Change

Data aggregation is one of the roads we use to understand our data diversity. SQL "Selective Query Language" is the easiest way we use to do so. below is how to map our SQL syntax to Pig or Spark. SQL Structure: What to retrieve, stating which column we choose to display from my data structure SQL: Select student, age From mathClass Pig: namedMathClass = foreach mathClass generate (chararray) $0 as student:chararray, (int) $2 as age:int ; Spark: namedMathClass = mathClass.map( row => row(0), row(2) ) Whether this row is to be added in our data-set or not "Condition" SQL: where age > 10 Pig: greater_10 = Filter namedMathClass by age > 10 ; Spark: greater_10 = namedMathClass.filter( col => col(1) > 10 ) How to aggregate, we group similar data together  in one bag then apply our aggregate function on this bags  SQL: Select age, Count(student) From mathClass group by age Pig: groupAge = Group mathClass by age; Iterate_Age = For