Unhappy customers: caveat emptor (Score 1)

I think many of the 'unhappy customers' the article refers to are companies where somebody who didn't quite understand the technology pushed hadoop as a replacement for (expensive) proprietary software like Oracle, to be then sorely disappointed especially on interactive performance.
I've been working with hadoop since 2007 and have successfully deployed for multiple clients. First of all, you really want to see if the use case makes sense, sometimes you're just better off with a RDBMs like mysql. Some companies just jump on the 'NoSQL' bandwageon to find out almost immediately that they, oh well, actually do need SQL.
Hadoop is based on some Google technologies (GFS, etc) that were designed to process immutable, append only crawler logs, for the search engine. So anything that requires record-level CRUD is off the table in vanilla hadoop. Other systems (like kudu or hbase) try to address that, but even there, it depends on your use case. These technologies are also not that easy to operate, especially when you stack them on top of each other. That's why there's a flurry of companies (like - guess what - snowflake, whose CEO is the author of the article) that offer Data Warehouse as a Service on top of hadoop.
I think the article is meant to scare people into buying their service...and it may be actually worth it if you don't have the skills/manpower to run the hadoop stack yourself. If only a few people access data, it's probably worthwhile, but in larger organizations, hadoop is merely the engine that does the ETL heavy lifting but you have other systems where the the data is queried. Note that for querying, there are several patterns: looking at long-term trends, looking at the most recent data, ad hoc querying, machine learning... each one may need its own specialize query engine.
Finally, the comparison with kafka is just insane and sounds like another sales pitch. Kafka is a whole different beast, it does streaming, but querying??