Hive and Impala are tools for querying data stored in Hadoop, but they have key differences. Hive uses MapReduce to transform SQL queries into jobs and is better for long-running ETL processes due to fault tolerance. Impala is a massively parallel processing database that pushes processing directly to nodes, making it faster and more suitable for interactive queries from data analysts. The main differences are that Hive uses disk-based operations while Impala keeps data and calculations in memory, and Hive provides fault tolerance by restarting failed queries while Impala would need to restart from the beginning.

1. Hive Vs Impala
Omid Vahdaty, Big Data ninja

2. Differences of Hive VS. Impala
Hive Impala
Author Apache Cloudera/Apache
design Map reduce jobs MPP database
Use cases Hive which transforms SQL
queries into MapReduce or
Apache Spark jobs under the
covers, is great for long-
running ETL jobs (for which
fault tolerance is highly
desirable; for such jobs, you
don't want to have to re-do a long-
running query that failed after
several hours)
Impala is a MPP analytic database
on top of Hadoop and is largely
written in C++ for speed, pushes
data processing down to local
DataNodes, avoiding network
bottlenecks. enables low-
latency/interactive queries,
especially under multi-user
load. This makes Impala very
popular with data analysts who
need and expect an interactive
"BI" experience

3. Differences of Hive VS. Impala
Hive Impala
Read/write parallel Read in parallel, write on 1
virtual disk - may change.
Resource management Yarn 128GB per node.Yarn
supported.
SQL syntax HiveSQL ?
Performance Disk In memory, All heavy
calculations like group by,
conversions would be memory
based.
Querying May start in a delay (batch
jobs)
No delay
Query fault tolerance Will restart on failure. Start over in failure.

4. Differences of Hive VS. Impala
Hive Impala
Complex data types yes no
Anti pattern Interactive / ad hoc. ?