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Channel

Data Science. SQL hub

@sqlhub

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Polls · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry

35,948subscribers

+68 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001434942369
TypeChannel
Username@sqlhub
DescriptionПо всем вопросам- @workakkk @itchannels_telegram - 🔥лучшие ит-каналы @ai_machinelearning_big_data - Machine learning @pythonl - Python @pythonlbooks- python книги📚 @datascienceiot - ml книги📚 РКН: https://vk.cc/cIi9vo #VRHSZ
CreatedBetween 1 April 2019 and 31 October 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live4 October 2026
Measurements held34
Confirmed unchanged1 time, most recently 4 October 2026
On Telegramt.me/sqlhub

Topic

Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 9 September 2026 and assigned it the closest of 31 fixed categories, at 92% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.

Growth

35,85435,98835,9216 August 2026 — 35,880 subscribers6 August 2026 — 35,886 subscribers7 August 2026 — 35,894 subscribers8 August 2026 — 35,892 subscribers9 August 2026 — 35,889 subscribers10 August 2026 — 35,888 subscribers11 August 2026 — 35,898 subscribers12 August 2026 — 35,886 subscribers13 August 2026 — 35,877 subscribers14 August 2026 — 35,881 subscribers16 August 2026 — 35,864 subscribers17 August 2026 — 35,870 subscribers18 August 2026 — 35,872 subscribers19 August 2026 — 35,854 subscribers20 August 2026 — 35,860 subscribers21 August 2026 — 35,873 subscribers23 August 2026 — 35,924 subscribers25 August 2026 — 35,928 subscribers26 August 2026 — 35,916 subscribers28 August 2026 — 35,903 subscribers29 August 2026 — 35,909 subscribers29 August 2026 — 35,921 subscribers31 August 2026 — 35,943 subscribers1 September 2026 — 35,971 subscribers3 September 2026 — 35,984 subscribers4 September 2026 — 35,988 subscribers8 September 2026 — 35,971 subscribers11 September 2026 — 35,955 subscribers13 September 2026 — 35,946 subscribers14 September 2026 — 35,966 subscribers16 September 2026 — 35,983 subscribers18 September 2026 — 35,969 subscribers25 September 2026 — 35,960 subscribers4 October 2026 — 35,948 subscribers35,9486 August 20264 October 2026
34 measurements spanning 59 days, net +68. 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 35,834–36,008 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 34
Measured (UTC)SubscribersChange
4 Oct 2026, 03:1835,948-12
25 Sept 2026, 08:0235,960-9
18 Sept 2026, 18:5635,969-14
16 Sept 2026, 07:5935,983+17
14 Sept 2026, 16:1935,966+20
13 Sept 2026, 01:2235,946-9
11 Sept 2026, 02:5635,955-16
8 Sept 2026, 07:3535,971-17
4 Sept 2026, 23:3835,988+4
3 Sept 2026, 05:5735,984+13
1 Sept 2026, 21:4635,971+28
31 Aug 2026, 20:2635,943+22
29 Aug 2026, 21:2635,921+12
29 Aug 2026, 00:1435,909+6
28 Aug 2026, 01:3335,903-13
26 Aug 2026, 01:0335,916-12
25 Aug 2026, 01:1735,928+4
23 Aug 2026, 15:5435,924+51
21 Aug 2026, 22:2835,873+13
20 Aug 2026, 17:4235,860first reading

Engagement

82 posts held, back to 15 July 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 109 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
7.50%
avg views ÷ 35,948 subscribers
Avg views / post
2,700
33 posts measured
Reaction rate
0.418%
reactions ÷ views · ER floor
Posts in window
33
of 82 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. It is computed over the 32 of 33 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 6 October 2026
Posts held82 (15 July 2026 – 6 October 2026)
Views total88,940
Reactions total361
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken6 Oct 2026, 19:38 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

