Schema alignment is critical when integrating multiple datasets because it harmonizes different data structures by ensuring consistent field names and data types across datasets. For example, aligning fields like “Date of Purchase” with “Purchase Date” ensures data uniformity, and converting data types like text-based dates to standardized formats makes merging more effective. Schema alignment facilitates smoother data integration, making analysis more accurate and cohesive across disparate datasets. It is particularly essential in environments where data from various sources must be merged and analyzed as a whole. The other options are incorrect because: • Option 1 involves aggregation, which is about summarizing data rather than aligning field names or data types. • Option 2 (normalization) is useful for scaling but does not address naming or type consistency. • Option 4 is inefficient as it can lead to loss of potentially valuable data by discarding non-matching entries. • Option 5 (z-scores) is a transformation technique for numerical standardization, unrelated to resolving inconsistencies in data schema.
Four of the following five are alike in a certain way based on the above arrangement. Find which one does not belongs to the group?
Which of the following statements is/ are true regarding W?
Which of the following statement is true?
Which of the following combination is true?
Who among the following person lives in flat 2 of floor 4?
Four of the following five are alike in a certain way based on the given arrangement and thus form a group. Which is the one that does not belong to tha...
Who purchased bungalow in year 1993?
Who among the following live immediate below P?
Who among the following person goes immediately before I?
E attends which of the following exams?