The world of IT and AI moves so fast that the only way to keep up is to keep learning. I firmly believe that sharing your own knowledge and breaking down new concepts through real examples is the best path to understanding. Today we continue exploring data visualization — one of the fundamental disciplines in the world of AI and analytics. We'll look at nine chart types using real Requests data from Cloudflare for my site devitpro.uz, covering the period from September 1 to October 1, 2026.

The data for this breakdown is a genuine treasure trove for learning. Over the month my site received 64.87 thousand requests, was visited by 2.3 thousand unique visitors from 60 countries around the world. These aren't abstract numbers from a textbook — this is live statistics from a working project. It's precisely on data like this that you best understand how one chart type differs from another and when to use each one.

My goal: not just to show nine beautiful charts, but to teach you to think in visualizations. When you look at data and immediately know which chart will reveal it best — that's the true skill of an analyst and engineer.

The Data We're Working With

Before building charts, it's essential to understand the underlying data. This is Requests statistics from the Cloudflare service, collected for the period from September 1 to October 1, 2026. Cloudflare is a global content delivery and security network through which all traffic to devitpro.uz passes. The dataset covers web traffic, unique visitors, requests through Cloudflare, and geographic distribution across 60 countries.

64.87k
Total Requests
Total requests from 09/01/2026 to 10/01/2026
2.3k
Unique Visitors
Total Unique Visitors over the previous 30 days
210
Daily Maximum
Maximum Unique Visitors in a single day
29.88k
Cloudflare Requests
Uncached requests over the previous 30 days

Web Traffic by Host

It's worth highlighting traffic distribution by host separately. Over the reporting period the main domain leads — www.devitpro.uz with 36.05 thousand requests, followed by devitpro.uz with 20.29 thousand requests. This tells us that most users arrive at the primary domain, while the mirror without www is used less frequently.

Cached vs Uncached Requests

Cloudflare analytics aggregates two types of requests. Uncached — requests with responses fetched from your origin server. Cached — requests served directly from the Cloudflare cache. This helps you understand how much content is being cached over time. The higher the amount cached by Cloudflare, the lower the load on your origin server, which frees up your infrastructure resources.

Nine Types of Visualization — A Breakdown

Below are nine charts, each answering a different question. I built them all on the same Cloudflare dataset so you can compare how perception shifts depending on the visualization type.

1. 3D Chart — Requests by Month
Volumetric bars display monthly periods. Height = number of requests.
Jun — 14,443 · Jul — 8,897 · Aug — 23,267 · Sep — 64,870
2. Diagram — Distribution by Top Hosts
A pie diagram shows each source's share of total traffic.
www.devitpro.uz — 36.05k · devitpro.uz — 20.29k · other sources — 8.53k
3. Line Chart — Requests Over Time
A line chart reveals the growth trend of requests month by month.
September showed record growth — 64,870 requests.
4. Stacked Chart — Contribution by Country
Stacked areas show the cumulative contribution of each region to total traffic.
Top 5: United States, Uzbekistan, Netherlands, Italy, Germany.
5. Normalized Stacked Chart
All values are normalized to 100% — showing percentage share by region.
Ideal for comparing traffic structure across different periods.
6. Marker Chart — Requests by Country
Marker points show the number of requests from each country.
Each marker represents a country and its traffic volume.
7. Marker and Stacked Chart
Markers plus stacking — you see both dynamics and cumulative contributions.
Stacking helps visualize cumulative traffic growth.
8. Normalized Marker and Stacked Chart
Maximum information: markers, stacking, and normalization in one chart.
Each line totals 100%, and the markers show each country's share.
9. Volumetric Chart — Requests by Region
3D bars show traffic volume from the top 10 countries worldwide.
Volumetric charts add perceptual depth and work well in presentations.

Regional Map of Cloudflare Requests

I've extracted the regional statistics into a separate block — they deserve their own section. The data covers 60 countries around the world. Each card shows the number of requests and its percentage of the total. The top 3 regions are highlighted with a golden accent.

