2 Matching Annotations
  1. Nov 2018
    1. Unless you need to push the boundaries of what these technologies are capable of, you probably don’t need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, … – places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call “solutions”. Messes tend to necessitate specialization.
  2. Jul 2018
    1. We noticed that the people who use the data are usually not the same people who produce the data, and they often don’t know where to find the information about the data they try to use. Since the Schematizer already has the knowledge about all the schemas in the Data Pipeline, it becomes an excellent candidate to store information about the data. Meet our knowledge explorer, Watson. The Schematizer requires schema registrars to include documentation along with their schemas. The documentation then is extracted and stored in the Schematizer. To make the schema information and data documentation in the Schematizer accessible to all the teams at Yelp, we created Watson, a webapp that users across the company can use to explore this data. Watson is a visual frontend for the Schematizer and retrieves its information through a set of RESTful APIs exposed by the Schematizer.