interruptions and meetings
  • Micro-management and tight deadlines
  • Vagueness and unclear prioritization
  • A distracting workplace environment
  • Uncontrolled changes in the project’s scope
  • Unclear product definition process
  • Tool multiplicity and hardware
  • Lack of documentation

Techniques and Strategies for Boosting Developer Productivity

Software team leaders and project managers who are aiming to boost productivity of their developer teams should consider the following strategies:

  • Minimizing distractions and multitasking: When developers write code, they are in a space of deep focus. The smallest distractions could lead to drops in productivity and have other negative effects. The same is true when you require your developers to multitask. Whether it’s attending to incessant phone calls or unplanned stand-ups, it’s necessary to create a positive space where they can thrive. Give them sufficient time to prepare for planned meetings in advance, ensure they are working in a quiet environment, and avoid micro-managing them to avoid frustration and poor productivity. You can also use task apps to organize tasks by priority and set time limits so your developers don’t have to waste time and attention preparing their to-do list. 
  • Optimizing the Integrated Development Environment (IDE): There are software apps that help developers operate more productively. Essentially, this is known as an IDE and it combines functionalities that include software editing automation, building, testing, and packaging. IDEs can improve coding efficiency through additional capabilities syntax highlighting, intelligent code completion, refactoring support, debugging, etc.
  • Clear project specifications: The importance of well-defined project specs in reducing misunderstandings cannot be stressed enough. Project team leads should introduce well-defined project deadlines with achievable milestones along the way. There should also be verification by the client or interested party of the expectations of deliverables that the project should produce upon completion. Other key criteria include having a clear budget, setting out quality assurance requirements, and software requirement specifications (SRS), including functional requirements, non-functional requirements, and technical requirements.
  • Eliminating unneeded tests: While testing may be a natural part of the software development process in ensuring conformity with business requirements and technical specifications, it shouldn’t go overboard. Instead, there should be processes in place that review and aim to optimize the testing activities. Ultimately, this can reduce the execution time for the final product. 
  • Utilizing No-Code Platforms: In recent years, the rise of no-code platforms has offered developers a new approach to streamline development processes. These platforms allow for the creation of software applications without the need for traditional programming, enabling developers to focus on higher-level tasks while still achieving efficient results. Integrating such platforms into development workflows can significantly boost productivity by accelerating the development cycle and reducing the need for manual coding tasks.

Developer Productivity Tools

While there may be many developer time tracking and productivity tools available, we’ve curated the top two to help you with different development tasks. Here are the tools that topped our list:

Sublime Text

sublime text

Developed by Jon Skinner, Sublime Text is a versatile text editor for code, markup, and documentation. It is incredibly fast in launching and can handle large files with ease. Available for Windows, macOS, and Linux, its cross-platform compatibility means switching between different operating systems is a breeze and no functionalities are lost.

For those developers who would like to customize their coding environment, its functionality can be extended using community-contributed packages. Meanwhile, there are plugins that can be used, too. With a minimalist and clean interface, it offers a clutter-free environment for distraction-free writing.

What is more, it offers the ability to make multiple selections and edits at the same time. It offers a command palette to help you access numerous functions quickly. And for more complex projects, you can split your code into numerous columns or rows for easy comparison and editing. Finally, it can be configured to automatically save your files regularly.

GitHub

githyb

While there are distinct advantages and disadvantages of cloud computing, using GitHub is all about the benefits. This is a website and cloud-based service that helps developers boost their productivity. Think of it as a massive file where multiple developer collaborators can work on a project, store code, and implement version control to manage changes to their code.

It’s all about improving developer productivity and taking it to the next level seamlessly. What is more, it has a user-friendly interface, making it easy for novice and experienced developers to share, merge, change, and write code in one place.

Conclusion

With the techniques and tools mentioned above, project managers can ensure greater developer productivity without compromising the mental health of their team members.

Many developers experience burnout and this needs to be prevented with proper collaboration and communication, supported by the right tools.

