While working on some data analysis I saw one Spark SQL query was not getting me expected results. The table had some good amount of data, I was filtering on a value but some records were missing. So, I checked online and found that Spark SQL works differently compared to SQL Server, in this case while comparing 2 different datatypes columns or variables.
Read on to learn more about the issue. This is the downside of Feasel’s Law: just because both system interfaces are SQL doesn’t mean that they’re equivalent or that the assertions and assumptions you can make for one follow through to the next.