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from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/features/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>","id":"seo-faq-pairs#what-is-presto","customSort":"1"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#presto","name":"presto","namespaceId":"seo-faq-pairs#faq-collections","description":"<p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/details/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>","metadata":{}}]}},{"fields":{"faqQuestion":"What is the history of Presto?","faqAnswer":"<p>Presto started as a project at <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, to run interactive analytic queries against a 300PB data warehouse, built with large Hadoop/HDFS-based clusters. Prior to building Presto, Facebook used Apache Hive, which it created and rolled out in 2008, to bring the familiarity of the SQL syntax to the Hadoop ecosystem. Hive had a significant impact on the Hadoop ecosystem for simplifying complex Java MapReduce jobs into SQL-like queries, while being able to execute jobs at high scale. However, it wasn’t optimized for fast performance needed in interactive queries.</p> \n<p>In 2012, the Facebook Data Infrastructure group built Presto, an interactive query system that could operate quickly at petabyte scale. It was rolled out company-wide in spring, 2013. In November, 2013, Facebook <a href=\"https://github.com/prestodb/presto\">open sourced</a> Presto under the Apache Software License, and made it available for anyone to download on Github. Today, Presto has become a <a href=\"https://github.com/prestodb/presto/wiki/Presto-Users\">popular choice</a> for doing interactive queries on Hadoop, and has a lot of <a href=\"https://github.com/prestodb/presto/graphs/contributors\">contributions</a> from Facebook, and other organizations. Facebook’s implementation of Presto is used by over a thousand employees, who run more than 30,000 queries, processing one petabyte of data daily.</p>","id":"seo-faq-pairs#what-is-the-history-of-presto","customSort":"2"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#presto","name":"presto","namespaceId":"seo-faq-pairs#faq-collections","description":"<p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/details/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>","metadata":{}}]}},{"fields":{"faqQuestion":"How does Presto work?","faqAnswer":"<p>Presto is a distributed system that runs on Hadoop, and uses an architecture similar to a classic massively parallel processing (MPP) database management system. It has one coordinator node working in synch with multiple worker nodes. Users submit their SQL query to the coordinator which uses a custom query and execution engine to parse, plan, and schedule a distributed query plan across the worker nodes. It is designed to support standard ANSI SQL semantics, including complex queries, aggregations, joins, left/right outer joins, sub-queries, window functions, distinct counts, and approximate percentiles.</p> \n<p>After the query is compiled, Presto processes the request into multiple stages across the worker nodes. All processing is in-memory, and pipelined across the network between stages, to avoid any unnecessary I/O overhead. Adding more worker nodes allows for more parallelism, and faster processing.</p> \n<p>To make Presto extensible to any data source, it was designed with storage abstraction to make it easy to build pluggable connectors. Because of this, Presto has a lot of connectors, including to non-relational sources like the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/features/hbase/\">HBase</a>, and relational sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata. The data is queried where it is stored, without the need to move it into a separate analytics system.  </p>","id":"seo-faq-pairs#how-does-presto-work","customSort":"3"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#presto","name":"presto","namespaceId":"seo-faq-pairs#faq-collections","description":"<p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/details/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>","metadata":{}}]}},{"fields":{"faqQuestion":"What are the differences between Presto and Hadoop?","faqAnswer":"<p>Presto is an open source, distributed SQL query engine designed for fast, interactive queries on data in HDFS, and others. Unlike Hadoop/HDFS, it does not have its own storage system. Thus, Presto is complimentary to Hadoop, with organizations adopting both to solve a broader business challenge. Presto can be installed with any implementation of Hadoop, and is packaged in the <a href=\"https://aws.amazon.com/emr/\">Amazon EMR</a> Hadoop distribution.