Tuesday, 28 July 2026

The Rise and Fall of the Marquee Tag

 




The Rise and Fall of the Marquee Tag 

If you've been online since the late 90s or early 2000s, you've probably seen text scrolling across a webpage like a mini news ticker. Maybe it said "Welcome to my website!" and slid lazily from right to left, over and over. That's the marquee element in action, and it was everywhere back then.

Let's talk about what it actually is, why it existed, and why most developers today steer clear of it.

So What Is the Marquee Element?

The <marquee> tag is an HTML element that makes text or images scroll automatically across the screen. You didn't need to write any JavaScript or CSS animation to use it. You just wrapped your content in the tag, like this:

<marquee>This text will scroll across the screen</marquee>


And that was it. The browser handled the rest, sliding your text from one side to the other without you lifting a finger.

You could also tweak how it behaved. Want it to bounce back and forth instead of looping? There's an attribute for that. Want it to scroll up and down instead of side to side? There's an attribute for that too. Some common ones included:

  • direction – controls which way the content moves (left, right, up, down)

  • behavior – decides if it scrolls continuously, bounces, or slides once and stops

  • scrollamount – controls the speed

  • loop – sets how many times it repeats

It was simple, quick, and required zero coding skill beyond copy-pasting a tag.

Why Did People Use It?

Back in the early days of the web, things were a lot less polished. There was no CSS animation, no JavaScript libraries built for smooth transitions, and definitely no fancy frameworks. If you wanted movement on your page, your options were limited.

The marquee tag filled that gap. It let regular people, not just professional developers, add a bit of motion and personality to their sites. A scrolling "Under Construction" banner or a ticker of site updates felt exciting at the time. It was one of the few ways to make a static page feel alive.

It was also incredibly easy to use. You didn't need to understand programming logic or timing functions. You just typed the tag and your text moved.

Why It Fell Out of Favor

Here's the thing: the marquee element aged badly, and for good reason.

It was never a proper web standard. It started as something Internet Explorer introduced, and other browsers copied it just to keep up. It was eventually added to HTML5, but only as an "obsolete" element, meaning browsers still support it, but you're not supposed to rely on it going forward.

It's bad for accessibility. Constantly moving text is distracting and can be genuinely difficult for people with certain cognitive or visual conditions to read. Screen readers also don't handle it gracefully. Good web design today puts a lot of weight on accessibility, and marquee just doesn't fit that mindset.

It gives you no real control. You can't style it with much precision, you can't easily pause it on hover without extra tricks, and it doesn't play nicely with responsive design. Modern websites need to look good on everything from a small phone screen to an ultra-wide monitor, and marquee just wasn't built with that flexibility in mind.

It looks outdated. Fair or not, scrolling text now reads as a signal that a site hasn't been updated in twenty years. It's become a bit of a running joke among developers, a symbol of early, clunky internet design.

What Do Developers Use Instead?

Today, if you want scrolling or moving text, you reach for CSS animations. Something like the animation and @keyframes properties gives you full control over speed, direction, easing, and behavior. You can pause it on hover, make it responsive, and style it however you like.

For example, a basic CSS scroll effect might look like this:

.scrolling-text {

  display: inline-block;

  white-space: nowrap;

  animation: scroll-left 10s linear infinite;

}


@keyframes scroll-left {

  from { transform: translateX(100%); }

  to { transform: translateX(-100%); }

}


It takes a bit more setup than the old <marquee> tag, but it gives you way more control, works consistently across browsers, and doesn't carry the same accessibility baggage, especially if you add ways to pause the animation.

There are also JavaScript libraries built specifically for smooth scrolling banners, news tickers, and carousels if you need something more advanced, like pausing on hover, syncing with user interaction, or adding fade effects.

Should You Ever Use the Marquee Tag?

Honestly, no, not in real, modern projects. Browsers still support it for backward compatibility, so old websites won't break, but it's not something you should build with today. If a client or a project asks for scrolling text, the better move is to explain the accessibility and design concerns and offer the CSS animation approach instead.

That said, there's nothing wrong with knowing about it. Understanding the marquee element gives you a bit of web history and helps you recognize why certain modern practices, like accessibility-first design and separating structure (HTML) from presentation (CSS), matter so much today.


