Having used Redshift considerably in the past and experienced significant pain with many aspects of it, I would stay away. There are other much better options, ideally Snowflake.
I read somewhere (a YC video or post I can't find now) that during a pivot, the problem you're working on shouldn't really change, but the solution might change completely.
I interpreted the superordinate goal as the higher level problem you want to solve and dedicate years/decades of your life towards. It might result in multiple startup attempts with entirely different products.
It seems that a superordinate goal which is something you truly believe is worthy is probably well-validated due to deep personal experience. This seems less likely to change easily.
I think one of the less discussed advantages of SQL and reasons why it might be so prevalent in the analytics world is that users don't really need to spend much time to set up their environment. I can send a query to a less technical person with my analysis for them to run. They can put the processed data into a spreadsheet and do whatever they want.
This is exactly my experience. I've found becoming proficient with Pandas to take much more time than other data manipulation libraries like dplyr or Pyspark dataframes. The Pandas way of doing things is just not memorable or intuitive.
Hi, I’m Bayan, author of the post. I would love to hear if you agree/disagree with the definition of a unicorn product analyst or you’ve met anyone who fits the mould. Keen to hear your thoughts.