United States US
8,350 27.9%
Uzbekistan UZ
5,576 18.6%
Netherlands NL
2,112 7.1%
Italy IT
1,953 6.5%
Germany DE
1,576 5.3%
United Kingdom GB
1,244 4.2%
Belgium BE
887 3.0%
Japan JP
855 2.9%
Russia RU
768 2.6%
Canada CA
727 2.4%
Brazil BR
620 2.1%
China CN
406 1.4%
Poland PL
377 1.3%
Australia AU
306 1.0%
Spain ES
285 1.0%
Saudi Arabia SA
280 0.9%
France FR
280 0.9%
Israel IL
266 0.9%
Romania RO
201 0.7%
Czech Republic CZ
155 0.6%
Finland FI
126 0.5%
Luxembourg LU
108 0.4%
Switzerland CH
80 0.3%
Sweden SE
77 0.3%
Bangladesh BD
77 0.3%
Indonesia ID
75 0.3%
Denmark DK
69 0.2%
India IN
52 0.2%
Ukraine UA
50 0.2%
United Arab Emirates AE
44 0.1%
Kazakhstan KZ
43 0.1%
Hungary HU
27 0.1%
Thailand TH
24 0.1%
Mexico MX
22 0.1%
Iran IR
21 0.1%
Lithuania LT
21 0.1%
Uganda UG
20 0.1%
Ireland IE
20 0.1%
New Zealand NZ
20 0.1%
Jamaica JM
19 0.1%
Austria AT
19 0.1%
South Africa ZA
17 0.1%
Turkey TR
17 0.1%
Colombia CO
9 0.0%
Vietnam VN
7 0.0%
Venezuela VE
5 0.0%
Philippines PH
5 0.0%
Iceland IS
5 0.0%
Slovenia SI
5 0.0%
Korea KR
4 0.0%
Argentina AR
4 0.0%
Bolivia BO
4 0.0%
Portugal PT
2 0.0%
Togo TG
2 0.0%
Bulgaria BG
2 0.0%
Latvia LV
2 0.0%
Azerbaijan AZ
2 0.0%
Malaysia MY
2 0.0%
Peru PE
1 0.0%
Paraguay PY
1 0.0%
Guatemala GT
1 0.0%
Croatia HR
1 0.0%
Belarus BY
1 0.0%
Bhutan BT
1 0.0%

What I Took Away From This Experience

After building all nine charts, I arrived at several important conclusions. Here they are — short and to the point.

  • The chart type matters more than the data. The same numbers in different visualizations tell different stories.
  • Markers give precision. When you need to see specific values, not just a trend — markers are irreplaceable.
  • Stacking shows the whole picture. It helps visualize cumulative growth and each country's contribution to the total.
  • Normalization saves the day. When comparing structure rather than absolute values, 100% stacking is unmatched.
  • 3D for presentations, 2D for analysis. Volume impresses, but precision is lost.
  • Cloudflare provides invaluable data. 60 countries, 64.87k requests — a living laboratory for learning visualization.
  • Traffic geography is strategy. Looking at country distribution reveals where infrastructure optimization is needed.

Verdict: Should You Learn Visualization in 2026?

Unequivocally — yes. In a world where data has become the new oil, the ability to visualize it isn't a "nice-to-have skill" — it's baseline literacy for any engineer or analyst. Especially now, when AI generates enormous volumes of data, humans need to quickly separate signal from noise. Charts are the best tool for that.

But there's a nuance. Each chart type has its own domain of application. Line charts show trends. Stacked charts show total volume and contribution. Normalized charts show structure. Markers show precise values. Volumetric charts create an impression. Only by learning to choose the right type for the task will you become a true master of visualization.

For me personally, this breakdown became another step toward understanding how data turns into knowledge. I realized that behind every chart lies a story — a story of people, countries, and technologies. And if you learn to read those stories, you gain an advantage in any field of IT and AI.