The marquee element was a product of its time. It solved a real problem when the web didn't have good tools for animation, and it gave ordinary people an easy way to add movement to their pages. But as the web matured, better, more flexible, and more accessible tools came along and made it obsolete.

It's a good reminder of how web development evolves. What felt cutting-edge in 1998 can look clumsy by today's standards, and that's not a bad thing. It just means the tools got better, and so did our understanding of what makes a website actually work well for everyone.

Reena Meena

Web Development Specialist

AeroSoft Corp

AeroSoftCorp.org 

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Why Web Developers with Drone Pilot Skills Are in High Demand in 2026

 

Why Web Developers with Drone Pilot Skills Are in High Demand in 2026

If you're a web developer, you've probably noticed something strange happening in the job market this year. Companies aren't just asking for React, Node, or Python anymore. Some of them are asking a question you'd never expect from a tech recruiter:

"Do you also fly drones?"

It sounds random. But it's actually one of the smartest career moves a developer can make right now. Here's why.

The Two Industries That Suddenly Need Each Other

Drones aren't just flying cameras anymore. They're collecting massive amounts of data — aerial images, mapping data, live video feeds, sensor readings — for industries like agriculture, construction, real estate, logistics, and disaster management.

But raw drone data is useless sitting on a hard drive. Someone has to turn it into something people can actually use: dashboards, maps, apps, and websites that clients can log into and understand at a glance.

That's where web developers come in. And that's why the combination of "can fly a drone" plus "can build the software around it" has become such a rare and valuable skill set. One person, two industries. Companies love that because it saves them from hiring two separate people who then have to constantly explain things to each other.

This shift has been talked about as one of the more overlooked career shifts of the decade — the kind of opportunity nobody really told you about until it started showing up in job postings.

What Does This Actually Look Like in a Job?

Let's make this practical. Imagine a construction company using drones to survey a building site every week. They don't just want the drone footage — they want:

  • A web dashboard showing progress over time

  • Interactive maps built from drone imagery

  • Automated reports generated from flight data

  • A simple portal where the client can check updates without calling anyone

A developer who understands how drones capture data, what formats that data comes in, and what a pilot actually needs on the ground can build all of this far more effectively than someone guessing from the outside. You're not just writing code — you're solving the real problem, because you understand both ends of it.

This is exactly the kind of career path described in a detailed look at how professionals are moving from flying drones to building the technology around them. It's not a side hustle anymore — it's becoming a legitimate specialization.

Why 2026 Specifically?

A few things have lined up at the same time:

  1. Drone regulations have matured. Getting a Remote Pilot Certificate is now a structured, well-defined process instead of a legal grey area.

  2. Drone data volumes have exploded. More industries are using drones for daily operations, not just occasional inspections.

  3. Clients expect software, not just footage. Nobody wants a folder of images anymore — they want a website or app that makes sense of it.

  4. Fewer people have both skills. Most drone pilots aren't developers, and most developers have never touched a drone. That gap is the opportunity.

If you want to see how structured drone training has become in India, this overview of leading remote pilot training programs is a good starting point. It's no longer something you learn informally — there's a real curriculum behind it now.

Is This Just for Developers, or Could Pilots Learn to Code Too?

It works both ways, actually. Drone pilots who pick up basic web development skills are just as in-demand as developers who learn to fly. Either direction gets you to the same overlap zone that companies are hunting for.

There's a good breakdown of this two-way opportunity in this piece on the career path nobody is currently teaching — the general idea being that schools and bootcamps still treat these as two completely separate fields, when in practice, businesses want them combined.

The Bigger Picture: An Industry That's Just Getting Started

The drone industry isn't slowing down. It's expanding into new sectors every year — agriculture monitoring, delivery services, infrastructure inspection, film and media, security, and more. Every one of these sectors eventually needs a digital layer: websites, dashboards, client portals, and internal tools.

This article on the booming drone industry goes into more detail on which specific skills are becoming valuable as the industry scales up — and web development sits right near the top of that list.

And if you're wondering whether this is a short-term trend or something worth actually investing time in, this piece on the drone revolution and the career opportunities it's creating makes a solid case that this is just the early stage of a much longer shift.