Enhancing developer productivity in coding can be a more streamlined process as managers take their wellbeing into account.

Nikola Baldikov is a skilled SEO expert who is dedicated to helping businesses thrive. He is the esteemed founder of InBound Blogging, where his expertise lies in search engine optimization and crafting effective content strategies. Throughout his career he has had the pleasure of collaborating with a wide range of companies regardless of their scale and has consistently aided them in accomplishing their objectives online. During his leisure time. He finds joy in engaging in football matches and dance routines.

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Community

Enhancing Online Security: Best Practices for Developers

Enhancing Online Security: Best Practices for Developers
Enhancing Online Security: Best Practices for Developers

Developing a new software platform, mobile application, or online tool can be a great opportunity to offer innovative tools to the public. It can also present some serious risks. There will be those who seek to steal your intellectual property during the dev process. Alternatively, your completed product may be targeted by those who want to exploit valuable user data.

This makes it vital that your development team enhances its online security measures. 

Be Proactive

As a developer — or a leader of a dev team — it’s important not to treat security as a set of superficial defensive measures. This reactive attitude can put you and your applications on the back foot, struggling against the onslaught of threats. You and your team need to be proactive in making security as central and important to development as your coding.

One good approach to this is to make the product secure by design. As the name suggests, this process is about incorporating strong online security into the design phase of the development lifecycle. You’ll basically avoid waiting to consider security until the testing phase of the project or even fixing bugs in the beta phase, as is common. Instead, alongside brainstorming the key features of your product, your team should be looking at what the specific security challenges of the product are likely to be and how to minimize them. This allows you to build a strong security foundation from the outset.

Another way to be proactive in implementing security measures is to ensure your team follows Secure Software Development Lifecycle (SSDLC) protocols. This is effectively a set of actions that are baked into every task developers on your team perform so that they can identify and handle potential issues before they become problematic. It includes creating a culture of security in which threats are discussed and considered regularly. It should involve frequent cybersecurity training so that your dev team is fully aware of the latest threats and protection techniques. Importantly, the development environment itself should be secure, both digitally and physically.

Utilize Advanced Encryption Techniques

Encryption is one of the most powerful tools for ensuring online security. This is particularly effective for minimizing unauthorized access to data that is likely to be shared online both during the development lifecycle and by consumers when using the final product.

Identify and use strong encryption algorithms

Algorithms are the basis upon which encryption operates. Therefore, it’s important to utilize the most appropriate algorithms both for the product itself and protecting your networks. For instance, Advanced Encryption Standard (AES) is a common tool for development teams. This symmetric algorithm performs multiple encryption rounds before breaking the data down into smaller blocks. Some software and apps that require end-user authentication to access sensitive data — like financial information — may be better served by asymmetric encryption, such as the Rivest-Shamir-Adleman (RSA) protocol.

Adopting solid key management

Any encryption algorithm you adopt requires keys to be generated and shared to decrypt the information. It’s vital that you implement management measures to mitigate unauthorized access to and use of these keys. It’s important to formalize which members of the team can obtain and use these keys. It’s also vital to regularly change keys, much as you might update a password to keep it strong.

Conduct Vulnerability Assessments and Improvements

The cybersecurity landscape is in flux. Even within the timeline of your development process, new threats can emerge and come into favor. One of the best practices developers need to adopt is conducting regular vulnerability assessments and making relevant improvements.

Perhaps the most convenient approach during development is using automated scanning software. You can invest in tools that scan both the specific code of your project alongside your overall IT infrastructure. There’s even an increasing number of artificial intelligence (AI) driven scanners that use machine learning algorithms to learn about and adapt to the security landscape in relation to your development. In addition, utilizing a DevOps monitoring tool can allow you to see real-time performance issues that could suggest weaknesses in security, such as slow response times.

