I think it's marketing nonsense. Databricks in particular has good "proximity to data" IMO, in that once you've figured out how to use it with your data sources, you can just fire up a web browser and connect to them, and connecting those data sources to your code is easy.
The problem with seeing Spark as your one tool for everything is that's only true if it's trivial to integrate your code with Spark. Viz tools like Plotly/Bokeh don't integrate well with Databricks' notebook, Deep Learning tools are not really supported yet unless you're running your own clusters and running special libraries to wire thing together.
I think Spark is a good workhorse for big data; it can do repetitive things well at large scale, it's less good when you want to use more niche tools since most of the Data Science community is not focussed on Spark. PySpark exists and will probably be good enough, but only if your data fits into memory in a single machine anyway.
And if you're not dealing with big data, Spark is overkill. It's usually simpler to just get a box with more RAM.
The problem with seeing Spark as your one tool for everything is that's only true if it's trivial to integrate your code with Spark. Viz tools like Plotly/Bokeh don't integrate well with Databricks' notebook, Deep Learning tools are not really supported yet unless you're running your own clusters and running special libraries to wire thing together.
I think Spark is a good workhorse for big data; it can do repetitive things well at large scale, it's less good when you want to use more niche tools since most of the Data Science community is not focussed on Spark. PySpark exists and will probably be good enough, but only if your data fits into memory in a single machine anyway.
And if you're not dealing with big data, Spark is overkill. It's usually simpler to just get a box with more RAM.