Should You Actually Learn to Fly a Drone?

You don't need to become a professional pilot overnight. Start small:

  • Look into your local remote pilot certification process

  • Learn the basics of how drones capture and store data (image formats, GPS metadata, flight logs)

  • Try building a simple project — even a basic gallery or map — using sample drone imagery

  • Follow a few companies in the drone-tech space to see what kind of web tools they're actually building

You don't need to master aviation. You just need enough understanding to speak the same language as the people flying the drones — and enough coding skill to turn their data into something useful.


Web development on its own is still a solid, in-demand skill. But adding drone pilot knowledge on top of it puts you in a very small, very useful category of professionals that companies are actively struggling to find. In 2026, that combination isn't a gimmick — it's a genuine competitive edge.

If you're already comfortable behind a keyboard, learning to fly might just be the next smartest move in your career.


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What Does a Data Analyst Actually Do in the Corporate World?

 


What Does a Data Analyst Actually Do in the Corporate World?

If you ask ten people what a Data Analyst does, you will probably get ten different answers. Some think it is just Excel all day. Some mix it up with Data Science. Some think it is only "making charts." The truth is a bit more layered, and it helps to first understand where this role sits inside a company before getting into specifics.

Understanding the Company Structure First

Every company, big or small, is basically a system for turning resources like people, money, and ideas into results like products, revenue, and growth. To make that happen, companies are usually organized in layers:

• Leadership (CEO, VPs, and other executives): decides where the company is headed and what the big goals for the year look like. 

• Middle Management (directors, senior managers): takes that big picture direction and turns it into actual departmental targets and budgets. 

• Operational Management (team leads, managers): makes sure day to day work gets done and stays on track. 

• Execution Layer (analysts, engineers, sales reps, associates): this is where most people actually work, doing the hands on work that produces real output.

When someone talks about "company orientation," this is really what they mean. It is less about learning where the coffee machine is and more about understanding who owns what decisions, which department you belong to, and how your work eventually rolls up to something leadership actually cares about. For a Data Analyst, this matters a lot, since an insight is only useful if it reaches the right person in a format they can actually use.

The Departments You Will Run Into

Most organizations, whether a small startup or a massive corporation, tend to have a similar set of core departments:

• Sales: focused on bringing in revenue. Cares about lead conversion, where deals fall through, and how much it costs to acquire a customer. 

• Marketing: owns the brand and demand side. Focused on campaign performance, customer segments, and which channels are worth the spend. 

• Finance: handles the money. Budgeting, forecasting, cost control, and keeping the business profitable.

• HR: looks after hiring, retention, and culture, and increasingly uses data to track attrition and employee engagement.

• Operations: keeps the business running smoothly day to day. Cares about efficiency, supply chains, and where processes tend to break down. 

• Product: decides what actually gets built. Needs data on feature usage and how users behave. 

• Engineering / IT: builds and maintains the systems everything else runs on. 

• Customer Support / Success: handles the relationship after the sale. Tracks churn and how quickly issues get resolved. 

• Data / Analytics: in a lot of companies today, this has become its own function, serving every department above with dashboards, pipelines, and insights.

A Data Analyst can sit inside any single department (Marketing Analyst, Finance Analyst, Product Analyst) or be part of a central analytics team serving everyone. The core job does not really change, only the subject matter and who you report to.

So What Does a Data Analyst Actually Do?

Cut through the job description language, and the real job is this: take raw, messy data and turn it into a clear answer that helps someone make a decision. In an average week, that usually looks like:

• Pulling data: mostly through SQL, sometimes Excel or Python, to get relevant numbers out of a database or warehouse. 

• Cleaning it up: dealing with missing values, duplicate rows, formatting issues, and outliers. This eats up more time than people expect. 

• Digging into it: slicing data by time period, region, customer type, or product to spot trends or anything unusual. 

• Building dashboards: using tools like Tableau, Power BI, or Looker so people can check numbers themselves instead of asking every time. 

• Writing it up: turning findings into a short report or slide deck that a busy manager can understand in a few minutes. 

• Talking to people: sitting with stakeholders to figure out what they are actually trying to find out, which is often different from what they first asked.

