Channel
@data_engineering_study
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
16subscribers
+0 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001438103730 |
|---|---|
| Type | Channel |
| Username | @data_engineering_study |
| Description | data_engineering_study |
| Created | Between 1 April 2019 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/data_engineering_study |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Aug 2026, 21:01 | 16 | no change |
| 6 Aug 2026, 09:10 | 16 | first reading |
19 posts held, back to 12 December 2021 — 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.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 19 posts for this entry, the most recent from 8 July 2025. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 11 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Anchor_Modeling____GP.pdf - Яндекс Документы https://docs.yandex.ru/docs/view?tm=1751981619&tld=ru&lang=ru&name=Anchor_Modeling____GP.pdf&text=anchor%20data%20modelling&url=https%3A%2F%2Fassets.ctfassets.net%2Foxjq45e8ilak%2F2pJZtNXYlJVcFY1ZVpvkqb%2F2477cacd2d2c8d521e42b02885b25694%2FAnchor_Modeling____GP.pdf&lr=213782&mime=pdf&l10n=ru&type=touch&sign=83af6f415b79fb1126f0a1ae8be6a1f1&keyno=0&nosw=1&serpParams=tm%3D17…
Part 1: Writing and testing your first SQL pipeline | Feldera Documentation https://docs.feldera.com/tutorials/basics/part1
Materialize For Everyone: Introducing Self-Managed and our Free Community Edition https://materialize.com/blog/materialize-for-everyone/?sf_campaign=701TR00000WvQY3YAN&utm_term=March&hs_contact=24927&hs_content=187633636528&utm_campaign=FY26_Newsletter&utm_medium=email&_hsenc=p2ANqtz-_eRrpm9vDQNpjDAMWKdLUNN-ufbi8Ao5gcZFDcLqx_CMCXJ7h2FREvWBzhYDjAoAxZB-bL7c4rphOk7GCjda4yeGWz0Q&_hsmi=353510082&utm_content=&utm_source=ne…
https://blog.min.io/building-a-high-performance-on-prem-data-pipeline-with-materialize-and-minio-aistor/
https://github.com/MaxHastings/Kolo
https://github.com/sinaptik-ai/pandas-ai Удивительная вещь, которая прошла мимо меня (а существует аж с апреля 2023 года) Pandas + LLM + BI в одной опенсорс коробке, главное датасет отдай нормальный!🙂 @ohmydataengineer - канал "🕯Труба Данных" немного меньше недолюбливает Pandas
https://github.com/IBM/data-prep-kit
Hands-on with Apache Iceberg on Your Laptop: Deep Dive with Apache Spark, Nessie, Minio, Dremio, Polars and Seaborn | by Alex Merced | Data, Analytics & AI with Dremio | Sep, 2024 | Medium https://medium.com/data-engineering-with-dremio/hands-on-with-apache-iceberg-on-your-laptop-deep-dive-with-apache-spark-nessie-minio-dremio-c5d689b01730
Building a large scale unsupervised model anomaly detection system — Part 1 | by Anindya Saha | Apr, 2023 | Lyft Engineering https://eng.lyft.com/building-a-large-scale-unsupervised-model-anomaly-detection-system-part-1-aca4766a823c
All of NoSQL is because of this… | Max De Marzi https://maxdemarzi.com/2023/02/27/ragedb-needs-worst-case-optimal-joins/
Ещё успел завести и немного потыкать вот эту штуку https://github.com/flekschas/jupyter-scatter Сразу некоторое разочарование, что нет ховеров и нельзя экспортировать выделение куда-то вне виджета. Пока потенциал этой штуки не раскрыл. Потом посмотрим. Теперь в планах соединить графовые и текстовые признаки, доделать кластеризацию и какую-то интерпретацию кластеров. Потом уже можно будет снова качать данные и обновля…
https://rethink.synadia.com/episodes/7/
Showing the 12 most recent of 19 posts we hold for @data_engineering_study. 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.
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.
Names
Channels on the register whose handles appear in this channel's posts.
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.
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.
“my data engineering study” (@data_engineering_study), 16 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/data_engineering_study.
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.