</p>","id":"seo-faq-pairs#what-are-the-differences-between-presto-and-hadoop","customSort":"4"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#presto","name":"presto","namespaceId":"seo-faq-pairs#faq-collections","description":"<p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/details/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>","metadata":{}}]}},{"fields":{"faqQuestion":"Who uses Presto?","faqAnswer":"<p>Presto is used in production at very large scale at many well-known organizations. You’ll find it used at <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>, and <a href=\"https://github.com/prestodb/presto/wiki/Presto-Users\">many more</a>. Facebook’s implementation of Presto is used by over a thousand employees, who run more than 30,000 queries, processing one petabyte of data daily. On average, Netflix runs around 3,500 queries per day on its Presto clusters. Airbnb built and open sourced, <a href=\"http://airbnb.io/airpal/\">Airpal</a>, a web-based query execution tool that works on top of Presto. The broader Presto community can be found on this <a href=\"https://groups.google.com/forum/#!forum/presto-users\">forum</a> and on the Presto page on <a href=\"https://www.facebook.com/prestodb\">Facebook</a>.</p>","id":"seo-faq-pairs#who-uses-presto","customSort":"5"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#presto","name":"presto","namespaceId":"seo-faq-pairs#faq-collections","description":"<p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/details/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>","metadata":{}}]}},{"fields":{"faqQuestion":"How can you deploy Presto in the cloud?","faqAnswer":"<p>Presto is an ideal workload in the cloud, because the cloud provides performance, scalability, reliability, availability, and massive economies of scale. You can launch a Presto cluster in minutes. You don’t need to worry about node provisioning, cluster setup, Presto configuration, or cluster tuning.</p>","id":"seo-faq-pairs#deploying-presto-in-cloud","customSort":"6"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#presto","name":"presto","namespaceId":"seo-faq-pairs#faq-collections","description":"<p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/details/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>","metadata":{}}]}},{"fields":{"faqQuestion":"How can AWS build your Presto Implementation in the cloud?","faqAnswer":"<p><a href=\"https://aws.amazon.com/emr/\">Amazon EMR</a> and <a href=\"https://aws.amazon.com/athena/\">Amazon Athena</a> are the best places to deploy Presto in the cloud, because it does the integration, and testing rigor of Presto for you, with the scale, simplicity, and cost effectiveness of AWS. With Amazon EMR, you can launch Presto clusters in minutes without needing to do node provisioning, cluster setup, Presto configuration, or cluster tuning. EMR enables you to provision one, hundreds, or thousands of compute instances in minutes. Amazon Athena lets you deploy Presto using the AWS Serverless platform, with no servers, virtual machines, or clusters to setup, manage, or tune. Simply point to your data at Amazon S3, define the schema, and start querying using the built-in query editor, or with your existing Business Intelligence (BI) tools. Athena automatically parallelizes your query, and dynamically scales resources for queries to run quickly. You pay only for the queries that you run.</p>","id":"seo-faq-pairs#building-your-presto-implementation-in-the-cloud","customSort":"7"},"metadata":{"tags":[{"id":"seo-faq-pairs#faq-collections#presto","name":"presto","namespaceId":"seo-faq-pairs#faq-collections","description":"<p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/details/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>","metadata":{}}]}}]},"metadata":{"auth":{},"pagination":{"empty":false,"present":true},"testAttributes":{}},"context":{"page":{"locale":null,"site":null,"pageUrl":"https://aws.amazon.com/what-is/presto/","targetName":null,"pageSlotId":null,"organizationId":null,"availableLocales":null},"environment":{"stage":"prod","region":"us-east-1"},"sdkVersion":"1.0.115"},"refMap":{"manifest.js":"3dea65b485","rt-faq.rtl.css":"75bc12ff4b","rt-faq.css":"b00bda11a1","rt-faq.css.js":"0af1d62724","rt-faq.js":"da177bdd5f","rt-faq.rtl.css.js":"a89cd83194"},"settings":{"templateMappings":{"question":"faqQuestion","answer":"faqAnswer"}}}</script> <div data-eb-tpl-root="" data-reactroot=""> <div class="eb-faq"> <script type="application/ld+json">{"@context":"https://schema.org","@type":"FAQPage","mainEntity":[[{"@type":"Question","name":"What is Presto or PrestoDB?","acceptedAnswer":{"@type":"Answer","text":"<p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/features/hbase/\">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> \n<p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, and <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>.