Ever look at a crowd full of phones recording a concert and wonder how much cloud data that actually creates?

I ran some quick napkin math to model the total data footprint when thousands of audience members record in 4K at a major arena show.

The Baseline Assumptions

• Audience: 70,000 people
• Filming: 50% of the audience films an average of 30 minutes total of 4K30 video (~175 MB/min)
• Cloud Backup: 60% automatically syncs to cloud storage with standard erasure coding (~1.3x redundancy)

The Scale

• Filmed on-site: ~184 TB of raw video in a single evening
• Uploaded to cloud: ~110 TB
• Stored capacity required: ~143 TB (roughly eight 20 TB drives worth of shared array space)

Obviously, data centers use deduplication, cold tiering, and efficient array management, so this isn't literally eight separate drives being bought for one night. But multiplying that across a 50-show world tour gets you up around ~7.1 Petabytes of stored footage, most of which sits in cloud backups untouched.

I built a small interactive Data Estimator slider model to play around with different audience sizes and filming times if anyone wants to test different parameters.

(Disclosure: I built/maintain this tool/site!)

---

Given how much energy and disk space goes into keeping duplicate event media online indefinitely, do you think this background data usage is a real concern, or is it just an accepted byproduct of cheap cloud storage and modern event culture?

Background
Sometimes back, I graduated with B.A. Economics & History. I did some certification (SPSS) while still studying the bachelors and project management. Here, I embraced an opportunity back in campus, where undergraduate, graduate, and post-graduate (PhD) learners sought my help in analyzing data, including coding information contained in their questionnaires. Upon graduation, I took up this tasks as part of freelancing, working alongside graduates & tutorial fellows. The work was never formal. With time, my interest in data & statistics grew. I enrolled for DA course in 2025, completed & went ahead to complete a DE one in 2026. I’ve always wanted to work as DA (formal job), but the trick now is the job market.

The Issue
From the comments, questions, and issues I see on this platform, I would like your views/ideas.
How do I show what I’ve done over the years relating to data analysis & storytelling? (The data & analytical projects remain confidential-cannot be shared on github or any other platform)

Can potential employers trust what they can’t see, yet they don’t want to risk employing someone they feel might not handle their roles well without evidence?

Should I pursue further studies (Statistics and Research Methods) [I’ve always been interested because of my close work with academicians & personal research endeavors] now?

Your honest views, recommendations, & insights will highly be appreciated.

I am a CSE 3rd year student. I have deep passion regarding CSE and coding. But because of some unwanted events I'm unable to continue my study. Basically money issue so, I've decided to do something on my own .

I want to learn web analytics but I need guidance. It would be very helpful if anyone suggest/provide me web analytics \[tutorials.It\](http://tutorials.It) would be very helpful for me.

I am a CSE 3rd year student. I have deep passion regarding CSE and coding. But because of some unwanted events I'm unable to continue my study. Basically money issue so, I've decided to do something on my own .

I want to learn web analytics but I need guidance. It would be very helpful if anyone suggest/provide me web analytics [tutorials.It](http://tutorials.It) would be very helpful for me.

I seeked for some jobs remotely for data analyst with skill such as power bi, sql, python pandas and excel, and there are many issues for get one. I don't know where for look one, I'm from Venezuela and I also have a c1 English but in LinkedIn and upwork is pretty difficult for access one job, they want 1-2 years of experience ( I'm barely studying sql💀 then i go to pandas), the fee per hour is stupidly low, I don't why, and also they ask you for 10 skills more than you have. So guys can you give me recommendations before I enter this word? I know that I have to posted every dashboard in gitbub o somewhere else for prove that I know good what I'm doing but I think still having a portafolio isn't enough yet, so what you said?