Photos
≈1,190
Videos
≈99
Links
≈1,190

Lifetime counters from Telegram’s own channel header, read 6 October 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
4m 24s
Average length
44s

Measured directly from 6 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

1,065 reactions across 77 posts, in 12 distinct kinds. The most used accounts for 36.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍38536.2%
❤32730.7%
🔥21520.2%
😁524.88%
👎242.25%
🥰222.07%
😱151.41%
👏70.657%
🎉60.563%
🤔60.563%
🤬40.376%
🤯20.188%

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 81 of the 82 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 1,088 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 82 most recent posts we hold, published 15 July 2026 to 6 October 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.

Advertising

Ad load
7.32%
6 of 82 posts carry an ad marker
Regulatory tokens
6
posts carrying an erid · 5 distinct tokens
Median views · ads
2,120
over 6 measured posts
Median views · rest
2,870
over 76 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. They are printed side by side with the count behind each rather than as a ratio: an ad and an ordinary post are not otherwise matched — for topic, for length, for hour of day — so the gap between them is a description of two groups and not the effect of one being an ad.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2W5zFJogLHX221 August 202624 August 2026
2VSb5xkQt6E117 September 202617 September 2026
2VtzquuHE37119 August 202619 August 2026
2Vtzqx3mtnf120 August 202620 August 2026
2W5zFGPtHER116 July 202616 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 82 most recent posts we hold, published 15 July 2026 to 6 October 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.

Recent posts

6 Oct 2026, 11:40 UTC≈1,120 views4 reactionsread 6 October 2026

⚡️ SQL с подвохом: NULL в пустом списке Что выведет PostgreSQL? SELECT NULL IN (SELECT 1 WHERE FALSE) AS a, NULL NOT IN (SELECT 1 WHERE FALSE) AS b; Ответ: `a = false`,⚡️ SQL с под Подзапрос возвращаетL в пустом списк Совпадений нет, поэтомув пуствозвращаетодвохом: а SQL с подвозвращаетодвохом: даже если проверяемое значение равно NULL. Для сравнения:м: NULL в пустовернёт NULL. Пустой набор меняет рез…

👍3🔥1

3 Oct 2026, 11:10 UTC≈3,110 views26 reactionsread 6 October 2026
Photo

DOOM запустили внутри SQL-базы. Каждый кадр теперь результат запроса 🤯 Лукас Фогель собрал SQLDoom: игровая логика и рендеринг оригинального DOOM работают внутри CedarDB. Карты, монстры, оружие и состояние игры хранятся в таблицах. SQL рассчитывает движение, атаки и столкновения, а затем собирает картинку: • около 1300 строк SQL и 89 CTE отвечают за рендеринг; • ещё 5900 строк SQL реализуют игровую логику; • кадры…

🔥13❤8😁3🥰2

3 Oct 2026, 09:09 UTC≈1,970 views1 reactionsread 6 October 2026
Photo

Из аналитика в дата-инженеры: переход реален? 🤔 В последнее время все чаще дата-инженерия для аналитиков становится более привлекательной, но в то же время страшной. С одной стороны, хочется понимать не только то, что происходит с данными, но и как эти данные вообще доезжают до нас. Откуда берутся, где хранятся, как обновляются, почему сегодня в витрине 10 млн строк, а завтра 8 млн и кто опять сломал загрузку🥲 И п…

🔥1

2 Oct 2026, 11:00 UTC≈2,010 views10 reactionsread 6 October 2026
Photo

🧠 TIL: SQLite превращает числа в текст сразу по две цифры Обычно integer → string делают циклом: / 10 → берём цифру % 10 → остаток и повторяем снова. SQLite идёт хитрее. В исходниках есть строка на 200 символов, содержащая все пары от 00 до 99: 000102030405...979899 При преобразовании числа SQLite обрабатывает его по две цифры за раз и просто берёт нужную пару символов по индексу. То есть вместо множества опер…