</p>"}},{"@type":"Question","name":"What is the history of Presto?","acceptedAnswer":{"@type":"Answer","text":"<p>Presto started as a project at <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, to run interactive analytic queries against a 300PB data warehouse, built with large Hadoop/HDFS-based clusters. Prior to building Presto, Facebook used Apache Hive, which it created and rolled out in 2008, to bring the familiarity of the SQL syntax to the Hadoop ecosystem. Hive had a significant impact on the Hadoop ecosystem for simplifying complex Java MapReduce jobs into SQL-like queries, while being able to execute jobs at high scale. However, it wasn’t optimized for fast performance needed in interactive queries.</p> \n<p>In 2012, the Facebook Data Infrastructure group built Presto, an interactive query system that could operate quickly at petabyte scale. It was rolled out company-wide in spring, 2013. In November, 2013, Facebook <a href=\"https://github.com/prestodb/presto\">open sourced</a> Presto under the Apache Software License, and made it available for anyone to download on Github. Today, Presto has become a <a href=\"https://github.com/prestodb/presto/wiki/Presto-Users\">popular choice</a> for doing interactive queries on Hadoop, and has a lot of <a href=\"https://github.com/prestodb/presto/graphs/contributors\">contributions</a> from Facebook, and other organizations. Facebook’s implementation of Presto is used by over a thousand employees, who run more than 30,000 queries, processing one petabyte of data daily.</p>"}},{"@type":"Question","name":"How does Presto work?","acceptedAnswer":{"@type":"Answer","text":"<p>Presto is a distributed system that runs on Hadoop, and uses an architecture similar to a classic massively parallel processing (MPP) database management system. It has one coordinator node working in synch with multiple worker nodes. Users submit their SQL query to the coordinator which uses a custom query and execution engine to parse, plan, and schedule a distributed query plan across the worker nodes. It is designed to support standard ANSI SQL semantics, including complex queries, aggregations, joins, left/right outer joins, sub-queries, window functions, distinct counts, and approximate percentiles.</p> \n<p>After the query is compiled, Presto processes the request into multiple stages across the worker nodes. All processing is in-memory, and pipelined across the network between stages, to avoid any unnecessary I/O overhead. Adding more worker nodes allows for more parallelism, and faster processing.</p> \n<p>To make Presto extensible to any data source, it was designed with storage abstraction to make it easy to build pluggable connectors. Because of this, Presto has a lot of connectors, including to non-relational sources like the Hadoop Distributed File System (HDFS), <a href=\"https://aws.amazon.com/s3/\">Amazon S3</a>, Cassandra, MongoDB, and <a href=\"https://aws.amazon.com/emr/features/hbase/\">HBase</a>, and relational sources such as MySQL, PostgreSQL, <a href=\"https://aws.amazon.com/redshift/\">Amazon Redshift</a>, Microsoft SQL Server, and Teradata. The data is queried where it is stored, without the need to move it into a separate analytics system.  </p>"}},{"@type":"Question","name":"What are the differences between Presto and Hadoop?","acceptedAnswer":{"@type":"Answer","text":"<p>Presto is an open source, distributed SQL query engine designed for fast, interactive queries on data in HDFS, and others. Unlike Hadoop/HDFS, it does not have its own storage system. Thus, Presto is complimentary to Hadoop, with organizations adopting both to solve a broader business challenge. Presto can be installed with any implementation of Hadoop, and is packaged in the <a href=\"https://aws.amazon.com/emr/\">Amazon EMR</a> Hadoop distribution.</p>"}},{"@type":"Question","name":"Who uses Presto?","acceptedAnswer":{"@type":"Answer","text":"<p>Presto is used in production at very large scale at many well-known organizations. You’ll find it used at <a href=\"https://code.facebook.com/projects/552007124892407/presto/\">Facebook</a>, <a href=\"https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f\">Airbnb</a>, <a href=\"https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4\">Netflix</a>, <a href=\"https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s\">Atlassian</a>, <a href=\"https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s\">Nasdaq</a>, and <a href=\"https://github.com/prestodb/presto/wiki/Presto-Users\">many more</a>. Facebook’s implementation of Presto is used by over a thousand employees, who run more than 30,000 queries, processing one petabyte of data daily. On average, Netflix runs around 3,500 queries per day on its Presto clusters. Airbnb built and open sourced, <a href=\"http://airbnb.io/airpal/\">Airpal</a>, a web-based query execution tool that works on top of Presto. The broader Presto community can be found on this <a href=\"https://groups.google.com/forum/#!forum/presto-users\">forum</a> and on the Presto page on <a href=\"https://www.facebook.com/prestodb\">Facebook</a>.