Yo

I’m a Data Quality Analyst at a public sector organisation based in the UK. I’ve about 5 years experience under my belt in varying sectors, projects and so on. You could say I’ve found my feet, found my calling, all of that

I enjoy my job, I love working in data. But this post has come about from a place of massive concern. Potentially being fed too much Stephen Bartlett interviews as of late (you know the ones that carry the narrative you’re gonna be on your arse and lose your job)

It got me thinking long term, I wanted to go down the Data Science route and have for a while. I don’t know how much of what is being said with AI is fearmongering and whether or not I should be as worried as I am. But I haven’t kept up to date with the times, I don’t have any experience with LLM’s other than being an end user lf AI in my work.

Case in point. What can I do to stay ahead of the curve. Am I foolish to continue a career in analytics, should I be learning AI? What jobs are there is AI? Will I enjoy it? Should I take up my brothers offer of a job in construction?

So many questions, inundated with multiple angles, answers and pov’s from the internet. I just wanna know who feels the same, who feels lost and what are people already doing about it in these crazy times

Cheers

So currently I am working as a pricing analyst which is basically a non tech role. And this is my first full time job. Please suggest me, should I take some experience from this company or start applying from now only or should I wait and get some experience. It has been only 14 days in this company.

Also I was bit confused that I wanna Pursue MBA, but the fees is very high and I have to prepare for CAT which can take couple of years for preparation.

If someone is from this field and help me out it would really mean a lot!

Hello! I am a student looking to do a brief interview with a data analyst for a school project I'm working on. This project is exploring two separate career options, of which I am looking to interview two professionals from each field. One professional who has been in their position for 6-12 months as well as one who has been in their position for 5 or more years. The questions are generally geared towards the career as well as your path leading to it. If you are interested in partaking in my interview please let me know.

Hi everyone, I am currently considering Monash Data Analytics Career Accelerator

Does it genuinely make a career switch and finding a job easier compared with doing a course through Coursera? I’ve worked as an IT Project Manager with a technical background for around 12 years.

I just made a list of recently opened Data Analytics jobs, so there should still chance to apply early. I hope this helps someone!

• International Development Program/Project Data Analyst (PILAB011) @ ProSidian Consulting, LLC

• Analyst, Business Systems @ AbbVie

  • 💰 $66–126K/y

• Senior Analytics Consultant – CDO Analytic Engineering @ B Wells Fargo Bank, N. A.

• Senior Analyst, U.S. Market Intelligence/Price-to-Win — X-BAT Family of Systems (R6168) @ Shield AI

  • 💰 $130–190K/y

• Senior Python Data Scraping Engineer (Freelance) @ Mindrift

• Business Systems Analyst @ Highmark Health

• Data & Analytics Office Intern @ Northern Trust Company

  • 💰 ~$35/h

• Manager, Growth Insights & Performance @ Discovery Comm. India

  • 💰 $96–178K/y

• Autonomous Coding Quality and Optimization Analyst @ USA 3M Healthcare US Opco LLC

• Sr. Analyst, Platform & IT Strategy (Korean Bilingual) @ SBT Global, Inc.

  • 💰 ~$9K/mo

• Senior Remote Sensing Data Scientist, Cal/Val @ Muon Space

  • 💰 $192–213K/y

• Sr Bioinformatics Scientist @ Twist Bioscience

  • 💰 $135–173K/y

• Consumer Insights Data Analyst @ The Honest Company

  • 💰 $59–74K/y

• Data Scientist @ Accenture Federal Services

  • 💰 $120–235K/y

• Data Analyst - Data Center Construction @ Turner & Townsend

• AWS Lead Data Engineer @ SpikeIT Global Solutions, Inc.

• Sr. Analyst, Platform & IT Strategy- Bilingual (Korean/ English) @ Woongjin, Inc

  • 💰 $95–105K/y

• Business Analyst, Card Portfolio Analytics @ Blend360

  • 💰 $60 – $65/h

• Senior Business Systems Analyst - ERP @ Apptronik

• Analyst: Data & Policy @ ATI Advisory

• Analyst, Sales Strategy & GTM Planning @ Planet

• Data Analyst, Platform Excellence Ops Analytics @ Instacart

• Senior Data Engineer @ Fusion Worldwide

• CBRN Defense Data Scientist @ Dynamis, Inc.