❤4👍3🔥3

1 Oct 2026, 12:04 UTC≈2,170 views5 reactionsread 6 October 2026
Photo

GitHub представил Agentic Engineering System - фреймворк для команд, которые внедряют AI-агентов в разработку. Идея простая: больше сгенерированного кода ещё не означает больше пользы. GitHub предлагает строить работу вокруг трёх вещей: - Governance — что агентам можно делегировать и где нужен человек - Shared Knowledge — код, документация, решения, телеметрия и контекст - Customer Value — приносит ли ускорение ре…

❤3👍1🔥1

1 Oct 2026, 09:37 UTC≈1,910 views4 reactionsread 6 October 2026
Photo

Tencent обошла экспортный запрет США: 100 000 ИИ-чипов в аренду у Oracle По данным Financial Times, Tencent арендовала у Oracle около 100 000 передовых ИИ-чипов на 5 лет. Сумма сделки около 7 млрд долларов, то есть примерно 14 000 долларов за чип в год. Сами чипы в Китай не попадают. Они стоят в нескольких дата-центрах Oracle в Юго-Восточной Азии, а Tencent получает к ним доступ удалённо. Формально это законно. Эк…

❤2👍1🔥1

30 Sept 2026, 17:04 UTC≈2,090 views8 reactionsread 6 October 2026

Полезный совет для MySQL 8: используй LATERAL, когда для каждой строки нужно получить «лучшие N связанных записей». Например, взять последние 3 заказа каждого пользователя: SELECT u.id, u.name, o.id AS order_id, o.created_at FROM users u JOIN LATERAL ( SELECT id, created_at FROM orders WHERE orders.user_id = u.id ORDER BY created_at DESC LIMIT 3 ) o ON TRUE; Без LATERAL такую з…

👍5❤2🔥1

30 Sept 2026, 15:00 UTC≈1,970 views2 reactionsread 6 October 2026
Photo

🗄 ИИ-агент работает с базой, но как не дать ему показать лишнее? 6 октября в Архитектурном клубе Яндекс 360 разберут архитектуру ИИ-агента над корпоративными данными. В том числе поговорят о поиске, метаданных и фильтрах, а главное — о том, как наследовать права доступа исходных систем. Также Даниил Смирнов, руководитель разработки ИИ-платформы Яндекс 360, расскажет, как изолировать данные пользователей и организац…

❤1👍1

29 Sept 2026, 11:56 UTC≈2,370 views9 reactionsread 6 October 2026
Photo

🖥 Vector-базы не умерли, но для многих задач отдельный сервер уже не нужен. sqlite-vec добавляет vector search прямо в SQLite через компактное расширение. Что это даёт: - без Pinecone - без Weaviate - без Qdrant - без отдельного vector DB сервера - всё хранится рядом с обычными данными в SQLite Расширение маленькое, open source и запускается везде, где работает SQLite. Для локальных AI-приложений, RAG, embedded-сц…

👍8❤1

28 Sept 2026, 16:06 UTC≈2,330 views3 reactionsread 6 October 2026
Poll

Для чего используется команда ANALYZE в PostgreSQL?

  1. Для проверки целостности файлов7%
  2. Для удаления неиспользуемых индексов6%
  3. Для дефрагментации таблиц3%
  4. Для сбора статистики о распределении данных84%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

❤3

28 Sept 2026, 14:57 UTC≈2,160 views6 reactionsread 6 October 2026
Photo

🔥Полноценный data stack внутри своего контура Когда данных становится много, одного SQL-движка или отдельной СУБД уже недостаточно. Нужна связка инструментов, которая закрывает весь путь данных: от загрузки и хранения до обработки, аналитики и визуализации. Такой сценарий можно реализовать в Stackland - платформе, которая разворачивается локально в контуре компании. В обновлении появились PaaS-компоненты Yandex Cl…

