Ask HN: What are some good resources on modern Data Engineering?

Data Engineering is a very wide field with an even wider variety of tools. What are some good resources that can shine some light on how to successfully build and run a modern Data Engineering operation at scale?

Specifically talking about practical techniques and preferably open-source software that can be composed to build a solution for the following: * Data Collection / Ingestion of streaming and batch snapshot data * Structuring a Data Lake * Creating and operating data pipelines * ETL or ELT? * Do you share source data between the warehouse tables and machine learning / streaming pipelines? * Data Warehouse * Machine Learning / Data Science / Spark * Data Visualization * ML Model Serving (and streaming updates) * Data Lineage * Am I forgetting anything?

I realise this is a very broad question. I know that there are good solutions (open source and commercial) for all of these if I simply Google, but integrating them well seems to be an art.

If you know of any books or resources or experts to follow, please share.