The invention discloses a 
mass data quality verification method based on Hadoop. The method comprises the following steps: formulating a 
data quality standard; for the DDL instruction, writing 
metadata information of the creation table into Hive; for the DQL statement, converting the 
SQL character string into an 
abstract syntax tree, performing 
syntax analysis on the 
abstract syntax tree, analyzing whether latest generated 
SQL semantics are wrong or not according to a 
data quality standard, and adding extension information; compiling the 
abstract syntax tree to generate a corresponding logic 
execution plan, optimizing the logic 
execution plan, converting the optimized logic 
execution plan into a physical plan, generating a MapReduce job, submitting the MapReduce job to the 
Yarn for execution, and finally returning an execution result; storing a returned execution result into the HDFS, and carrying out 
data visualization and abnormal data exporting, tracking and tracing. Therefore, thedata quality 
verification effects of abnormal 
data display, 
traceability, easy configuration and easy classification are achieved.