• Running small experiments: like A/B tests or simple before and after comparisons to check if some business change actually worked.

One way to think about it: a Data Analyst is basically a translator. On one side is a database full of numbers nobody outside the team can make sense of. On the other side is a manager who just wants a straight answer to something like "why did sales drop in the east region last month?" The analyst's whole job is closing that gap, accurately and quickly.

Data Analyst vs Business Analyst vs Data Scientist vs Data Engineer vs ML Engineer

These titles get thrown around like they are interchangeable, but they are not.


Role

Core Question

Typical Output

Key Tools

Data Analyst

What happened, and why?

Dashboards, reports, ad hoc insights

SQL, Excel, Tableau, Power BI, Python

Business Analyst

What does the business need?

Requirements docs, process maps, ROI cases

Excel, Visio, JIRA, SQL

Data Scientist

What is likely to happen, and why?

Statistical models, experiments, predictions

Python, R, ML libraries, statistics

Data Engineer

How do we move and store data reliably?

Pipelines, warehouses, ETL jobs

SQL, Spark, Airflow, cloud platforms

ML Engineer

How do we deploy and scale a model?

Production ML systems, APIs, monitoring

Python, Docker, Kubernetes, MLOps tools

In plain terms:

• Data Analyst: looks backward. Answers "what happened and why" using data that already exists, mostly through SQL and dashboards. 

• Business Analyst: sits closer to the business side than the data itself. More about understanding requirements, mapping processes, and translating a business problem into something a technical team can build. 

• Data Scientist: looks forward. Builds statistical or machine learning models to predict what is likely to happen and figure out why patterns exist in the first place. Usually needs a deeper math and coding background than a typical analyst role. 

• Data Engineer: builds the actual infrastructure, the pipelines and systems that move data from source to warehouse, so analysts and scientists have clean data to work with in the first place. 

• ML Engineer: takes what a Data Scientist builds and makes it work reliably in production, handling deployment, scaling, monitoring, and retraining so a model holds up in the real world, not just in a notebook.

A simple way to remember it: Data Engineers build the roads. Data Analysts drive on them and report what is happening right now. Data Scientists use the same roads to predict what is coming next. ML Engineers take that prediction and turn it into something running automatically at scale. Business Analysts are waiting at the destination, making sure the whole trip was worth it for the business.

The Data Analytics Lifecycle

No matter the company or industry, most analytics work tends to follow a similar sequence:


Stage

What Happens

1. Business Understanding

Figure out what question a stakeholder actually needs answered, and what decision it will affect.

2. Data Collection

Pull data from databases, APIs, spreadsheets, or wherever it happens to live.

3. Data Cleaning

Fix missing values, duplicates, and inconsistent formats. Least exciting part, but takes the most time.

4. Exploratory Analysis

Look for patterns, trends, and anything odd before jumping to conclusions.

5. Analysis / Modeling

Apply whatever statistical method or segmentation is needed to actually answer the question.

6. Visualization & Reporting

Turn findings into charts and a narrative that a non technical person can follow.

7. Communication

Present it to stakeholders, answer their questions, and help them decide something based on it.

8. Monitoring & Iteration

Check whether the decision worked out the way it was supposed to, and adjust going forward.

Worth mentioning, cleaning and exploring the data usually eats up the bulk of an analyst's time, sometimes 60 to 80 percent of it, even though visualization and communication are the parts stakeholders actually see and judge the work by. That mismatch is something most people do not realize until they are actually in the role.

The Takeaway

The real value of a Data Analyst is not just knowing SQL or Excel or Python. It is being able to consistently turn scattered, messy data into something clear enough that someone can act on it, and getting that answer to the right person in a way they will actually use. As companies keep generating more data every year, that translation skill only becomes more important. In essence, a Data Analyst acts as a translator, turning raw data into actionable insights to help leadership, middle management, and operational teams across departments like Sales, Marketing, and Finance make informed decisions. While often confused with related roles like Data Scientists or Engineers, Analysts focus on backward-looking "what and why" answers through a meticulous process where cleaning and preparation consume the majority of their efforts, ultimately serving as a natural entry point into broader data careers.








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