#Scala
Old but good article about cake pattern for dependency injection in #Scala
https://medium.com/rahasak/scala-cake-pattern-e0cd894dae4e

Valid points about data teams.
https://podcasts.google.com/?feed=aHR0cHM6Ly93d3cuZGF0YWVuZ2luZWVyaW5ncG9kY2FzdC5jb20vZmVlZC9tcDMv&ep=14&episode=cG9kbG92ZS0yMDIwLTEyLTA4dDE1OjM2OjU0KzAwOjAwLTQ2NjQzODM3YzViMTI2NQ

Lakehouse = DW + Data Lake.

Примеры lakehouse:
- Redshift + Redshift Spectrum
- Snowflake
- Databrics Delta Lake
- Azure Synapse Analytics

Попался очень интересный paper, который был только недавно опубликован основателями Databricks.

This paper argues that the data warehouse architecture as we know it today will wither in the coming years and be replaced by a new architectural pattern, the Lakehouse, which will (i) be based on open direct-access data formats, such as Apache Parquet, (ii) have first class support for machine learning and data science, and (iii) offer state-of-the-art performance. Lakehouses can help address several major challenges with data warehouses, including data staleness, reliability, total cost of ownership, data lock-in, and limited use-case support.

#Scala #cheatsheet
https://alvinalexander.com/downloads/scala/Scala-Cheat-Sheet-devdaily.pdf

I am using #dbt for my latest project and it definitely is a great tool. I wouldn't compare it with #Spark which is great tool to crunch data both on a single machine and cluster. But for me #dbt is great since you can store your reports in version control and deploy them via CI/CD.

У data сообщества большие планы на dbt.

#Tableau #Visualization
https://news.1rj.ru/str/rockyourdata/2054

#cheatsheet
#cheatsheet
https://twitter.com/gp_pulipaka/status/1312573918255951875?s=09

New #opensource projects from #AWS #LinkedIn and #Netflix:
1. Optimizing data warehouse storage
2. FastIngest: Low-latency Gobblin with Apache Iceberg and ORC format
3. Testing data quality at scale with PyDeequ

Data visualization tools comparison from Dropbox.
https://dropbox.tech/application/why-we-chose-apache-superset-as-our-data-exploration-platform

It seems Apache Iceberg is gaining momentum. So it worths getting familiar with it.
https://www.dremio.com/data-lake/apache-iceberg/

I would like to continue the topic with this article on CAP theorem.

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How to Learn Data Science in 2020 | Python, R, SQL, Data Viz | Towards Data Science
https://towardsdatascience.com/how-to-teach-yourself-data-science-in-2020-f674ec036965?gi=ccbaf7162d8c

I am not a big fan of JavaScript but learning how it was created from creator himself is really interesting. And generally a great conversation about privacy and browsers.
https://podcasts.google.com/?feed=aHR0cHM6Ly9sZXhmcmlkbWFuLmNvbS9mZWVkL3BvZGNhc3Qv&ep=14&episode=aHR0cHM6Ly9sZXhmcmlkbWFuLmNvbS8_cD00NTI2

FP vs OOP