It’s also wise to remember that your development team’s workflow can be a source of vulnerability. For instance, too many unnecessary repetitive security processes might cause dev staff to become complacent and overlook key protective actions. A commitment to regular process improvement can help you not only minimize weak points but also boost efficiency. Not to mention it helps you to notice changes in the security landscape and adapt to them. You can do this by taking time to map out both formal and informal processes visually in flow diagrams at milestones during the development lifecycle. This helps you to analyze where inefficiencies occur and what processes you can consolidate and strengthen.

Conclusion

With some solid security best practices, you can ensure your development project is protected from threats throughout the project’s life cycle. This should include taking secure-by-design protocols and adopting string encryption, among other measures. Wherever possible make certain that you have a cybersecurity expert embedded into your dev team or available to consult regularly. This can help you both implement effective processes and stay abreast of any potential threats you need to prepare for.

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Community

8 Indexing Strategies to Optimize Database Performance

Databases provide the backbone for almost every application and system we rely on, acting like a digital filing system for storing and retrieving essential information. Whether it’s organizing customer data in a CRM or handling transactions in a banking system, an efficient database is crucial for a smooth user experience.

However, when we get into large volumes of data and more complex queries, database management can become daunting. That’s where good indexing strategies can make all the difference. 

Think of it like tidying up your digital filing cabinet so you can quickly find what you need without rummaging through multiple drawers and folders to locate the correct file.

By organizing and structuring your data in a way that facilitates quick retrieval, indexing can make a big difference in how well your database performs. Here, we’ll explore some strategies to help you do just that.

Database indexing best practices

Before you settle on a strategy, it’s worth understanding the different ways you can approach indexing to improve query selection and overall database performance.

Identify key queries and columns

Before getting started with indexing, you need to identify the type of queries your application is running regularly and which columns are involved in those queries. This helps you to focus your efforts on areas that will give the best results. There’s no point in spending time and energy indexing columns that rarely get used.

For example, let’s say you’re developing an app for an online bookstore, and one of the most common queries is searching for books by author name. In this case, creating an index on the “author” column can dramatically improve the performance of search queries.

Data orchestration tools can examine query patterns and usage statistics to pinpoint the most commonly executed queries in your database. What is orchestration, we hear you ask. 

When we talk about data, orchestration is the process of managing and coordinating various tasks like collecting, processing, and analyzing data from different sources. This helps to keep data operations well-organized and efficient.

By understanding which queries are commonly used, database administrators can prioritize indexing efforts on the columns involved in these queries.

Avoid over-indexing

While indexing can undoubtedly speed up query performance, as the saying goes, you can have too much of a good thing. 

Over-indexing isn’t just a waste of time, it can actually have the opposite desired effect and hinder database performance.    

Keep in mind that every index you add takes up storage space and needs managing within the database. Plus, having too many indexes in play can slow down your insert and update performance because your database will be working overtime to update multiple indexes with every change.

To avoid this, follow data indexing best practices such as those covered in Apache Hive documentation. Aim to strike a balance between query performance and keeping the database easy to manage. 

Focus on indexing columns that are frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses. Also, think about using composite indexes for queries that involve multiple columns.

Regularly monitor and tune indexes

Creating indexes isn’t one of those jobs you can do once and forget about. Because data and query patterns often evolve over time, you need to regularly check and adjust them. 

It’s similar to the practices of Machine Learning Ops (MLOps), where ongoing monitoring ensures the model is still effective. Similarly, consistently reviewing and fine-tuning indexes plays a pivotal role in managing their effectiveness. 

Failure to do so can lead to accumulating technical debt, where outdated or inefficient indexes accumulate over time, resulting in degraded performance and increased maintenance overhead.

Use SQL tools like MySQL’s EXPLAIN or Microsoft SQL Server’s Query Execution Plan. These will give you a solid view of how queries are being executed and which indexes are well utilized. You can then more easily see where to add missing indexes and remove ones you no longer need. It also helps you spot opportunities to update existing ones to better suit query patterns.

Let’s look at what that means in practice. Suppose you notice a particular query performing poorly despite having an index. Upon closer inspection, you discover that the index’s cardinality (i.e. uniqueness) is low, leading to poor selectivity. In this case, modifying the index or adding additional columns to improve selectivity could significantly boost that query’s performance.

