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 Hive | Apache Impala |
| Not ideal for interactive computing | Ideal for interactive computing |
| MapReduce / Tez / Spark Engines | Massively 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 data | Can 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 latency | Low 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 jobs | Not ideal for long-running ETL jobs |
| Disk-based processing | Memory-bound (In-memory processing) |
| Not ideal for PowerBI/BI Tools Interactive Dashboards. | Ideal for PowerBI/BI Tools Interactive Dashboards. |
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Big Data and SQL 
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