title: Apache Spark + Delta Lake concepts
body: <p>I have many doubts related to Spark + Delta.
  <a href="https://i.stack.imgur.com/DEXrN.png" rel="noreferrer"><img src="https://i.stack.imgur.com/DEXrN.png" alt="enter image description here"></a></p>
  
  <p>1) Databricks propose 3 layers (bronze, silver, gold), but in which layer is recommendable to use for Machine Learning and why? I suppose they propose to have the data clean and ready in the gold layer.</p>
  
  <p>2) If we <strong>abstract</strong> the concepts of these 3 layers, can we think the bronze layer as a Data Lake, the silver layer as databases, and the gold layer as a data warehouse? I mean in terms of functionality, . </p>
  
  <p>3) Delta architecture is a commercial term, or is an evolution of Kappa Architecture, or is a new trending architecture as Lambda and Kappa architecture? What are the differences between (Delta + Lambda Architecture) versus Kappa Architecture?</p>
  
  <p>4) In many cases Delta + Spark scale a lot more than most databases for usually much cheaper, and if we tune things right, we can get almost 2x faster queries results. I know is pretty complicated to compare the actual trending data warehouses versus the Feature/Agg Data Store, but I would like to know how can I make this comparison? </p>
  
  <p>5) I used to use Kafka, Kinesis, or Event Hub for streaming process, and my question is what kind of problems can happens if we replace these tools by a Delta Lake table (I already know that everything depends of many things, but I would like to have a general vision of that).</p>