Consider using covering indexes

A covering index includes all the columns necessary to fulfill a query. This means that the database doesn’t need to keep accessing the underlying table. 

To return to our filing cabinet analogy, you can think of it as having the right folders set out in front of you so you don’t have to search through the entire cabinet to find what you need. Using covering indexes can speed up search queries by reducing the number of overall disk I/O operations.

For example, consider a call center analytics software that logs details of each customer interaction. This might include data such as:

  • Caller ID
  • Call duration
  • Call timestamp
  • Outcome

If you’re frequently running reports on the total duration of calls, creating a covering index on the caller ID and call duration fields can optimize query performance. This allows the software to retrieve call duration information directly from the index without having to repeatedly access the main call log table.

Monitor and manage index fragmentation

Index fragmentation occurs when the logical sequence of index pages is not in sync with the physical arrangement. This can make data storage less efficient and slow down search queries. It’s like a library’s card catalog not matching the actual locations of the books on the shelves. 

If you don’t catch this and fix it, the problem will only get worse as more data is added or updated. It’s essential to keep a close eye on your indexes and tidy them up regularly. 

One solution is containerization, which provides a structured environment for managing databases. Most modern systems also offer tools for detecting and addressing index fragmentation like rebuilding or reorganizing indexes to help with this.

8 database indexing strategies to try

Not all indexing strategies are created equal. When it comes to finding the best indexing strategy for your database, you need to consider a few things, including:

  • What type of data you’re working with
  • Which queries you run often
  • What performance goals you want to achieve

With that in mind, here are a few examples of indexing strategies for different situations.

1. Single-column indexes

Single-column indexes work well for databases with tables containing a large number of rows and where queries frequently filter or sort data based on a single column. For instance, if you’re regularly looking up users by their usernames, create an index for the “username” column in the user table for faster retrieval.

2. Composite indexes

If your common queries involve columns in a WHERE clause or involve ORDER BY and GROUP BY operations on multiple columns, composite indexes might be more useful. For example, if you have a sales database where you’re frequently searching for sales by date and location together, you can create an index for both the “date” and “location” columns.

3. Unique indexes

These ensure data integrity by enforcing uniqueness on one or more columns. They are beneficial for columns that should not contain duplicate values, such as primary keys or email addresses in a user table.

Image Sourced from DoneDone.com

4. Clustered indexes

Some databases feature rows that are physically stored in order based on the index key. In these cases, clustered indexes can improve the performance of range queries or sequential scans. For example, if you organize time-series data by date, clustering the primary key will make it quicker to find information chronologically.

5. Covering indexes

These indexes contain all necessary information for answering a query so the database doesn’t have to revert to the original data table. They’re helpful for queries with SELECT, JOIN, and WHERE clauses. 

This can significantly improve query performance, especially in scenarios where you might need to generate data-driven insights from complex queries that involve multiple columns or tables. For example, if you often create reports using data from multiple columns, a covering index could include all those columns to speed up the process.

For organizations managing large-scale data processing tasks, such as those involving HPC batch jobs, implementing covering indexes can significantly improve query performance, especially when dealing with complex queries across multiple columns or tables.

Another crucial consideration for database optimization is ensuring smooth operations during critical periods, such as website launches. Utilizing a comprehensive website launch checklist can help ensure that your database infrastructure is adequately prepared to handle increased traffic and demands on query performance during such events.

6. Partial indexes

When a subset of data is frequently queried, partial indexes can be created to cover only that subset, reducing the index size and improving query performance. An example is creating a partial index for active users in a user table where only rows with “active = true” are indexed. 

In cloud environments dealing with massive datasets, partial indexes can help you manage resources more efficiently and maintain optimal performance. What is cloud native architecture? This refers to apps built specifically to work well in cloud environments. It involves using cloud services and concepts like microservices, containerization, and orchestration. It’s frequently used for apps that need to perform in an agile environment and be quickly scaled up or down.