</p>"}},{"@type":"Question","name":"How can you deploy Presto in the cloud?","acceptedAnswer":{"@type":"Answer","text":"<p>Presto is an ideal workload in the cloud, because the cloud provides performance, scalability, reliability, availability, and massive economies of scale. You can launch a Presto cluster in minutes. You don’t need to worry about node provisioning, cluster setup, Presto configuration, or cluster tuning.</p>"}},{"@type":"Question","name":"How can AWS build your Presto Implementation in the cloud?","acceptedAnswer":{"@type":"Answer","text":"<p><a href=\"https://aws.amazon.com/emr/\">Amazon EMR</a> and <a href=\"https://aws.amazon.com/athena/\">Amazon Athena</a> are the best places to deploy Presto in the cloud, because it does the integration, and testing rigor of Presto for you, with the scale, simplicity, and cost effectiveness of AWS. With Amazon EMR, you can launch Presto clusters in minutes without needing to do node provisioning, cluster setup, Presto configuration, or cluster tuning. EMR enables you to provision one, hundreds, or thousands of compute instances in minutes. Amazon Athena lets you deploy Presto using the AWS Serverless platform, with no servers, virtual machines, or clusters to setup, manage, or tune. Simply point to your data at Amazon S3, define the schema, and start querying using the built-in query editor, or with your existing Business Intelligence (BI) tools. Athena automatically parallelizes your query, and dynamically scales resources for queries to run quickly. You pay only for the queries that you run.</p>"}}]]}</script> <div class="eb-sticky-sidebar"> <div class="eb-sidebar-wrapper"> <div class="eb-sidebar-content"> <span data-eb-item-id="seo-faq-pairs#what-is-presto"><a class="eb-sidebar-link lb-txt-bold lb-txt-none lb-txt-16 lb-txt eb-active" href="#seo-faq-pairs#what-is-presto">What is Presto or PrestoDB?</a></span> <span data-eb-item-id="seo-faq-pairs#what-is-the-history-of-presto"><a class="eb-sidebar-link lb-txt-bold lb-txt-none lb-txt-16 lb-txt" href="#seo-faq-pairs#what-is-the-history-of-presto">What is the history of Presto?</a></span> <span data-eb-item-id="seo-faq-pairs#how-does-presto-work"><a class="eb-sidebar-link lb-txt-bold lb-txt-none lb-txt-16 lb-txt" href="#seo-faq-pairs#how-does-presto-work">How does Presto work?</a></span> <span data-eb-item-id="seo-faq-pairs#what-are-the-differences-between-presto-and-hadoop"><a class="eb-sidebar-link lb-txt-bold lb-txt-none lb-txt-16 lb-txt" href="#seo-faq-pairs#what-are-the-differences-between-presto-and-hadoop">What are the differences between Presto and Hadoop?</a></span> <span data-eb-item-id="seo-faq-pairs#who-uses-presto"><a class="eb-sidebar-link lb-txt-bold lb-txt-none lb-txt-16 lb-txt" href="#seo-faq-pairs#who-uses-presto">Who uses Presto?</a></span> <span data-eb-item-id="seo-faq-pairs#deploying-presto-in-cloud"><a class="eb-sidebar-link lb-txt-bold lb-txt-none lb-txt-16 lb-txt" href="#seo-faq-pairs#deploying-presto-in-cloud">How can you deploy Presto in the cloud?</a></span> <span data-eb-item-id="seo-faq-pairs#building-your-presto-implementation-in-the-cloud"><a class="eb-sidebar-link lb-txt-bold lb-txt-none lb-txt-16 lb-txt" href="#seo-faq-pairs#building-your-presto-implementation-in-the-cloud">How can AWS build your Presto Implementation in the cloud?</a></span> </div> </div> </div> <div class="eb-faq-content"> <div class="lb-none-v-margin lb-grid lb-small-pad lb-grid" data-eb-item-id="seo-faq-pairs#what-is-presto"> <div class="lb-row lb-row-max-large lb-snap eb-active"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 class="lb-txt-bold lb-txt-none lb-txt-28 lb-h2 lb-title" id="seo-faq-pairs#what-is-presto">What is Presto or PrestoDB?</h2> <div class="lb-txt-14 lb-rtxt"> <p>Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), <a href="https://aws.amazon.com/s3/">Amazon S3</a>, Cassandra, MongoDB, and <a href="https://aws.amazon.com/emr/features/hbase/">HBase</a>, and relational data sources such as MySQL, PostgreSQL, <a href="https://aws.amazon.com/redshift/">Amazon Redshift</a>, Microsoft SQL Server, and Teradata.</p> <p>Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like <a href="https://code.facebook.com/projects/552007124892407/presto/">Facebook</a>, <a href="https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f">Airbnb</a>, <a href="https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4">Netflix</a>, <a href="https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s">Atlassian</a>, and <a href="https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s">Nasdaq</a>.