@Bala Vignesh N V Tez is a DAG (Directed acyclic graph) architecture. A typical Map reduce job has following steps: 1. Read data from file -->one disk access 2. Run mappers 3. Write map output --> second disk access 4. Run shuffle and sort --> read map output, third disk access 5. write shuffle and sort --> write sorted data for reducers --> fourth disk access 6. Run reducers which reads sorted data --> fifth disk output 7. Write reducers output -->sixth disk access Tez works very similar to Spark (Tez was created by Hortonworks well before Spark): 1. Execute the plan but no need to read data from disk. 2. Once ready to do some calculations (similar to actions in spark), get the data from disk and perform all steps and produce output. Only one read and one write. Notice the efficiency introduced by not going to disk multiple times. Intermediate results are stored in memory (not written to disks). On top of that there is vectorization (process batch of rows instead of one row at a time). All this adds to efficiencies in query time. Now to answer your question on why Tez queries fail but executed in MR. This should not happen. Possible bugs or sometimes people working with Hive have used MapReduce for a while and know how to make things work but not as familiar with Tez. I think, Tez queries should not fail any more than Map Reduce. I highly recommend skimming quickly over following slides, specially starting from slide 7. http://www.slideshare.net/Hadoop_Summit/w-235phall1pandey ... View more

@Ankur Kapoor Also attaching the template of my workflow.hcc.xml ... View more