List of Apache Druid Customers
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Since 2010, our global team of researchers has been studying Apache Druid 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 Apache Druid 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 Apache Druid for Database Management include: Walmart, a United States based Retail organisation with 2100000 employees and revenues of $681.00 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, Pinterest, a United States based Media organisation with 4000 employees and revenues of $2.80 billion and many others.
Contact us if you need a completed and verified list of companies using Apache Druid, 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.
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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 | Apache Software | Apache Druid | Database Management | 2018 | n/a |
In 2018, Airbnb deployed Apache Druid for Analytics/BI to power real-time and batch analytics for dashboards and self service metric systems in the United States. The implementation targeted interactive slice and dice on event data and supported product metric monitoring for engineering and analytics teams.
Apache Druid was integrated into an event pipeline using Kafka for streaming ingest, Spark for batch processing, and Superset for visualization, providing both streaming and historical query paths. Functional capabilities emphasized low latency OLAP queries, materialized rollups and time series indexing to enable sub second queries for dashboards, with BI and dashboarding usage inferred from engineering documentation and query behavior.
Operational coverage focused on product analytics and dashboard consumers within the United States, and the deployment supported anomaly detection workflows for product metrics. Governance concentrated on self service metric systems and centralized event schemas to maintain metric consistency, and engineering notes explicitly report sub second queries for dashboards and improved anomaly detection as outcomes.
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Netflix | Media | 14000 | $39.0B | United States | Apache Software | Apache Druid | Database Management | 2018 | n/a |
In 2018 Netflix implemented Apache Druid as a core analytics platform for observability/operations, using Apache Druid to power real-time playback-quality analytics across devices. The implementation is anchored in sub-second query latency targets at massive ingest rates, reflecting an architecture designed for millions of events per second and the ability to interrogate trillions of rows of event data.
Apache Druid was configured to support real-time ingestion and low-latency OLAP style queries, enabling engineers to slice and dice event streams and run A/B tests to detect regressions in playback quality. Functional capabilities emphasized in the deployment include real-time aggregation, ad hoc interactive queries, high-cardinality segmentation, and continuous anomaly detection workflows.
The deployment operationally covers device-level playback data across the United States and is used primarily by engineering and observability teams to monitor service quality and to validate controlled rollouts. The implementation supports controlled rollouts and experiment analysis by surfacing regression signals from A/B tests and feeding alerts into continuous anomaly detection processes.
Governance and operationalization focused on embedding Apache Druid into production observability pipelines, enabling engineers to run exploratory queries and automated checks with sub-second response times. The stated outcome from this implementation was sub-second query latency at massive scale, while the platform enabled realtime detection of regressions and supported rollout control mechanisms.
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Media | 4000 | $2.8B | United States | Apache Software | Apache Druid | Database Management | 2014 | n/a |
In 2014, Pinterest implemented Apache Druid as its core Database Management platform to power Promoted Pins reporting and broader ads analytics. Pinterest initially ran reporting metrics on Apache HBase, but growing scale and increasingly fine grained grouping and aggregation requirements rendered HBase insufficient, prompting the adoption of Apache Druid to support real time ingestion, fast aggregation, and flexible queryability.
The Apache Druid implementation uses both native and Hadoop ingestion modes, with ingestion jobs launched via REST to the Overlord node and native tasks executed on MiddleManager nodes. To avoid independent ingestion pipelines overwriting one another, Pinterest extended Druid segment versioning with a namespace identifier and built separate versioned interval timelines per namespace. Because native ingestion was too slow at scale and launching a new Hadoop indexing approach was undesirable, Pinterest adapted the metamx druid spark batch project to run a standalone Spark writer that filters events by interval, partitions data by configured granularity and rows per segment, persists intermediate indexes using a pool of IncrementalIndex classes, performs a final merge to create segment files, pushes segments to deep storage, and writes metadata to MySQL for the Druid coordinator to discover.
Integrations and operational coverage include real time ingestion via Kafka, optional Hadoop MapReduce based indexing, a standalone Spark ingestion pipeline, deep storage for segments, MySQL for metadata, and standard Druid services such as coordinator, historical, and middle manager nodes. The deployment is external facing, serving interactive queries from Pinterest’s ads management UI and programmatic access via external APIs, and supports both Druid native queries and SQL through Apache Calcite, with a preference for native queries in latency sensitive paths. A metadata store was introduced to short circuit queries for entities and time intervals that contain no metrics, reducing unnecessary load on Druid.
Operational governance and workflow changes included a tiered historical topology and an auto scaling strategy based on mirroring tiers. The cluster is bucketed into a hot tier for the most recent data on compute optimized nodes, a cold tier for recent historical data with more disk capacity, and an icy tier for older archival data on lower cost nodes, with hot nodes sized to keep segments memory resident. Because Druid’s rebalancing is slow at multi terabyte scale, Pinterest implemented a mirror tier mechanism that links mirror nodes to primary nodes using maximum bipartite matching so mirrors can download segments from deep storage and serve queries immediately during traffic spikes. Query construction governance centralized a query constructor and execution service, enforced rejection of invalid queries via the metadata store, favored native queries for high QPS workflows and reserved SQL for non latency sensitive internal use, and applied rate limiting and batching strategies to reduce connection and object handle pressure.
Pinterest adopted Apache Druid Database Management to eliminate extensive pre built slicing logic and to gain automatic segment versioning, data pre aggregation, approximate count distinct algorithms, and a SQL interface. The implementation required cluster and query tuning, including avoiding heavy use of GroupBy in favor of Timeseries and TopN where possible, and batching entity filters to reduce QPS. Pinterest continues platform work with intentions to contribute features such as a Spark writer and reader for Druid segments, mirroring tier support for auto scaling, and a new multiplexing IPC protocol, while onboarding other teams to leverage Druid at scale.
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Retail | 2100000 | $681.0B | United States | Apache Software | Apache Druid | Database Management | 2017 | n/a |
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FAQ - APPS RUN THE WORLD Apache Druid Coverage
Apache Druid is a Database Management solution from Apache Software.
Companies worldwide use Apache Druid, from small firms to large enterprises across 21+ industries.
Organizations such as Walmart, Netflix, Airbnb and Pinterest are recorded users of
Apache Druid for Database Management.
Companies using Apache Druid are most concentrated in Retail, Media and Professional Services, with adoption spanning over 21 industries.
Companies using Apache Druid are most concentrated in United States, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Apache Druid across Americas, EMEA, and APAC.
Companies using Apache Druid range from small businesses with 0-100 employees - 0%, to mid-sized firms with 101-1,000 employees - 0%, large organizations with 1,001-10,000 employees - 50%, and global enterprises with 10,000+ employees - 50%.
Customers of Apache Druid include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified Apache Druid customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of Database Management.