❤3👍3

28 Sept 2026, 11:07 UTC≈2,340 views11 reactionsread 6 October 2026
Photo

⚡️ Запустили PostgreSQL в Docker и случайно открыли его всей сети? Команда docker run -p 5432:5432 ... по умолчанию публикует порт на всех сетевых интерфейсах хоста. На Linux правило UFW, закрывающее порт, может не помочь: Docker направляет трафик к контейнеру до обработки правил UFW. Если доступ нужен только с самого сервера, укажите адрес явно: docker run -p 127.0.0.1:5432:5432 ... Для всех контейнеров можно …

👍10🔥1

Showing the 12 most recent of 82 posts we hold for @sqlhub. 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.

Polls

The 4 polls we hold for this entry, as Telegram rendered them when we read the post. A poll’s figures keep moving after that, so each one is dated.

28 Sept 2026, 16:06 UTCAnonymous Quiz461 voters

Для чего используется команда ANALYZE в PostgreSQL?

  1. Для проверки целостности файлов7%
  2. Для удаления неиспользуемых индексов6%
  3. Для дефрагментации таблиц3%
  4. Для сбора статистики о распределении данных84%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

16 Sept 2026, 20:10 UTCAnonymous Quiz446 voters

Какой объект в PostgreSQL используется для нормализации слов (удаления окончаний) при создании поискового индекса tsvector?

  1. Parser15%
  2. Dictionary23%
  3. Lexer30%
  4. Tokenizer31%

Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.

23 Aug 2026, 16:40 UTCAnonymous Quiz489 voters

Как называется механизм PostgreSQL, позволяющий ограничить видимость строк в таблице для пользователя на основе определенных правил (политик)?

  1. Column-Level Security8%
  2. Table Access Control16%
  3. View-Only Access11%
  4. Row-Level Security (RLS)65%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

16 Jul 2026, 16:03 UTCAnonymous Quiz523 voters

Какой параметр в postgresql.conf задает объем общей памяти, используемой сервером для кэширования блоков данных?

  1. shared_buffers53%
  2. temp_buffers22%
  3. work_mem21%
  4. max_connections4%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.

The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.

Read from the 82 most recent posts we hold, published 15 July 2026 to 6 October 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.

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 50 registered channels — 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. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.

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.

Channels Telegram recommends alongside this one

Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.

