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Distributed SQL Query Engine for Big Data

What is Presto?

Distributed SQL Query Engine for Big Data
Presto is a tool in the Big Data Tools category of a tech stack.
Presto is an open source tool with GitHub stars and GitHub forks. Here’s a link to Presto's open source repository on GitHub

Who uses Presto?

54 companies reportedly use Presto in their tech stacks, including Airbnb, Facebook, and Netflix.

334 developers on StackShare have stated that they use Presto.

Presto Integrations

MySQL, PostgreSQL, Redis, Kafka, and Microsoft SQL Server are some of the popular tools that integrate with Presto. Here's a list of all 30 tools that integrate with Presto.
Pros of Presto
Works directly on files in s3 (no ETL)
Join multiple databases
Gets ready in minutes
Decisions about Presto

Here are some stack decisions, common use cases and reviews by companies and developers who chose Presto in their tech stack.

Needs advice

Hello experts,

I am trying to get a comprehensive list of the differences in syntax between Presto and PostgreSQL.

Is there an official documentation where I can find it? Is one a subset of another (as in all commands of presto are in Postgres or vice versa)?

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Manish Bhoge
Solution Architect at Oracle Financial Software Services · | 3 upvotes · 22.8K views
Needs advice

We are evaluating Presto against the Denodo to build the virtualization layer on top of the Cloudera Data warehouse. We have customer and transaction data in the Cloudera data warehouse, and we want to build the virtualization layer on top of the multiple datasets and Cloudera DW.

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Pardha Saradhi
Technical Lead at Incred Financial Solutions · | 6 upvotes · 101.9K views
Needs advice
Amazon S3Amazon S3MetabaseMetabase


We are currently storing the data in Amazon S3 using Apache Parquet format. We are using Presto to query the data from S3 and catalog it using AWS Glue catalog. We have Metabase sitting on top of Presto, where our reports are present. Currently, Presto is becoming too costly for us, and we are looking for alternatives for it but want to use the remaining setup (S3, Metabase) as much as possible. Please suggest alternative approaches.

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Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

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Jobs that mention Presto as a desired skillset

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Presto Alternatives & Comparisons

What are some alternatives to Presto?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
A state-of-the-art platform for statistical modeling and high-performance statistical computation. Used for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.
Apache Impala
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
Apache Drill
Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
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Presto's Followers
1026 developers follow Presto to keep up with related blogs and decisions.