Very simple in just 2 steps.

Step 1: Extract new changes to users table in MySQL, as avro data files on DFS

// Command to extract incrementals using sqoop
bin/sqoop import \
  -Dmapreduce.job.user.classpath.first=true \
  --connect jdbc:mysql://localhost/users \
  --username root \
  --password ******* \
  --table users \
  --as-avrodatafile \
  --target-dir \ 
  s3:///tmp/sqoop/import-1/users

Step 2: Use your fav datasource to read extracted data and directly “upsert” the users table on DFS/Hive

// Spark Datasource
import org.apache.hudi.DataSourceWriteOptions._
// Use Spark datasource to read avro
val inputDataset = spark.read.avro("s3://tmp/sqoop/import-1/users/*");
     
// save it as a Hudi dataset
inputDataset.write.format("org.apache.hudi”)
  .option(HoodieWriteConfig.TABLE_NAME, "hoodie.users")
  .option(RECORDKEY_FIELD_OPT_KEY(), "userID")
  .option(PARTITIONPATH_FIELD_OPT_KEY(),"country")
  .option(PRECOMBINE_FIELD_OPT_KEY(), "last_mod")
  .option(OPERATION_OPT_KEY(), UPSERT_OPERATION_OPT_VAL())
  .mode(SaveMode.Append)
  .save("/path/on/dfs");

Alternatively, you can also use the Hudi DeltaStreamer tool with the DFSSource.