Анализ данных (Data analysis)
@data_analysis_ml · 50,680
#1
Machinelearning
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#2
Математика Дата саентиста
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#3
Python/ django
@pythonl · 58,613
#4
DevOps
@DevOPSitsec · 23,671
#5
Big Data AI
@bigdatai · 18,202
#6
Machine learning Interview
@machinelearning_interview · 30,334
#7
Python вопросы с собеседований
@python_job_interview · 24,860
#8
Машинное обучение RU
@machinelearning_ru · 18,200
#9
Linux Academy
@linuxacademiya · 28,459
#10
Data Science
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#11
Data Secrets
@data_secrets · 94,538
#12
Data Science Jobs
@datascienceml_jobs · 21,310
#13
Инжиниринг Данных
@rockyourdata · 23,707
#14
Аналитик данных
@dataanlitics · 6,252
#15
Искусственный интеллект. Высокие технологии
@vistehno · 71,011
#16
Python RU
@pro_python_code · 12,343
#17
Машиннное обучение | Наука о данных Библиотека
@machinelearning_books · 16,859
#18
Библиотека С# С++
@cpluscsharp · 10,085
#19
LEFT JOIN
@leftjoin · 41,847
#20
C# (C Sharp) programming
@csharp_ci · 18,060
#21
Python Jobs
@python_djangojobs · 14,855
#22
Reveal the Data
@revealthedata · 27,780
#23
Data Science | Machinelearning [ru]
@devsp · 19,787
#24
Javascript
@javascriptv · 16,939
#25
Rust
@rust_code · 8,925
#26
Neural Networks | Нейронные сети
@neural · 11,582
#27
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@dsproglib · 18,331
#28
Datalytics
@datalytx · 8,816
#29
Artificial Intelligence
@ArtificialIntelligencedl · 16,747
#30
Data jobs — вакансии по data science, анализу данных, аналитике, искусственному интеллекту
@datajob · 14,863
#31
Golang Books
@golang_books · 16,937
#32
Библиотека программиста
@proglibrary · 78,238
#33
Golang вопросы собеседований
@golang_interview · 15,038
#34
IT Portal
@IT_Portal · 99,777
#35
Хабр
@habr_com · 132,847
#36
No Data No Growth | Pavel Bukhtik
@nodatanogrowth · 12,774
#37
SQLpedia | Базы данных
@sql_wiki · 5,945
#38
Data engineer / SQL работа
@datascjobs · 5,684
#39
Data jobs feed
@datajobschannel · 12,361
#40
Данные на стероидах
@sterodata · 3,244
#41
Базы данных | Access, SQL, Big Data
@databases_secrets · 30,086
#42
BZD • Книги для программистов
@bzd_channel · 36,381
#43
Python Developer
@python_tg · 20,904
#44
Нескучный Data Science
@not_boring_ds · 12,045
#45
Аналитика данных / Data Study
@data_study · 9,466
#46
Библиотека задач по Data Science | тесты, код, задания
@ds_problems_lib · 3,975
#47
Start Career in DS
@start_ds · 11,487
#48
Github
@github_code · 2,538
#49
Библиотека питониста | Python, Django, Flask
@pyproglib · 37,258
#50
Java Books
@java_library · 14,194
#51
DeepSchool
@deep_school · 10,729
#52
Yandex for ML
@yandexforml · 18,948
#53
data.csv
@data_csv · 13,823
#54
Машинное обучение. Книги по программированию
@maschinelearning · 10,830
#55
karpov.courses
@KarpovCourses · 27,406
#56
Библиотека собеса по Data Science | вопросы с собеседований
@ds_interview_lib · 4,466
#57
Golang
@Golang_google · 40,420
#58
Kali Linux
@linuxkalii · 55,481
#59
Python tests
@python_testit · 6,888
#60
C++ Academy
@cpluspluc · 15,487
#61
Java
@javatg · 16,716
#62
PHP Academy
@phpshka · 9,216
#63
C# 1001 notes
@csharp_1001_notes · 6,668
#64
Английский для программистов
@english_forprogrammers · 8,249
#65
эйай ньюз
@ai_newz · 97,222
#66
Время Валеры
@cryptovalerii · 31,566
#67
Django Python
@Django_pythonl · 6,613
#68
Physics.Math.Code
@physics_lib · 146,792
#69
Сиолошная
@seeallochnaya · 80,254
#70
настенька и графики
@nastengraph · 28,181
#71
Denis Sexy IT 🤖
@denissexy · 137,955
#72
IT мемы | Мемы программиста
@memes_prog · 6,242
#73
Love. Death. Transformers.
@lovedeathtransformers · 25,727
#74
CodeCamp
@codecamp · 180,250
#75
[PYTHON:TODAY]
@python2day · 63,715
#76
XOR
@xor_journal · 213,509
#77
GitHub Community
@github · 155,595
#78
Зарплатник Аналитика
@zarplatnik_analytics · 11,646
#79
React JS
@react_tg · 16,236
#80
Простой Python | Программирование
@python_piton_javascript · 125,849
#81
Поступашки - ШАД, Стажировки и Магистратура
@postypashki_old · 46,014
#82
Job for Analysts & Data Scientists
@foranalysts · 36,999
#83
Дата-сторителлинг
@data_publication · 11,932
#84
Young&&Yandex
@Young_and_Yandex · 111,614
#85
Мобильная разработка
@mobdevelop · 3,873
#86
Библиотека Go-разработчика | Golang
@goproglib · 24,058
#87