</p> </div> </div> </div> </div> <div class="lb-none-v-margin lb-grid lb-small-pad eb-bg-dark" data-eb-item-id="seo-faq-pairs#what-is-the-history-of-presto"> <div class="lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 class="lb-txt-bold lb-txt-none lb-txt-28 lb-h2 lb-title" id="seo-faq-pairs#what-is-the-history-of-presto">What is the history of Presto?</h2> <div class="lb-txt-14 lb-rtxt"> <p>Presto started as a project at <a href="https://code.facebook.com/projects/552007124892407/presto/">Facebook</a>, to run interactive analytic queries against a 300PB data warehouse, built with large Hadoop/HDFS-based clusters. Prior to building Presto, Facebook used Apache Hive, which it created and rolled out in 2008, to bring the familiarity of the SQL syntax to the Hadoop ecosystem. Hive had a significant impact on the Hadoop ecosystem for simplifying complex Java MapReduce jobs into SQL-like queries, while being able to execute jobs at high scale. However, it wasn’t optimized for fast performance needed in interactive queries.</p> <p>In 2012, the Facebook Data Infrastructure group built Presto, an interactive query system that could operate quickly at petabyte scale. It was rolled out company-wide in spring, 2013. In November, 2013, Facebook <a href="https://github.com/prestodb/presto">open sourced</a> Presto under the Apache Software License, and made it available for anyone to download on Github. Today, Presto has become a <a href="https://github.com/prestodb/presto/wiki/Presto-Users">popular choice</a> for doing interactive queries on Hadoop, and has a lot of <a href="https://github.com/prestodb/presto/graphs/contributors">contributions</a> from Facebook, and other organizations. Facebook’s implementation of Presto is used by over a thousand employees, who run more than 30,000 queries, processing one petabyte of data daily.</p> </div> </div> </div> </div> <div class="lb-none-v-margin lb-grid lb-small-pad lb-grid" data-eb-item-id="seo-faq-pairs#how-does-presto-work"> <div class="lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 class="lb-txt-bold lb-txt-none lb-txt-28 lb-h2 lb-title" id="seo-faq-pairs#how-does-presto-work">How does Presto work?</h2> <div class="lb-txt-14 lb-rtxt"> <p>Presto is a distributed system that runs on Hadoop, and uses an architecture similar to a classic massively parallel processing (MPP) database management system. It has one coordinator node working in synch with multiple worker nodes. Users submit their SQL query to the coordinator which uses a custom query and execution engine to parse, plan, and schedule a distributed query plan across the worker nodes. It is designed to support standard ANSI SQL semantics, including complex queries, aggregations, joins, left/right outer joins, sub-queries, window functions, distinct counts, and approximate percentiles.</p> <p>After the query is compiled, Presto processes the request into multiple stages across the worker nodes. All processing is in-memory, and pipelined across the network between stages, to avoid any unnecessary I/O overhead. Adding more worker nodes allows for more parallelism, and faster processing.</p> <p>To make Presto extensible to any data source, it was designed with storage abstraction to make it easy to build pluggable connectors. Because of this, Presto has a lot of connectors, including to non-relational sources like the Hadoop Distributed File System (HDFS), <a href="https://aws.amazon.com/s3/">Amazon S3</a>, Cassandra, MongoDB, and <a href="https://aws.amazon.com/emr/features/hbase/">HBase</a>, and relational sources such as MySQL, PostgreSQL, <a href="https://aws.amazon.com/redshift/">Amazon Redshift</a>, Microsoft SQL Server, and Teradata. The data is queried where it is stored, without the need to move it into a separate analytics system.  </p> </div> </div> </div> </div> <div class="lb-none-v-margin lb-grid lb-small-pad eb-bg-dark" data-eb-item-id="seo-faq-pairs#what-are-the-differences-between-presto-and-hadoop"> <div class="lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 class="lb-txt-bold lb-txt-none lb-txt-28 lb-h2 lb-title" id="seo-faq-pairs#what-are-the-differences-between-presto-and-hadoop">What are the differences between Presto and Hadoop?</h2> <div class="lb-txt-14 lb-rtxt"> <p>Presto is an open source, distributed SQL query engine designed for fast, interactive queries on data in HDFS, and others. Unlike Hadoop/HDFS, it does not have its own storage system. Thus, Presto is complimentary to Hadoop, with organizations adopting both to solve a broader business challenge. Presto can be installed with any implementation of Hadoop, and is packaged in the <a href="https://aws.amazon.com/emr/">Amazon EMR</a> Hadoop distribution.