Top 15 Scala Libraries for Data Science in 2021 | Scalac.io
https://scalac.io/blog/top-15-scala-libraries-for-data-science-in-2021/

Про data engineering для тех, кто не в теме: https://www.youtube.com/watch?v=qWru-b6m030
Классное вводное видео.

Cloud Data Warehousing: Understanding Your Options
https://www.datanami.com/2021/04/01/cloud-data-warehousing-understanding-your-options/?utm_source=rss&utm_medium=rss&utm_campaign=cloud-data-warehousing-understanding-your-options

Azure ecosystem

Лучшие практики использования SQL по версии Metabase.

Некоторые советы действительно ценные. Из того, что реально встречалось на практике особенно выделил бы один, так как в свое время удалось существенно оптимизировать время выполнения запроса:
Prefer EXISTS to IN
If you just need to verify the existence of a value in a table, prefer EXISTS to IN, as the EXISTS process exits as soon as it finds the search value, whereas IN will scan the entire table. IN should be used for finding values in lists.

Metabase довольно интересный инструмент, на одном из проектов используем его, надо бы записать видео в продолжение Гайда по BI.
Если у кого-то есть время и интерес поисследовать Metabase, а также записать видео, пишите мне в DM: @valiotti.

Here are more than 100 free course from Microsoft about #DataEngineering, #DataScience and #DataAnalytics. Many of them are #Azure related but also many focus on general knowledge in mention fields.
https://docs.microsoft.com/en-us/learn/browse/?terms=data&roles=ai-engineer%2Cdata-analyst%2Cdata-engineer%2Cdata-scientist

Interesting long read about databases for website analytics. Besides telling about their experience of choosing right solution, author describes migration process and different options. However, he doesn't mention Druid which I think can be a good fit.
https://usefathom.com/blog/worlds-fastest-analytics

Credits to: https://news.1rj.ru/str/rockyourdata/2448

Did you notice that there are no books on data engineering or just a couple which mostly describe different technologies that data engineers can use. Of course, there is "Designing Data-Intensive Applications", but this book makes sense if you have some experience. Courses also focus on technologies but not general knowledge that data engineer should have. Also technologies advertise what problems they solve, but these problems are familiar to more or less experienced data engineers. And currently there is a high demand of data engineers.

Azure Synapse Analytics vs Azure Databricks, a detailed comparison of feature and use-cases
https://www.element61.be/en/resource/when-use-azure-synapse-analytics-andor-azure-databricks

So as you may know, AWS has Lambda and Step Functions. But it seems Azure took a bit different approach with Durable functions. I am not sure if this is a right comparison, but it is interesting idea to have not only stateless functions but also stateful ones.

I wonder how this is implemented under the hood and is AWS planning similar feature for S3?
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace

Partitioning also known as "poor man's indexing"
Never heard of this one 😁