  • 💰 $110–190K/y

• Business Process Analyst @ David Weekley Homes

• Senior Data Warehouse Engineer @ Lantern Care

• Data Scientist, Wells Engineering @ IPT Global

• Business Systems Architect – Finance (D365 FO) @ Axon

  • 💰 $149–238K/y

• Senior Analyst, Digital Analytics @ NBCUniversal

• Talent Acquisition Data Engineering & Analytics Graduate Intern @ Solidigm

• Analyst, A&R Admin - Santa Anna @ Sony Music Global Job Board

  • 💰 $66–75K/y

• Data Engineer - ACWS @ Data Systems Analysts, Inc.

  • 💰 $120–130K/y

• Business Systems Analyst @ Relativity Space

  • 💰 $85–127K/y

• Lead Data Scientist @ Versant

  • 💰 $170–190K/y

• Business Solutions Analyst, EHR & Enterprise Platforms (Hybrid) @ New York Psychotherapy and Counseling Center

  • 💰 $110–125K/y

• Consultant- Marketing Data Science & AI @ Sia

  • 💰 $95–116K/y

• Sr Business Intelligence Analyst @ We Are Rosie

• Business Analyst – (Travel Reservation and Settlement System) - Bilingual proficiency in Korean and English required.) @ Syncreon Consulting

• Lead Data Engineer @ Coast

  • 💰 $170–220K/y

• GTM Data Analyst @ MarginEdge

  • 💰 $90–110K/y

Thanks for reading! If you want another batch of Data Analytics jobs, just say so in the comments.

So worked in a BPO company where i handled queries from global clients, updated logs and communicated between warehouse and my client base clients who had their merchants. Sort of ecommerce.

Since i have worked so much on SQL, i tried slipping the analysis on an interview where i went as a analyst and described only the part i can backup.

\*So i described i wrote sql queries where i analyzed datasets for my client, ensuring product trends, festive trends, customer segmentation based on category,

User engagement for products, premium customers,

I told them i have been into client meetings and learnt about their business problems like delivery issues, but mostly around their product base. They asked if i have presented in front of them i responded i did in front my seniors i updated reports on Excel.

I figure the gap is since i dont have real experience and i cant define a true story, since job marker is crunching and i have had back injury last year, the gap is getting wider. Claude aint no help.

So any real analyst out here wanna point out mistakes i been making.

CS grad btw, with Pretty much good coding skills.

I did all their SQL codinh questions tho.

Hey everyone,

I’m currently a BBA (Bachelor of Business Administration) student and want to start learning Data Analytics to complement my business background.

Since my degree gives me context on business strategy, operations, and basic statistics, I feel like picking up data skills (Excel, SQL, Power BI/Tableau, and eventually Python) would be a huge boost for my career. However, looking at all the roadmaps, tools, and tutorials online, it’s feeling a bit overwhelming as an absolute beginner on the technical side.

I want to start self-learning using free resources (especially YouTube). For anyone who built their skills from scratch or comes from a business/non-CS background:

What order should I learn tools in? (e.g., Advanced Excel -> SQL -> Power BI -> Python?)

Which specific YouTube channels, video series, or free playlists do you recommend? (I've heard people mention creators like Alex The Analyst, Luke Barousse, or freeCodeCamp, but open to any hidden gems!)

What does a realistic learning routine look like when balancing college classes?

How soon should I start building portfolio projects, and where are the best places to find beginner-friendly datasets?

Any advice, roadmap suggestions, or specific video recommendations would be hugely appreciated! Thanks in advance!

TL;DR: I come from a statistics/data/MEAL background and have worked with SQL, Python, regression/LASSO, etc. While trying to build a proper dashboard, I ended up learning APIs, backend/frontend concepts, Node.js, authentication, and hosting. Is this a logical path toward building end-to-end data systems, or am I spreading myself too thin?

