Because of this connectivity, Presto is a drop-in replacement for organizations using Hive today.May I know the partitions of the hive tables before presto has executed the sql? I used pyhive to connect hive to use Presto.Thus this is resolved by creating partitions in. So, it becomes inefficient to run MapReduce jobs over a large table. When we submit a SQL query, Hive read the entire data-set. Apache Hive converts the SQL queries into MapReduce jobs and then submits it to the Hadoop cluster. The Hive was introduced to lower down this burden of data querying.Mac OS X or Linux Java 8 Update 151 or higher (8u151+), 64-bit. See the User Manual for deployment instructions and end user documentation. Presto is a distributed SQL query engine for big data. This offering is maintained by Starburst, the leading contributors to Presto. Architected for separation of storage and compute, Presto is cloud native and can query data in Azure data storages, Hadoop, SQL and NoSQL databases, and other data sources. Presto is a fast and scalable open source SQL engine.Unlike Hive Presto doesn't use MapReduce. Presto is an alternative to tools that query HDFS using pipelines of MapReduce jobs - such as Hive. Before we start, let's take a quick look at its main architecture principles. Running Presto in Big Data Environment.