List of Presto Customers
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Since 2010, our global team of researchers has been studying Presto customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Presto for Database Management from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Presto for Database Management include: Meta, a United States based Media organisation with 75945 employees and revenues of $164.50 billion, Netflix, a United States based Media organisation with 14000 employees and revenues of $39.00 billion, Airbnb, a United States based Professional Services organisation with 7300 employees and revenues of $11.10 billion and many others.
Contact us if you need a completed and verified list of companies using Presto, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Presto customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Airbnb | Professional Services | 7300 | $11.1B | United States | Presto | Presto | Database Management | 2014 | n/a |
In 2014, Airbnb integrated Presto as its interactive query engine, deploying Presto as a Database Management application to support exploratory SQL analysis and BI. Airbnb built Airpal, a web based query execution tool on top of Presto, to broaden employee access to data and self service analysis.
Airpal functioned as the primary user facing module, surfacing Presto query execution, result browsing, and simple job management through a browser interface. Presto provided the interactive execution layer, enabling ad hoc SQL workflows and concurrent query handling consistent with an interactive query engine implementation.
The implementation explicitly connected Presto to Airbnb's Hadoop data warehouse, enabling analysts and nontechnical teams to run ad hoc queries across Hadoop resident datasets. Airpal was launched internally around spring 2014, and within a year over one third of employees had issued queries, indicating company wide operational coverage beyond a central analytics team.
Governance and rollout emphasized self service access through the Airpal UI, changing how exploratory analysis and BI requests were fulfilled across business functions. The adoption pattern documented the role of Presto in speeding ad hoc analysis across Airbnb's Hadoop data warehouse, reinforcing Presto as the central Database Management component for interactive querying.
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Meta | Media | 75945 | $164.5B | United States | Presto | Presto | Database Management | 2012 | n/a |
In 2012 Meta developed Presto and launched the project to build an internal distributed SQL engine. The project began in fall 2012 and reached company wide production in 2013, after which Presto operated as Meta's Database Management platform for interactive analytics and data warehouse queries.
Presto was implemented to provide SQL processing, parallel query execution, and low latency ad hoc analysis over petabyte scale data, enabling interactive analysis for analytics and data engineering teams. The implementation emphasized a distributed execution model and scalable query processing consistent with Database Management platform architectures.
Operational deployment used a distributed cluster architecture with query coordination and parallel worker execution to support concurrent workloads, and the environment ran across multiple regions. Presto in production supported thousands of users and processed tens of thousands of queries per day, serving ad operations, analytics, and data warehousing use cases at scale.
Rollout progressed from fall 2012 development to company wide production in 2013, accompanied by governance and operational processes for query management, resource allocation and cross region availability. Meta's Presto implementation established an internal Database Management capability focused on interactive, low latency ad hoc analytics over very large data sets.
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Netflix | Media | 14000 | $39.0B | United States | Presto | Presto | Database Management | 2014 | n/a |
In 2014, Netflix put Presto into production as part of its big data platform to provide low latency, interactive queries against a multi petabyte data warehouse on AWS S3, positioning Presto as a Database Management component for analytics workloads. The deployment targeted analytics use cases such as A/B testing and product insights and supported thousands of daily queries by analytics teams.
Presto was implemented as a distributed SQL query engine offering interactive SQL processing and connector based access to large object storage, with configuration and runtime tuning focused on large scale read performance and parallel execution. Functional capabilities emphasized include interactive query execution, federated access to S3 stored datasets, and optimized handling of columnar Parquet files, all consistent with Database Management responsibilities.
Integrations in the implementation were explicitly with AWS S3 for storage and the Parquet columnar format for on disk data, and Netflix contributed S3 and Parquet integration improvements back to the Presto community. The implementation operated within Netflixs AWS based big data platform and directly supported product analytics workflows and experimentation pipelines.
Operationally the team put Presto into production in 2014 and established ongoing operational ownership within the data platform organization, incorporating production deployment practices and performance tuning for multi petabyte reads. Governance and workflow changes included iterative enhancement of connectors and collaborative contributions to the open source Presto project, aligning implementation maintenance with community driven improvements.
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