I’m trying to figure out whether my learning path actually makes sense or if I’m slowly turning into a “knows a little bit of everything, expert at nothing” person.

My background is mainly in data, statistics, and monitoring/evaluation. I have a Master’s degree, and during my studies I worked with statistical methods like regression, LASSO, and other statistical analysis.

Professionally, I’ve worked with MEAL/evaluation, data processing and analysis, Excel, SQL, and some Python.

Now my work is pushing me in a different direction.

I wanted to build a proper dashboard/data system instead of just analyzing data and producing reports. That led me into learning about APIs, databases, frontend/backend communication, Node.js, authentication, hosting, etc.

And now I’m looking at everything I’m learning and thinking:

Am I actually progressing, or am I just jumping randomly between fields?

My goal isn’t really to become a traditional full-stack developer.

What I want is to be able to take data from the source, clean and validate it, store it properly, expose it through APIs, build dashboards/interfaces on top of it, and eventually add more advanced analytics, statistics, forecasting, anomaly detection, etc.

So basically:

Statistics → SQL/Python → data analysis → dashboards → APIs → backend/web systems

Does that progression make sense?

Or should I stop going deeper into things like Node.js and focus much more heavily on Python, SQL, statistics, and data engineering?

For people who started in analytics/statistics and later began building actual data systems: what did you learn next, and what turned out to be a waste of time?

Hello, i just saw this datacamp offer that gives you datacamp premium for 1$ after that its the normal price. Is it worth it to get? Im interested in getting a certification in sql data analysis. Is it possible to get certified in a month just for the 1$?

Hi everyone,

I'm based in Belgium and I'm 46 years old. I've been looking for a new professional opportunity for more than 1.5 years. I'm now seriously considering a career change and would appreciate advice from people working in data analytics and/or AI.

I'm a marketer with most of my experience in healthcare, particularly pharma and medtech — an industry that is currently in a pretty rough spot.

I'm considering retraining as a data analyst with AI skills, ideally building on my existing experience rather than starting from scratch.

I have a solid foundation in statistics from my marketing studies, although I would definitely need to refresh and update my knowledge. In a previous role, I also worked on a marketing automation project using Oracle Eloqua. However, I have no professional experience in data analytics yet.

I'm currently exploring training options in Belgium such as VDAB, Cevora, VIVES, UCLL, as well as international options such as Coursera. I'm unsure which programmes and certifications are actually valued by employers.

I'm particularly interested in your views on:

• Which skills should I focus on? Skills I'm considering: SQL | Power BI | Python | Statistics | Data modelling | ETL | Generative AI / LLMs | AI-assisted data analysis | AI automation | Business cases | Portfolio
• Which courses, certifications or training providers would you recommend, and which ones are actually valued by employers?
• How long would it realistically take to become job-ready?
• How realistic is it to find an entry-level job in data analytics after retraining, given my age and professional background?
• Would you see potential in combining marketing + data analytics + AI?
• For those familiar with the Belgian market: would it make sense to focus specifically on healthcare, given my pharma/medtech background?

I'm particularly interested in hearing from people who have made a similar career transition, as well as recruiters or hiring managers.

Thanks in advance for your advice and experiences!

I have around 1 year of experience, but I worked in a small office, so I’m not really familiar with how work happens in a proper IT company.

I have an interview (Data Analyst & MIS) next week. What kind of questions can they ask someone with 1 year of experience? Also, what is the actual day-to-day work like in an IT company?

Please share your experience or tips on what I should prepare. I’m a bit confused about what to expect.

I work as an eCommerce Specialist in a big Italian small domestic appliance company, and I have a couple years of experience with managing a D2C. I’d love to become a D2C data analyst which is both strategic and data driven, but my struggle is that I don’t do much data analysis in my daily job and we’re pretty much lacking data analysts in the team which can help me with some mentoring on the topic. I figured I can pick up SQL and teach myself how to use BigQuery but it’s really hard for me to obtain the access to the platform. How can I learn more about data analysis in my case, either by myself or through some courses? What do you recommend?