7. Expression indexes

These indexes are created based on expressions or functions applied to one or more columns. They are useful for queries involving computed values or transformations. For example, indexing the result of a mathematical operation or string concatenation performed on columns.

8. Hash indexes

Particularly useful for equality comparisons, hash indexes can provide fast access to data with low cardinality columns or when accessing a large number of rows randomly. They are suitable for scenarios like indexing boolean or enumerated columns.

Database indexing – optimize database performance

In database management, optimizing queries is key to ensuring your database performs well across all platforms, from web to mobile. To do this, you need a solid indexing strategy. 

Choosing the right database index can directly impact business operations. When your database is well-organized, it means employees and users can find what they need quickly, leading to tangible benefits from improved response times to streamlined operations and reduced costs.

Understanding the different approaches and best practices means you’ll be better equipped to streamline your data and manage it efficiently.

Pohan Lin – Senior Web Marketing and Localizations Manager

Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan has written for other domains such as Spiceworks and Parcel Monitor. Here is his LinkedIn.

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Community Tips

How Do UX Design Principles Elevate Customer Experiences?

User Experience (UX) Design principles play a key role in increasing customer experience. UX principles focus on creating products that are user-friendly and meaningful to use.

According to the research paper published by Core, If you want to design a user-friendly interface, white-label service providers who are experts in designing can help you out. White-label web development companies have UI/UX experts who know how to place each UI element in your product. So,  white-label agencies can help you build a more responsive and highly interactive design to help the users move around the website. 

Here are the things white-label agencies take care of while designing the UI/UX of your product.

6 UX Design Principles for Increasing Customer Experience

#1 Simple and Clear Designs

Keeping your website design simple and clear is an integral part of your UX strategy. This would involve defining the navigation menus clearly. You must design intuitive design layouts and use effective language. 

Make sure to add inputs and messages along the design wherever needed. It will help enhance the experience of the user. Your white-label design service provider will prioritize simplicity while crafting the solution. They will use the existing successful websites as their guide to define a clean and organized layout. 

The services will devise a strategy to make navigation intuitive and guided. This would help people move around without being stuck at any point. Moreover, they can plan for a “one-task, one-screen” layout that avoids clutter. 

According to the research paper published by Core, simplicity in design is about going deep into your user’s minds. There are 3 ways to achieve simplicity in design:

• Maintain clarity: Understand and design for your users’ main goals

• Make use of automation: Design for a minimum amount of conscious and cognitive effort

• Limit options: Design for a strong “information scent”

#2 Crafting Responsive Designs

Your users access the same websites across devices and resolutions. For instance, a user may access the website on the Chrome browser on their Android tablet. Another user may access the website on their iPhone browser. 

It is also possible the same user opens the website on an Android phone and tablet simultaneously. Responsive designs will automatically adapt to the platform’s needs and screen sizes. The design will stay consistent, and users will not face any friction while moving around. This adaptability will enhance the user’s experience.

Your white-label service provider can help you implement the responsive design layout. They are crucial in imagining and testing responsive designs. They will thoroughly test the design and address layout issues in advance.

#3 Ensuring Design Consistency

Consistency is key to keeping your users happy. You must ensure your design is aligned with your brand’s identity. Each element must be cohesive and defined. 

You must add the brand’s color scheme, typography, and design styles while creating the website. This would make it recognizable and relatable to the users. You can improve the overall appearance and ensure a professional design outcome. 

A white-label website design and development service provider works with defined guidelines. They are aware of the design expectations and nuances. As a result, companies can offer clean and consistent designs. Companies would design the wireframe to prototype to eliminate inconsistencies and provide a smooth layout.

#4 Well-defined Information Architecture

Information flow is pivotal in enhancing the user’s experience. You must define a logical movement and the organization of the content. When the user moves from one particular information to another, they must sense an intuitive flow. 

