Startups > Dremio

Dremio

Dremio enables SQL analytics on cloud data lakes without moving data into warehouses.

Series E $420M total Founded 2015 Santa Clara, California

Dremio is a data lakehouse platform that enables fast SQL analytics on open data storage formats like Apache Iceberg, Amazon S3, and Azure Data Lake Storage without requiring data movement into proprietary systems. It combines the query performance of traditional data warehouses with the cost efficiency and flexibility of cloud object storage. The platform serves enterprises that want to query distributed data in place while maintaining interoperability and avoiding vendor lock-in. Dremio has raised $420M and is trusted by thousands of global enterprises including Shell, TD Bank, Michelin, and FactSet.

Problem solved

Organizations waste time and money copying data into multiple proprietary data warehouses instead of querying cost-effective cloud storage directly with warehouse-grade performance.

Target customer

Enterprise data teams and analytics organizations with large distributed datasets in cloud object storage who need fast query performance without proprietary warehouse costs.

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Founders

Tomer Shiran

Co-Founder, CPO

Former VP Product at MapR (grew company to 300+ employees), product and engineering roles at Microsoft and IBM Research, MS Computer Engineering from Carnegie Mellon.

Jacques Nadeau

Co-Founder, CTO

Also founder of Sundeck and YapMap.

Julian Hyde

Co-Founder

Funding history

Series A $10M September 2015 Led by Redpoint Ventures · Unknown

Series B Unknown Unknown Led by Unknown · Unknown

Series C $70M March 2020 Led by Insight Partners · Cisco Investments, Lightspeed Venture Partners, Redpoint Ventures, Norwest Venture Partners

Series D $135M January 2021 Led by Sapphire Ventures · Insight Partners, Lightspeed Venture Partners, Redpoint Ventures, Cisco Investments, Norwest Venture Partners

Series E $160M January 2022 Led by Adams Street Partners · Sapphire Ventures, Insight Partners, Lightspeed Venture Partners, StepStone Group, DTCP, Cisco Investments, Norwest Venture Partners

Total raised: $420M

Industries

Business Intelligence Big Data Analytics Machine Learning Software

Pricing

Consumption-based pricing measured in Dremio Compute Units (DCUs). Costs scale directly with compute usage; clusters auto-scale during peak demand and spin down when idle. No storage markup. Custom pricing available; no free plan.

Notable customers

Shell, TD Bank, Michelin, FactSet, Amazon, Abbott, Deloitte, GoPro, Airbus, Bentley, BlackRock, Bose, Decathlon, Carter's, Bridgestone, Nokia, Maersk, Québec Blue Cross

Integrations

Apache Iceberg, Amazon S3, Azure Data Lake Storage, Google Cloud Storage, HDFS, DBT

Website

dremio.com/

Competitors

Snowflake

Proprietary closed warehouse; Dremio works with open storage formats and avoids vendor lock-in with lower storage costs.

Databricks

Databricks charges for storage markup; Dremio's consumption-based model doesn't markup storage costs.

Trino

Open-source distributed SQL engine; Dremio adds managed platform, optimization, and semantic layer capabilities on top.

Starburst

Commercial Trino distribution; Dremio offers more integrated platform with data discovery and curation features.

Onehouse Technologies

Lakehouse competitor with different architectural approach; Dremio more established with larger customer base.

Why this matters: Dremio represents the shift toward open lakehouse architectures as enterprises push back against closed proprietary data warehouses. With $420M raised and customers like Shell and FactSet, it's validating the market for warehouse-performance analytics without warehouse costs or lock-in—particularly important as cloud storage becomes ubiquitous and consumption-based pricing becomes table stakes.

Best for: Enterprise analytics teams with data in cloud object storage who need warehouse-grade query performance without proprietary data warehouse costs or vendor lock-in.

Use cases

Energy Analytics at Scale

Shell processes 6-8 billion records in minutes, runs 100+ concurrent models, and built a scalable data mesh for energy insights. Dremio enabled real-time analytics on massive distributed datasets without duplicating data across systems.

Financial Data Acceleration

FactSet accelerated access to crucial financial data by 20x using Dremio, enabling clients to make better investment decisions faster. The platform replaced time-consuming data pipeline processes with instant SQL queries on cloud storage.

Cost Reduction with Query Performance

Québec Blue Cross cut infrastructure costs by 50% while accelerating data delivery using Dremio with DBT-enabled CI/CD pipelines. Organizations eliminate expensive warehouse licensing while maintaining or improving query speed.

Unified Analytics Platform

Maersk unified analytics across the organization by making Dremio the end-user SQL hub for their data platform in just six months. Eliminates fragmented analytics tools and provides single source of truth.

Alternatives

Snowflake Fully managed proprietary warehouse with higher costs; choose Snowflake if you want simplicity and don't mind vendor lock-in, Dremio if you need flexibility and cost efficiency.

Databricks Strong ML/Delta Lake focus with additional storage costs; choose Databricks for advanced ML workloads, Dremio for cost-efficient SQL analytics without storage markup.

Trino Open-source SQL engine requiring self-management; choose Trino for maximum control and no vendor lock-in, Dremio for managed platform with optimization and discovery features.

FAQ

What does Dremio do? +

Dremio is a data lakehouse platform that enables fast SQL analytics directly on cloud object storage (S3, Azure Data Lake Storage, etc.) without moving data into proprietary warehouses. It delivers warehouse-grade query performance on open data formats like Apache Iceberg while eliminating data duplication and vendor lock-in. Organizations query data in place, pay only for compute used, and maintain flexibility with open standards.

How much does Dremio cost? +

Dremio uses consumption-based pricing measured in Dremio Compute Units (DCUs), where you pay only for the compute you actually use. Unlike Snowflake and Databricks, Dremio doesn't mark up storage costs. Clusters auto-scale based on demand. Custom pricing is available for enterprise customers; no free plan is offered.

What are alternatives to Dremio? +

Snowflake offers a fully managed proprietary warehouse but with higher costs and lock-in. Databricks provides ML-focused analytics with Delta Lake but charges storage markups. Trino is an open-source SQL engine offering maximum control but requires self-management. Starburst is a commercial Trino distribution with managed support.

Who uses Dremio? +

Enterprise data teams and analytics organizations with large distributed datasets in cloud storage. Notable customers include Shell, TD Bank, Michelin, FactSet, Amazon, Abbott, Deloitte, BlackRock, Bentley, Nokia, and Maersk. Companies range from energy, finance, logistics, insurance, and consulting sectors.

How does Dremio compare to Snowflake? +

Snowflake is a proprietary closed warehouse that requires moving data into its system; Dremio queries data in place on open cloud storage. Snowflake offers simplicity but with higher costs and vendor lock-in; Dremio provides flexibility, cost efficiency (no storage markup), and interoperability. Choose Snowflake for simplicity, Dremio for cost-efficient analytics without lock-in.

Does Dremio require moving data? +

No. Dremio's key advantage is querying data in place on cloud object storage like Amazon S3, Azure Data Lake Storage, and Google Cloud Storage. Data remains in open formats (Apache Iceberg) without requiring duplication or copying into proprietary systems.

What storage formats does Dremio support? +

Dremio works with open data storage formats including Apache Iceberg, Parquet, and ORC, and integrates with Amazon S3, Azure Data Lake Storage, Google Cloud Storage, and HDFS. This open format approach enables data portability and avoids vendor lock-in.