Impala is developed and shipped by Cloudera. If you want to insert your data record by record, or want to do interactive queries in Impala then Kudu is likely the best choice. AtScale recently performed benchmark tests on the Hadoop engines Spark, Impala, Hive, and Presto. Impala queries are not translated to MapReduce jobs, instead, they are executed natively. The final comparison I wanted to evaluate was In-Database performance of using Hive (MapReduce & YARN), Impala (daemon processes), and Spark. Comparison between Hive and Impala or Spark or Drill sometimes sounds inappropriate to me. Hive gives a SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Spark uses RDD (Resilient Distributed Datasets) to keep data in memory, reducing I/O, and therefore providing faster analysis than traditional MapReduce jobs. So, it would be safe to say that Impala is not going to replace Spark soon or vice versa. Hive has its special ability of frequent switching between engines and so is an efficient tool for querying large data sets. Now it boils down to whether you want to store the data in Hive or in Kudu, as Spark can work with both of these. Apache Hive and Spark are both top level Apache projects. It is an advanced analytics language that would allow you to leverage your familiarity with SQL (without writing … Get a thorough walkthrough of the different approaches to selecting, buying, and implementing a semantic layer for your analytics stack, and a checklist you can refer to as you start your search. Today AtScale released its Q4 benchmark results for the major big data SQL engines: Spark, Impala, Hive/Tez, and Presto.. Apache Hive: It is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. The Complete Buyer's Guide for a Semantic Layer. Hive can now be accessed and processed using spark SQL jobs. For this Drill is not supported, but Hive tables and Kudu are supported by Cloudera. Spark, Hive, Impala and Presto are SQL based engines. Hive, Impala and Spark SQL all fit into the SQL-on-Hadoop category. It was built for offline batch processing kinda stuff. So answer to your question is "NO" spark will not replace hive or impala. It’s just that Spark SQL can be seen to be a developer-friendly Spark based API which is aimed to make the programming easier. Find out the results, and discover which option might … Spark which has been proven much faster than map reduce eventually had to support hive. Hive Vs Mapreduce - MapReduce programs are parallel in nature, thus are very useful for performing large-scale data analysis using multiple machines in the cluster. The goals behind developing Hive and these tools were different. Big data face-off: Spark vs. Impala vs. Hive vs. Presto. We cannot say that Apache Spark SQL is the replacement for Hive or vice-versa. 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