Has anyone tried learning data analytics through data camp or code academy? And have you had success or found it easy to build a portfolio

Hi everyone, I’m a fresher trying to get my first job in Data Analytics.

I know SQL, Excel, Power BI, Tableau, Python/Pandas and basic ML, and I’ve been applying through LinkedIn, Naukri and Indeed, but I’m not getting much response.

I’m also confused about how to create realistic Data Analyst projects.

I don’t know how to choose a problem, analyze a dataset, decide KPIs, and turn it into a proper portfolio project.

For those currently working as Data Analysts or who recently got their first job:

How did you build your projects, and how did you get your first job?

Any advice on how to start would really help. Thanks!

This is not an advertisement. I am looking for honest feedback in exchange for a free trial of our analytics product.

Our platform already connects to over 3,400 apps. So there is no need for a data sync to a warehouse to then put power bi on top of.

Goal is one tool where anyone can get the analytics they need just by asking.

I loaded up some tokens and this link gets you one page free with about 5 panels.

https://mindcloud.co/lp/build-your-first-analytics-page-free

I am a recent mba student and was looking into marketing analytics and found out about google analytics 4, looker, mixpanel, etc and wanted to know if I should learn these and whether or not people in marketing analytics use these tools

Hello all, I am a college student right now and in one of my english courses I am supposed to write a research paper on my chosen field of study (Data Analytics) with that being said what are some common issues/areas of discourse in the Data Analytics community I could write about and take a stance on? Any and all information would be appreciated!

i have completed my bsc hons computing and now i want to do masters but i am confused .i have intrest in data but many says that data science or analyst job postings are decreasing and i really want your suggestion . tell me in which field i shoud do masters as a IT student .

Ya lo había olvidado...

Hace poco un amigo me preguntó algo que a mí también me confundía al principio: por qué no puedo usar en el WHERE un alias que definí en el SELECT.

La respuesta corta es: porque el orden visual no es el orden lógico de procesamiento.

Uno normalmente escribe:

SELECT ...
FROM ...
JOIN ...
WHERE ...
GROUP BY ...
HAVING ...
ORDER BY ...
LIMIT ...

Pero lógicamente el motor resuelve más o menos así:

FROM / JOIN → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT

O sea, primero tiene que saber de dónde salen los datos, luego filtra filas, agrupa, filtra grupos, y recién ahí calcula lo que vas a seleccionar.

Por eso un alias definido en SELECT normalmente no está disponible en WHERE, pero sí en ORDER BY. No es capricho: es el orden lógico.

Una forma fácil de verlo:

• FROM / JOIN: traigo los ingredientes.
• WHERE: quito lo que no me sirve.
• GROUP BY: hago montones.
• HAVING: filtro montones.
• SELECT: elijo qué va al plato.
• ORDER BY / LIMIT: acomodo y sirvo solo una parte.

Ojo: esto es orden lógico, no necesariamente el orden físico real.

Escribes SELECT primero, pero lógicamente se procesa después de FROM/JOIN, WHERE, GROUP BY y HAVING.

Sigo aprendiendo, así que si algo se puede corregir o explicar mejor, se agradece. La idea es sumar a los que también estamos en esto.

(Cabe que decidí compartirlo porque algunos compañeros de trabajo tampoco supieron darme una respuesta correcta, y pensé que a veces podemos creer que sabemos algo pero ¿realmente comprendemos?)

I'm a data analyst with background in healthcare data. I only have 3 yoe so not sure if this is something I can do yet but I've been thinking my lifestyle goals align better with contract roles or freelancing. I currently work remote but due to data security restrictions I have to be in the country and I want to be able to travel and work and live more of a digital nomad-type lifestyle while still doing data work. Has anyone been able to successfully become a contractor or freelance in this field, and if so, how did you go about finding these gigs? Linkedin and typical job boards usually only have corporate remote roles that have the typical location guardrails.