Read from Telegram’s recommendation API, most recently 2 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Linux Academy
@linuxacademiya · 28,459
Telegram ranks this channel #4 of 84 here — alongside 83 others — read 9 September 2026
Анализ данных (Data analysis)
@data_analysis_ml · 50,680
Telegram ranks this channel #4 of 93 here — alongside 92 others — read 25 August 2026
DevOps
@DevOPSitsec · 23,671
Telegram ranks this channel #5 of 90 here — alongside 89 others — read 18 September 2026
Python вопросы с собеседований
@python_job_interview · 24,860
Telegram ranks this channel #6 of 90 here — alongside 89 others — read 15 September 2026
Python/ django
@pythonl · 58,613
Telegram ranks this channel #6 of 84 here — alongside 83 others — read 22 August 2026
Искусственный интеллект. Высокие технологии
@vistehno · 71,011
Telegram ranks this channel #7 of 94 here — alongside 93 others — read 20 August 2026
Базы данных | Access, SQL, Big Data
@databases_secrets · 30,086
Telegram ranks this channel #11 of 82 here — alongside 81 others — read 7 September 2026
Data Science
@datascienceiot · 42,567
Telegram ranks this channel #11 of 76 here — alongside 75 others — read 29 August 2026
Data Science Jobs
@datascienceml_jobs · 21,310
Telegram ranks this channel #22 of 97 here — alongside 96 others — read 24 September 2026
Секреты аналитики | Data Science, BI, Tableau
@analytics_secrets · 46,786
Telegram ranks this channel #25 of 85 here — alongside 84 others — read 27 August 2026
Data Science | Machinelearning [ru]
@devsp · 19,787
Telegram ranks this channel #26 of 91 here — alongside 90 others — read 30 September 2026
Machinelearning
@ai_machinelearning_big_data · 278,381
Telegram ranks this channel #30 of 95 here — alongside 94 others — read 10 August 2026
Machine learning Interview
@machinelearning_interview · 30,334
Telegram ranks this channel #36 of 98 here — alongside 97 others — read 7 September 2026
Python Learning
@Python_per_month · 28,173
Telegram ranks this channel #42 of 86 here — alongside 85 others — read 9 September 2026
LEFT JOIN
@leftjoin · 41,847
Telegram ranks this channel #43 of 95 here — alongside 94 others — read 29 August 2026
Python Books. Книги по питону
@pythonbooks · 38,507
Telegram ranks this channel #48 of 65 here — alongside 64 others — read 17 September 2026
Инжиниринг Данных
@rockyourdata · 23,707
Telegram ranks this channel #51 of 93 here — alongside 92 others — read 17 September 2026
Python Hacks
@python_secrets · 40,572
Telegram ranks this channel #54 of 82 here — alongside 81 others — read 30 August 2026
Golang
@Golang_google · 40,420
Telegram ranks this channel #56 of 90 here — alongside 89 others — read 30 August 2026
Kali Linux
@linuxkalii · 55,481
Telegram ranks this channel #56 of 89 here — alongside 88 others — read 23 August 2026
Python обучающий
@pythonist24 · 55,754
Telegram ranks this channel #58 of 88 here — alongside 87 others — read 23 August 2026
Библиотека питониста | Python, Django, Flask
@pyproglib · 37,258
Telegram ranks this channel #65 of 84 here — alongside 83 others — read 1 September 2026
Библиотека программиста
@proglibrary · 78,238
Telegram ranks this channel #66 of 82 here — alongside 81 others — read 19 August 2026
Простой Python | Программирование
@python_piton_javascript · 125,849
Telegram ranks this channel #68 of 84 here — alongside 83 others — read 13 August 2026

This channel appears in 26 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

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 4 October 2026 — this entry's latest reading, not the date you are reading this.

“Data Science. SQL hub” (@sqlhub), 35,948 subscribers as measured 4 October 2026. Telegram Register, tgregister.com/channel/sqlhub.

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.