Dremio is an open data lakehouse platform that enhances data accessibility and performance while reducing costs, enabling companies to eliminate data silos and improve self-service analytics. It utilizes technologies like Apache Arrow and Iceberg to provide sub-second query performance, and its architecture supports federated queries across various data sources with enterprise-grade security features. Dremio aims to address issues like skyrocketing data warehouse costs, compliance risks, and limited resources so organizations can effectively democratize data access.

1. Confidential - Do Not Share or Distribute
Dremio
The Easy and Open Lakehouse Platform
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2. Confidential - Do Not Share or Distribute
The Easy and Open Data Lakehouse Platform
– Data warehouse performance directly on the lake
– Query acceleration to eliminate copies and BI extracts
– Semantic layer to enable governed self-service
– Database connectors to enable queries on other sources
Enterprise Adoption
– 1000s of companies across all industries
– 5 of the Fortune 10
Open Source & Community
– Apache Arrow (60M+ downloads/m), Apache Iceberg, Nessie
– Creator and host of Subsurface LIVE conference
About Dremio

3. 3 Confidential - Do Not Share or Distribute
SQL
Data Science Dashboards Apps
Companies Want to Democratize Data… But How?
▪ Everyone wants access
▪ Data volumes are
exploding
▪ Security risks
▪ Compliance requirements
▪ Limited resources
Application Databases | IoT | Web | Logs
Continuous New Data
ADLS RDBMS
S3 GCS
Cloud Object Storage On-Prem

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SQL
Data Science Dashboards Apps
Data Warehouses: Expensive, Proprietary, Complex
Application Databases | IoT | Web | Logs
Continuous New Data
✗ Skyrocketing costs
✗ Vendor lock-in
✗ Exploding backlog
✗ Can’t explore data
✗ No self-service
ADLS RDBMS
S3 GCS
Cloud Object Storage On-Prem

5. 5 Confidential - Do Not Share or Distribute
SQL
Data Science Dashboards Apps
Dremio Data Lakehouse: Easy, Open, 1/10th the Cost
Application Databases | IoT | Web | Logs
Continuous New Data
⇅ ODBC | JDBC | REST | Arrow Flight ⇅
⇅ Parallelism | Caching | Optimized Push-Downs ⇅
✓ Sub-second performance
✓ Eliminate Data Silos
✓ Improve Data Discovery and
Access
✓ No Data Movement Required
✓ No Copies
✓ Inexpensive
✓ No lock-in
ADLS RDBMS
S3 GCS
Cloud Object Storage On-Prem

6. 6 Confidential - Do Not Share or Distribute
Raw
zone
Physical
datasets
Semantic
zone
Virtual
datasets
Data
Engineers
BI Users
SQL
Data Scientists
⇅ ODBC | JDBC | REST | Arrow Flight ⇅
ADLS S3
or or
Acceleration
(Data Reflections)
Data
Analysts
and
Engineers
IT-Governed Self-Service Semantic Layer
Standardized, User-Defined Abstraction Layer Enabling Virtual Data Sets, with an Easy-to-Use UI
Data Analysts
✓ Consistent business logic & KPIs
✓ No more waiting for IT
✓ Use visualization tool(s) of choice
Data Engineers & Architects
✓ Centralize data security & governance
✓ No more reactive, tedious work
✓ Easy collaboration with data analysts

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SQL
Data Science Dashboards Apps
A Realistic Example: DW Offload
Application Databases | IoT | Web | Logs
Continuous New Data
✗ Maxed capacity
✗ End-of-life support
✗ Complex ETL processes
✗ Legacy query engines
performance
RDBMS

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SQL
Data Science Dashboards Apps
A Realistic Example: DW Offload
Application Databases | IoT | Web | Logs
Continuous New Data
RDBMS
✓ Unified layer
✓ Combine DW and DL data
✓ Address DW capacity issues
✓ Smooth transition
Third-Party Data
Bloomberg, S&P, AWS Data Exchange…
Semantic Model