</p> </div> </div> </div> </div> <div class="lb-none-v-margin lb-grid lb-small-pad lb-grid" data-eb-item-id="seo-faq-pairs#who-uses-presto"> <div class="lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 class="lb-txt-bold lb-txt-none lb-txt-28 lb-h2 lb-title" id="seo-faq-pairs#who-uses-presto">Who uses Presto?</h2> <div class="lb-txt-14 lb-rtxt"> <p>Presto is used in production at very large scale at many well-known organizations. You’ll find it used at <a href="https://code.facebook.com/projects/552007124892407/presto/">Facebook</a>, <a href="https://medium.com/airbnb-engineering/airpal-a-web-based-query-execution-tool-for-data-analysis-33c43265ed1f">Airbnb</a>, <a href="https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4">Netflix</a>, <a href="https://www.youtube.com/watch?v=0vdW1ORLWyk&feature=youtu.be&t=20m58s">Atlassian</a>, <a href="https://www.youtube.com/watch?v=LuHxnOQarXU&feature=youtu.be&t=25m13s">Nasdaq</a>, and <a href="https://github.com/prestodb/presto/wiki/Presto-Users">many more</a>. Facebook’s implementation of Presto is used by over a thousand employees, who run more than 30,000 queries, processing one petabyte of data daily. On average, Netflix runs around 3,500 queries per day on its Presto clusters. Airbnb built and open sourced, <a href="https://airbnb.io/airpal/">Airpal</a>, a web-based query execution tool that works on top of Presto. The broader Presto community can be found on this <a href="https://groups.google.com/forum/#!forum/presto-users">forum</a> and on the Presto page on <a href="https://www.facebook.com/prestodb">Facebook</a>.</p> </div> </div> </div> </div> <div class="lb-none-v-margin lb-grid lb-small-pad eb-bg-dark" data-eb-item-id="seo-faq-pairs#deploying-presto-in-cloud"> <div class="lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 class="lb-txt-bold lb-txt-none lb-txt-28 lb-h2 lb-title" id="seo-faq-pairs#deploying-presto-in-cloud">How can you deploy Presto in the cloud?</h2> <div class="lb-txt-14 lb-rtxt"> <p>Presto is an ideal workload in the cloud, because the cloud provides performance, scalability, reliability, availability, and massive economies of scale. You can launch a Presto cluster in minutes. You don’t need to worry about node provisioning, cluster setup, Presto configuration, or cluster tuning.</p> </div> </div> </div> </div> <div class="lb-none-v-margin lb-grid lb-small-pad lb-grid" data-eb-item-id="seo-faq-pairs#building-your-presto-implementation-in-the-cloud"> <div class="lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 class="lb-txt-bold lb-txt-none lb-txt-28 lb-h2 lb-title" id="seo-faq-pairs#building-your-presto-implementation-in-the-cloud">How can AWS build your Presto Implementation in the cloud?</h2> <div class="lb-txt-14 lb-rtxt"> <p><a href="https://aws.amazon.com/emr/">Amazon EMR</a> and <a href="https://aws.amazon.com/athena/">Amazon Athena</a> are the best places to deploy Presto in the cloud, because it does the integration, and testing rigor of Presto for you, with the scale, simplicity, and cost effectiveness of AWS. With Amazon EMR, you can launch Presto clusters in minutes without needing to do node provisioning, cluster setup, Presto configuration, or cluster tuning. EMR enables you to provision one, hundreds, or thousands of compute instances in minutes. Amazon Athena lets you deploy Presto using the AWS Serverless platform, with no servers, virtual machines, or clusters to setup, manage, or tune. Simply point to your data at Amazon S3, define the schema, and start querying using the built-in query editor, or with your existing Business Intelligence (BI) tools. Athena automatically parallelizes your query, and dynamically scales resources for queries to run quickly. You pay only for the queries that you run.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div> <figure class="lb-img"> <div> <img src="/libs/settings/wcm/designs/default/resources/0.gif" alt="" title="" class="cq-dd-image cq-image-placeholder"> </div> </figure> </div> </div> </div> <div class="lb-grid"> <div class="lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 id="Next_Steps_on_AWS" class="lb-txt-bold lb-txt-none lb-txt-28 lb-small-v-margin lb-h2 lb-title" style="margin-top:0px; margin-bottom:0px;"> Next Steps on AWS</h2> <div class="lb-none-pad lb-none-v-margin lb-grid lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-8"> <figure class="lb-img"> <div style="padding-right:60px;"> <img src="https://d1.awsstatic.com/webteam/product-pages/Product-Page_Standard-Icons_01_Product-Features_SqInk.a8d5666758afc5121b4eb818ae18126031c4b61e.png" alt="" title="" class="cq-dd-image"> </div> </figure> <div class="lb-tiny-align-left 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