This would increase the user’s engagement on the website and allow them to find the information faster.  You can connect with your white-label service provider to define the sitemap and wireframes for your website. This would establish an organized information flow. You can design the user journeys and map them in your website design. 

Companies can also help you conduct usability tests and validate the information flow for engagement.

#5 Iterative Design with Feedback Loops

Knowing what your users think of the design is crucial for designing an appropriate website. You must ensure you have a feedback loop that brings back the messages from the user. This would help build a user-centric website.

You must use an iterative design strategy to implement the loops and leverage them to avail the feedback. 

You must have defined mechanisms to help collect the user’s feedback. This would include surveys and analytics tools. White-label service providers can implement these feedback loops and incorporate iterative design for excellent user insights.

Companies can use user insights to build an optimized website aligned with the user’s preferences and needs.

#6 Accessibility Design Considerations

Diverse users, including people with disabilities, will access your website. You must prepare your website for all user types. Ensure the website is aligned with the ethical design considerations while designing for web accessibility. 

Implementation: The white-label service provider is well aware of accessibility guidelines. Their understanding of accessibility standards would help them implement the right headings and alt+text for images.

Moreover, they would ensure the design is accessible via screen readers and other inputs. This would address all the barriers and ensure inclusivity.

Conclusion

It is crucial to create UX-led white-label websites. This would enhance your design strategy and extend exceptional results. You can elevate your user’s experience by implementing consistent and clear designs. 

Moreover, it offers an organized information architecture and accessible design that boosts the website’s quality. When your website meets the functional, aesthetic, and quality standards, it is usable, user-friendly, and highly engaging.

This would improve the session length and the conversion rate for your business. Prioritizing UX design principles in your website design is no longer a strategy; it is the definition of a successful website.

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Self-Hosting

Systemd journal logs: A Game-Changer for DevOps and Developers

“Why bother with it? I let it run in the background and focus on more important DevOps work.” — a random DevOps Engineer at Reddit r/devops

In an era where technology is evolving at breakneck speeds, it’s easy to overlook the tools that are right under our noses. One such underutilized powerhouse is the systemd journal. For many, it’s a mere tool to check the status of systemd service units or to tail the most recent events (journalctl -f). Others who do mainly container work, ignore even its existence.

What is the purpose of systemd-journal?

However, the systemd journal includes very important information. Kernel errors, application crashes, out of memory process kills, storage related anomalies, crucial security intel like ssh or sudo attempts and security audit logs, connection / disconnection errors, network related problems, and a lot more. The system journal is brimming with data that can offer deep insights into the health and security of our systems and still many professional system and devops engineers tend to ignore it.

Of course we use logs management systems, like Loki, Elastic, Splunk, DataDog, etc. But do we really go through the burden to configure our logs pipeline (and accept the additional cost) to push systemd journal logs to them? We usually don’t.

On top of this, what if I told you that there’s an untapped reservoir of potential within the systemd journal? A potential that could revolutionize the way developers, sysadmins, and DevOps professionals approach logging, troubleshooting, and monitoring.

But how does systemd-journal work?​

systemd journal isn’t just a logging tool; it’s an intricate system that offers dynamic fields for every log entry. Yes, you read right. Each log line may have its own unique fields, annotating and tagging it with any number of additional name-value pairs (and the value part can be even binary data). This is unlike what most log management systems do. Most of them are optimized for logs that are uniform, like a table, with common fields among all the entries. systemd journal on the other hand, is optimized for managing an arbitrary number of fields on each log entry, without any uniformity. This feature gives this tool amazing power.

Check for example coredumps. systemd developers have annotated all applications crashes with a plethora of information, including environment variables, mount info, process status information, open files, signals, and everything that was available at the time the application crashed.

Now, imagine a world where application developers don’t just log errors, but annotate those logs with rich information: the request path, internal component states, source and destination details, and everything related to identify the exact case and state this log line appeared. How much time such error logging would save? It would be a game-changer, enabling faster troubleshooting, precise error tracking, and efficient service maintenance.