9. Fast Performance
Apache Arrow-based columnar
execution increases throughput and
reduces cost
Transparent Acceleration
Reflections enable sub-second
queries and eliminate copies and BI
extracts
Semantic Layer
Data teams define and expose a
logical data model for governed
self-service
Ingest & Transform Data
DML and dbt integration help ingest
data into the lakehouse and transform
it as needed.
Open Data Formats
Apache Iceberg ensures no vendor
lock-in and the flexibility to use any
engine.
Enterprise-Grade Security
Role-based access control, native
row/column-level policies and
advanced integrations.
Dremio at a glance

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Powering Analytics for Thousands of Companies
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Merci !
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Open Source Roots: Apache Arrow Inside
– Dremio seeded the market with its internal memory format
– Arrow now downloaded over 60M times per month
– Dremio is the only Arrow-based engine in the market
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Apache Arrow was created by Dremio
– Data is immediately read into Arrow
– All operators use Arrow as input and output
– Gandiva: LLVM-based vectorized execution on Apache Arrow
Arrow-based vectorized execution

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Data Sources
Data Lake Engine
BI Users
SQL
Data Scientists
Data
Consumer
Tools
⇅ ODBC | JDBC | REST | Arrow Flight ⇅
⇅ Optimized Push-Downs ⇅
Coordinator
Node
Executor
Nodes
Orchestrated via Cloud, Kubernetes or YARN
External Data Reflection Stores Data Reflection Stores
Executor
Nodes
Executor
Nodes
Coordinator
Node
Coordinator
Node
DREMIO
Dremio deployment architecture

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Query Acceleration: BI on Data Lakes
Columnar Cloud Cache (C3) Data Reflections
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– NVMe-level I/O performance on S3/ADLS/GCS
– Eliminate S3/ADLS I/O costs (10-15% of cost per query)
– Use existing NVMe/SSD on EC2 instances & Azure VMs
– Transparent to analysts and engineers
– Enable low-latency (including sub-second) BI queries
– Eliminate cubes and BI extracts
– Reduce infrastructure costs by up to 100x
– Persisted on Data Lake as Parquet/Iceberg tables
– Transparent to analysts (advanced query plan rewrites)
NVMe NVMe NVMe NVMe
Data Lake
Columnar Cloud Cache (C3)
Executor Executor Executor Executor
ENGINE
User-specific cubes, extracts, aggregations
Domain-specific data marts
User picks the best optimization
DL/DW
Dremio picks the best optimization
DL
TRADITIONAL DREMIO
Reflections

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Multi-Engine Architecture
XL
M
L
Engine Routing Rules
● User
● Roles
● Query type
● Query cost
● Connection parameters
● Date & time
● ...
Queues Engines
Query
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LOWER EC2 COSTS
Auto-stop/start and right-sized
engines eliminate the need to
over-provision infrastructure.
60% NOISY NEIGHBOR CONCERNS
Workloads are physically separated
so one workload can’t impact the
performance of another workload.
0 CONTROL OF RESOURCES
Control resource allocation with policies
such as query priority, max query cost,
max queue time, max runtime, etc.
100%

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The Dremio Advantage
Open Data, No Lock-In
● Modern and Intuitive User Interface
● Unified View of Data (on-prem,
hybrid and Cloud)
● Federated Queries
Based on community-driven standards:
● Apache Parquet
● Apache Iceberg
● Apache Arrow
Sub-Second Performance
at 1/10th the Cost
Self–Service Analytics
● Lightning-fast queries
● High concurrency
● No expensive data copies to manage
● No semantic layer
● No federated queries
● Cloud only
● Proprietary platform
● Must ingest data in order to query it
● Limited Apache Iceberg support
● Very expensive
● Data duplication
● No query acceleration
● Poor performance with open standards
● Designed for batch processing
(ETL/data science)
● No semantic layer
● Experimental federated queries
● Cloud only
● Focused on Delta Lake, not Apache
Iceberg
● No query acceleration, BI
extracts/imports required for low latency
● Limited and expensive for data serving
● Proven cost reduction after replacement
by Dremio