Apache Hive vs Apache Impala

The following is a comprehensive list of the differences between Apache Hive and Apache Impala. There were many differences, but the majority of them are no longer present as a result of the features added to Apache Impala, such as complex data types, and so on.

Apache HiveApache Impala
Not ideal for interactive computingIdeal for interactive computing
MapReduce / Tez / Spark EnginesMassively Parallel Processing (MPP) SQL Engines
Hive converts queries into MapReduce jobs for execution.Impala responds quickly because of its massively parallel processing.
Every hive query has this problem of “cold start”Since daemon processes are started at boot time, it avoids startup overhead.
Use familiar built-in user-defined functions (UFFDs) to manipulate the dataCan easily read metadata using ODBC driver and SQL syntax from Apache Hive
Used for analysis processing and visualization.Used by programmers for running queries on HDFS and Apache HBase
It is a data warehouse infrastructure built over Hadoop platform.It doesn’t require data to be moved or transformed
By default, Hive stores metadata in an embedded Apache Derby database.Uses metadata, ODBC driver, and SQL syntax from Apache Hive.
Hive latencyLow latency
Since Hive is fault-tolerant, the query’s output will be delivered even if a data node fails during execution.Impala restarts from the beginning when a data node fails during the query execution.
Ideal for long-running ETL jobsNot ideal for long-running ETL jobs
Disk-based processingMemory-bound (In-memory processing)
Not ideal for PowerBI/BI Tools Interactive Dashboards.Ideal for PowerBI/BI Tools Interactive Dashboards.

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