3 measurements spanning 3 days, net +1. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 2,059–2,060 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
11 Aug 2026, 03:06
2,060
+1
7 Aug 2026, 15:31
2,059
no change
7 Aug 2026, 15:21
2,059
first reading
Engagement
20 posts held, back to 27 March 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
49.7%
avg views ÷ 2,060 subscribers
Avg views / post
1,020
2 posts measured
Reaction rate
1.96%
reactions ÷ views · ER floor
Posts in window
2
of 20 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 24 July 2026
Posts held
20 (27 March 2026 – 24 July 2026)
Views total
2,046
Reactions total
40
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 15:31 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
1m 06s
Average length
33s
Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
702 reactions across 20 posts, in 14 distinct kinds. The most used accounts for 19.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
136
19.4%
🤣
118
16.8%
custom 5433863928499150581
113
16.1%
🔥
88
12.5%
❤
66
9.40%
custom 5244673458083734407
57
8.12%
😁
42
5.98%
✍
36
5.13%
😱
14
1.99%
custom 5458682663307057981
13
1.85%
💅
11
1.57%
🤔
5
0.712%
😢
2
0.285%
😭
1
0.142%
Custom emoji. 3 of the rows above are Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The counts beside them areTelegram’s.
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 20 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 702reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 27 March 2026 to 24 July 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram Stars
Stars received
9
across the posts below
Posts paid on
5
of 20 we hold a reading for · 25%
Most on one post
4
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @data_penguin. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 20 most recent posts we hold for this entry, published 27 March 2026 to 24 July 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Advertising
Ad load
5.00%
1 of 20 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
1,520
over 1 measured post
Median views · rest
2,180
over 19 measured posts
An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.
This is a floor, and it can only ever be a floor.A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.
Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.
Advertising tokens recorded on this entry
erid
Posts
First seen
Last seen
2W5zFJTycAD
1
7 July 2026
7 July 2026
A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.
Measured over the 20 most recent posts we hold, published 27 March 2026 to 24 July 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.
Обзор собеседования с Лемана Про
Формат работы: Гибрид
Зп: от 250к просил
Период собеседования: Июль 2026
Итог собеседования: Игнор
————
Общий опыт работы
С какими базами данных вы работали и до какого уровня погружались в SQL и БД?
Миграция Oracle → Greenplum
Опишите подробнее процесс миграции на Greenplum. Какие особенности необходимо было учитывать?
Какая конфигурация использовалась в Oracle — распределённая …
Аналитик из Wildberries показывает, что происходит с данными после того, как дата-инженеры загрузили их в таблицы:
Хочу сегодня порекомендовать канал, который раскрывает дата-аналитику со всех сторон — «Дима SQL-ит». 🛒
Дима работает аналитиком данных в Wildberries и публикует много теории и кейсов из практики — всё структурированно и по делу 🤝
Что рекомендую посмотреть в первую очередь:
🟡Подборки бесплатных матер…
Обзор собеседования со СБЕР v2
ВОПРОСЫ:
Расскажи про интересную рабочую задачу, связанную с данными, которой ты гордишься, и про свою роль в ней.
Какой итоговый объем витрин был, в строках или гигабайтах?
ЗАДАЧА 1:
Дана таблица такого формата
code status
1 активный
2 закрыт
3 аннулирован
3 аннулирован
Как написать запрос, который покажет, есть ли в таблице дублирующиес…
Обзор собеседования со СБЕР
Вопросы:
Какие виды JOIN ты знаешь, и в чем между ними разница?
Что такое anti join?
Что будет при JOIN по ключу, если в ключевых колонках с обеих сторон есть NULL?
Чем отличаются UNION и UNION ALL?
Что такое нормализация таблицы?
Зачем нужна нормализация, и можешь привести пример нормализованной схемы?
Чем отличается OLAP от OLTP?
Был ли у тебя опыт работы со Spark?
Какой основн…
Учить инженерию данных по курсам и роликам — это как собирать пазл без картинки на коробке: детали вроде бы есть, а как они складываются вместе — не всегда понятно
karpovꓸcourses сделали тест-квест, который эту картинку как раз и показывает: вы сами, шаг за шагом, собираете дата-платформу стартапа — от Excel до Kafka, Spark и ClickHouse — и видите, как отдельные инструменты складываются в цельную архитектуру.
Прохо…
Всем привет!
Подскажите плз хорошее компьютерное кресло. Нужно с сеткой и анатомической спинкой.
Я посмотрел несколько видосиков (например, это) и всякие статьи.
Одно нормальное кресло не нашел, склоняюсь к линейке самураев. Но вижу, что в начале у всех отличные отзывы, а после продолжительного использования – плохие.
И кто знает норм компьютерный стол (желательно с крыльями и подъемным механизмом) тоже можете ссыло…
Обзор собеседования с Neoflex
ВОПРОСЫ:
1. Что на каждом слое делают с данными? (опиши 3–4 глаголами: raw, staging, ODS, DDS, data mart)
2. Слой data mart — нормализован или нет?
3. Почему data mart денормализован? Что выигрываем?
4. С какими моделями данных сталкивался: звезда, снежинка, data vault?
5. Как косвенно определить по структуре таблицы (без данных, только атрибуты, типы, констрейнты) — это факт или измер…
🔥 ПЕТ-ПРОЕКТ ДАТА ИНЖЕНЕРА
В эту СРЕДУ 17 ИЮНЯ в 20:00 МСК у нас будет довольно интересный стрим. Причём проводить его будет не только команда BootCamp, но и один из наших выпускников.
К нам придёт Артем — участник 6-го потока BootCamp. За время обучения он собрал полноценный пет-проект и теперь покажет его нам от начала до конца.
➡️ ссылка на стрим
Что вообще будет в проекте?
Берём данные из API New York Times, …
Ребят, сори, что редко пишу посты. Хочу кратенько рассказать, что у меня сейчас происходит.
На работе сейчас идет миграция с Greenplum+dbt -> Lakehouse (S3, iceberg, trino).
Пока что Lakehouse мне не очень нравится) Наверное, в основном потому, что у нас теперь нет dbt и вместо тысячи dbt-моделек мы создаем сотни дагов в аирфлоу. С этим работать неудобно и непривычно.
Сам процесс миграции очень рутинный. Прям много…
Как сейчас устроен рабочий процесс
- 🍴Ты еще работаешь?
🤖Thinking: Пользователь спрашивает, продолжаю ли я работу. Да, я работаю - только что завершил документацию для 12 витрин и сейчас должен перейти к ***. Мне нужно прочитать файл *** и продолжить работу. Давайте перейдем к следующей витрине - ***.
- 🤖 Да, брат, работаю! Только что завершил 12 витрин, сейчас перехожу к следующей
- 🍴 Работай братишка 💅🦦
🤣32custom 52446734580837344077💅4❤1👍1😢1
Showing the 12 most recent of 20 posts we hold for @data_penguin. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Citation-graph rank
Citation-graph rank — 713,991 of 1,169,250entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 11 August 2026 — this
entry's latest reading, not the date you are reading this.
“Айти-Пингвин | Дата инженер” (@data_penguin), 2,060 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/data_penguin.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.