All this power is hidden behind a very cryptic journalctl command. So, at Netdata we decided to reveal this power and make it accessible to everyone.

Try it for yourself in one of our Netdata demo rooms here.

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Self-Hosting

5 DevOps best practices to reinforce with monitoring tools

This blog is contributed to Developer Nation by Netdata

As part of a modern software development team, you’re asked to do a lot. You’re supposed to build faster, release more frequently, crush bugs, and integrate testing suites along the way. You’re supposed to implement and practice a strong DevOps culture, read entire novels about SRE best practices, go agile, or add a bunch of Scrum ceremonies to everyone’s calendar. Every week, the industry recommends that you “shift-left” another part of the DevOps pipeline, to the point where you’re supposed to handle everything from unit testing to production deployment optimization from day one.

While you might have some experience in monitoring software, the reality is that as an aggregate, some others around you probably don’t. According to the Stack Overflow Developer Survey 2020, nearly 40% of developers have less than 5 years of professional experience. There’s not enough time for anyone to learn all these DevOps tools and best practices while also putting meaningful code into a GitHub repository on a regular basis.

Monitoring, and the metrics data it creates, can be a powerful way to encourage DevOps best practices through a common language, and implementing it doesn’t have to be complicated or time-consuming. By combining a DevOps mindset with a “full-stack” monitoring tool, you can start getting instant feedback about the performance and availability of what you’re trying to build—without waiting another 5 years for your team’s DevOps experience to catch up.

If your team has already settled on a monitoring tool, you can start applying these best practices today. If you’re still looking for the right piece of kit, you can start making informed tooling decisions based on what’s going to strengthen your team.

Focus on infrastructure monitoring first

When we talk about monitoring software for DevOps teams, we’re talking primarily about infrastructure monitoring. Infrastructure monitoring is the practice of collecting metrics data about the performance and availability of an application’s “full stack.” That’s everything including the hardware, any virtualized environment, the operating system, and any services (like databases, message queues, or web servers) that might make your application possible.

Depending on the full stack’s complexity, infrastructure monitoring can mean keeping an eye on a single virtual machine (VM) running on Google Cloud Platform (GCP), a Kubernetes (k8s) deployment with dozens of ephemeral nodes that scale horizontally during periods of high usage, or anything in between.

Here’s some key infrastructure metrics to keep an eye on using your monitoring tool (Netdata included):

If you can collect eBPF metrics, that’s even better, even if you aren’t experienced enough to make sense of them yet. eBPF metrics are still very much the cutting edge of infrastructure monitoring, providing extremely granular detail into exactly how the Linux kernel deals with your full stack, so there’s still a lot of flux in recommendations and best practices.

Monitor performance and availability in every environment

Modern DevOps teams should be monitoring the full stack no matter where it runs. This presents quite a large break from tradition, where the operations (Ops) team handled monitoring only once the application was running in production. The perception was that seeing users interact with a full stack was the only way to catch real bugs.

The latest best practices acknowledge that it’s possible—even inevitable—to catch bugs early by monitoring everywhere. That starts with local development servers and extends to any number of testing, staging, or production environments. That also means the monitoring tool should work whether the application is running off the latest M1 Macbook Air or in a multi-cloud deployment across dozens of virtual machines (VMs).

Before you go rushing into your next release process, take time to develop the tooling to monitor in more places. That might mean creating a custom Dockerfile for local development, or adding hooks into your CI/CD toolchain to deploy a fresh staging environment every time a developer reaches a milestone.

How’s this for an easy deployment experience in any environment?

Collect everything, worry about it when you need it

The only way to know that something is going wrong with your application’s infrastructure is to have the data to support it. One common practice is to vacuum up every metric, store it for 2-3 weeks, and have it available if you need to go back in time and root cause an issue or outage.

One way to ensure you’re collecting everything is to choose a tool with high granularity. Every infrastructure monitoring tool collects and visualizes metrics at a specific granularity, which is another way of talking about the time period between one point of collection and the next.

