Основы Docker за Х часов и Y дней / Хабр
https://habr.com/ru/post/337306/

Docker на практике by Иан Милл, Эйдан Хобсон Сейерс.pdf

Простая идея Docker – упаковка приложения и его зависимостей в единый развертываемый контейнер – породило ажиотаж в индустрии программного обеспечения. Теперь контейнеры являются крайне необходимыми для корпоративной инфраструктуры, а Docker представляет собой бесспорный отраслевой стандарт.

Руководство по Docker Compose для начинающих / Блог компании RUVDS.com / Хабр
https://habr.com/ru/company/ruvds/blog/450312/

Orchestration Frameworks for Big Data | by Javier Ramos | ITNEXT
https://itnext.io/orchestration-frameworks-for-big-data-cfb9d3af6e7e

ETL with Azure Cookbook Practical recipes for building modern ETL solutions to load and transform data from any source

- Explore ETL and how it is different from ELT
- Move and transform various data sources with Azure ETL and ELT services
- Use SSIS 2019 with Azure HDInsight clusters
- Discover how to query SQL Server 2019 Big Data Clusters hosted in Azure
- Migrate SSIS solutions to Azure and solve key challenges associated with it
- Understand why data profiling is crucial and how to implement it in Azure Databricks
- Get to grips with BIML and learn how it applies to SSIS and Azure Data Factory solutions

The Data Engineering Interview Study Guide | by SeattleDataGuy | Apr, 2021 | Better Programming
https://betterprogramming.pub/the-data-engineering-interview-study-guide-6f09420dd972

PowerBI-Advanced-Analytics-with-PowerBI-white-paper.pdf

DAX Guide
https://dax.guide/

KNIME Analytics Platform is the open source software for creating data science. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.

https://www.knime.com/knime-analytics-platform

KNIME Analytics Platform is the “killer app” for machine learning and statistics | by SJ Porter | Towards Data Science
https://towardsdatascience.com/knime-desktop-the-killer-app-for-machine-learning-cb07dbef1375

Data Engineering with Python, Paul Crickard, 2020

What you will learn

- Understand how data engineering supports data science workflows
- Discover how to extract data from files and databases and then clean, transform, and enrich it
- Configure processors for handling different file formats as well as both relational and NoSQL databases
- Find out how to implement a data pipeline and dashboard to visualize results
- Use staging and validation to check data before landing in the warehouse
- Build real-time pipelines with staging areas that perform validation and handle failures
- Get to grips with deploying pipelines in the production environment

End-to-End BI Project: Strategy, Steps, Processes, and Tools Part-01 | by Yemunn Soe | Geek Culture | May, 2021 | Medium
https://medium.com/geekculture/end-to-end-bi-project-strategy-steps-processes-and-tools-part-1-1f8c3f8cb00c