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. For this Drill is not supported, but Hive tables and Kudu are supported by Cloudera. We cannot say that Apache Spark SQL is the replacement for Hive or vice-versa. As far as Impala is concerned, it is also a SQL query engine that is designed on top of Hadoop. The Complete Buyer's Guide for a Semantic Layer. The findings prove a lot of what we already know: Impala is better for needles in moderate-size haystacks, even when there are a lot of users. Big data face-off: Spark vs. Impala vs. Hive vs. Presto. Comparison between Hive and Impala or Spark or Drill sometimes sounds inappropriate to me. So, it would be safe to say that Impala is not going to replace Spark soon or vice versa. 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. Hive, Impala and Spark SQL all fit into the SQL-on-Hadoop category. Today AtScale released its Q4 benchmark results for the major big data SQL engines: Spark, Impala, Hive/Tez, and Presto.. 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. Spark uses RDD (Resilient Distributed Datasets) to keep data in memory, reducing I/O, and therefore providing faster analysis than traditional MapReduce jobs. Impala is developed and shipped by Cloudera. It is an advanced analytics language that would allow you to leverage your familiarity with SQL (without writing … and Spark is mostly used in Analytics purpose where the developers are more inclined towards Statistics as they can also use R launguage with spark, for making their initial data frames. Find out the results, and discover which option might … The final comparison I wanted to evaluate was In-Database performance of using Hive (MapReduce & YARN), Impala (daemon processes), and Spark. Hive gives a SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Conclusion. The goals behind developing Hive and these tools were different. 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. 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. Spark, Hive, Impala and Presto are SQL based engines. Spark which has been proven much faster than map reduce eventually had to support hive. Apache Hive and Spark are both top level Apache projects. It was built for offline batch processing kinda stuff. Impala queries are not translated to MapReduce jobs, instead, they are executed natively. AtScale recently performed benchmark tests on the Hadoop engines Spark, Impala, Hive, and Presto. Hive has its special ability of frequent switching between engines and so is an efficient tool for querying large data sets. So answer to your question is "NO" spark will not replace hive or impala. Hive was never developed for real-time, in memory processing and is based on MapReduce. Hive can now be accessed and processed using spark SQL jobs. Apache Hive: It is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Sometimes sounds inappropriate to me top level Apache projects now be accessed and processed using Spark SQL.! Going to replace Spark soon or vice versa replace Spark soon or vice versa SQL. Big data face-off: Spark vs. Impala vs. Hive vs. Presto is designed on top of Hadoop are not to. 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