One data point every 60 seconds = low granularity One data point every 1 second = high granularity If you have a transitive-but-critical error that comes and goes within 5 seconds, a low-granularity solution might not even show a blip, which means you still don’t know anything went wrong in the first place.

With low granularity, metrics are averaged out over long periods of time, which has the unwanted effect of flattening what should be a worrying spikes into nothing more than a blip in the noise.

Netdata itself uses an internal time-series database for storing per-second metrics in an efficient way, which gives you tons of flexibility to find the sweet spot between disk space considerations and keeping historical metrics around long enough for proper analysis.

Some DevOps teams even use tools like (e)BPF, which collect and visualize metrics with an event granularity, which means they can show you every event, and not just an average/minimum/maximum of data between two points in time.

Break down silos with metrics

One of the DevOps mindset’s core purposes is to break down existing silos between what used to be separate development (Dev) and operations (Ops) teams. In the past, the Dev team finished writing code, flung across the fence to the Ops team, and wiped their hands clean from whatever came next. The Ops team then spent their days putting out fires and understanding how the application worked.

DevOps is designed to stop this unproductive cycle, but it only works if everyone has access to the same platform and uses the same language: metrics. Choose a tool that’s accessible to everyone who touches application code or controls the production environment. That doesn’t mean having one person who controls the infrastructure monitoring dashboards and lets the rest of the organization look at it in read-only mode.

Make sure your monitoring tool encourages the sharing of information. Let anyone on your team, no matter their role, peek at your configurations or dashboards. By looking over your shoulder, they might learn something valuable, like a metric they’d previously overlooked or a unique troubleshooting strategy. On the other hand, the tool should also let anyone experiment and explore in a “sandbox” that doesn’t affect the core health and availability dashboards.

For example, Netdata Cloud uses the concepts of War Rooms, which are shared containers for DevOps teams who need to do infrastructure monitoring. Every node, alert, and custom dashboard in that War Room is shared between everyone, but each team member can freely create, reconfigure, and learn. No more keys to the dashboarding kingdom, and no more worrying about messing up someone else’s perfectly-crafted troubleshooting experience.

Bubble it up into continuous monitoring

While continuous integration (CI) and continuous delivery (CD; CI/CD) have gotten all the attention, a lot of DevOps practitioners have forgotten about continuous monitoring (CM). This practice helps DevOps teams track, identify, and make decisions from all collected metrics, across all environments, in real time.

While some consider CM the last part of the DevOps pipeline—the practice of monitoring an application in production—other organizations bring CM to the entire CI/CD toolchain, monitoring internal processes and tooling to identify issues before being released into the wild.

With a sophisticated CM strategy in place, your team can better respond to ongoing incidents, with the added benefit of making leaps in the 4 key metrics for DevOps success: mean time to acknowledge (MTTA), mean time to recovery (MTTR), mean time between failures (MTBF), and mean time to failure (MTTF). You’ll improve company-wide visibility into the performance and availability of its stack, and you’ll end up driving real business results, like happier users and improved retention. Because Netdata deploys (easily) everywhere, has highly-granular metrics, and lets users of all experience levels explore and learn their infrastructure, it’s perfect for leveling up a DevOp team with CM.

Don’t have a DevOps monitoring tool yet?

The IT infrastructure monitoring tools that make all these best practices come to life come in a huge variety of shapes and sizes, from open-source toolchains you cobble together on your own to enterprise-friendly monoliths that do everything but cost a ton.

Because there are so many moving parts, a lot of developers and DevOps teams hesitate when choosing an IT monitoring tool, and then end up with something that doesn’t actually empower them, knock down silos between teams, or ramp up the speed of development.

One choice that enables all of the above best practices, and many more, is Netdata. Download the free and open-source Netdata Agent to start implementing DevOps best practices and improve team’s performance know-how with a free, open-source monitoring tool.

Once you’re seeing metrics with per-second granularity, familiarize yourself with Netdata’s documentation and guides to explore more opportunities to explore, troubleshoot, and resolve even the most complex of full-stack issues.