Unlock AI and Analytics:
Escape Redshift's Limitations
Accelerate Innovation with Dremio's Agentic Lakehouse
Simpler, faster, more open and flexible – at lower cost. Spend your time innovating with AI and analytics instead of wrestling with Redshift's maintenance, tuning, and scaling limitations.
OVERVIEW
Why Dremio Cloud
Enable Agentic AI and analytics with unified data through our AI Semantic Layer and Dremio’s AI Agent to connect your LLM to your data - at a fraction of Redshift's cost and complexity.
Fastest Lakehouse
Sub-second query performance at any level of scale.
Autonomous Optimization
Intelligently manages materialization, SQL queries, clustering, and table management.
Fraction of the Cost
Less than half the cost of Redshift, with no lock-in (built on Apache Iceberg, Polaris, and Arrow).
Columnar performance
Proven at scale
Zero ETL
Simplified architecture
Open standards
Enterprise secure
CUSTOMER STORIES
Proven Results from Leading Companies
Leading Regional Bank:
Dramatically increased query speed leveraging an Iceberg lakehouse with Dremio vs. Redshift
- 10x faster query performance (from 80 seconds to 10-12 seconds)
- Eliminated Amazon Redshift licensing and infrastructure costs performance (from 80 seconds to 10-12 seconds)
- Simplified data pipeline with fewer moving parts
Global Beauty & Wellness Brand:
Faster Queries, Lower Costs, Eliminated Data Silos
- Significant cost-performance advantage – faster queries with reduced hardware requirements
- Built performant, cost-effect next-generation data platform with Dremio
- Eliminated data scattered across systems – no more extracts from Redshift to Power BI, achieved single source of truth through semantic layer
Top 10 Global Pharma Company:
Enabled Direct Data Access for Business Users
- Business users understand data better than engineers but couldn't access it – Dremio enabled direct access through an enterprise semantic layer
- Increased performance across the board while reducing ETL pipelines
OVERVIEW
Why Leading Companies Choose
Dremio Over Redshift
PROBLEM
Performance and
Concurrency Limits
- Scaling constraints limit concurrent users and effective query execution
- Constant tuning of sort keys, distribution keys, vacuuming jobs
- Poor performance on complex joins, semi-structured data, large data volumes
- No data democratization due to performance constraints
SOLUTION
Superior Performance and Scale
- Handle unlimited concurrent users with sub-second query performance
- Autonomous performance provides automatic query acceleration
- Automatic table optimization eliminates manual tuning
- Auto clustering removes sort/distribution key management
- Apache Arrow Core and C3 Columnar Cache deliver lightning fast performance
PROBLEM
Infrastructure
Maintenance Overhead
- ETL complexity maintaining pipelines from data lake to Redshift
- Proprietary table formats – only Redshift can access data
- Data duplication & schema management across environments
- Manual optimization tasks: vacuuming, partitioning, etc.
SOLUTION
Zero-Maintenance Architecture
- Zero ETL with direct query federation eliminates pipeline complexity
- Modern lakehouse architecture - eliminates data duplication
and movement - Open standards built on Iceberg, Arrow, and Polaris
- Enterprise Catalog with RBAC and metadata in open format built on Apache Polaris (incubating)
PROBLEM
High Costs & Lock-in
- AWS vendor lock-in – no flexibility when they raise costs
- High infrastructure & licensing costs, data duplication
- Additional ETL tool costs: Glue, Lambda, Matillion, Fivetran, dbt, Airflow
- Always-on pricing model vs. pay-as-you-go
SOLUTION
Lower Total Cost of Ownership
- Pay-as-you-go pricing vs. Redshift's always-on model
- Open Iceberg lakehouse format prevents vendor lock-in
- Query federation eliminates expensive ETL pipelines
- No data duplication required – data stays in lake
- Open lakehouse architecture leverages best deals across providers
Redshift vs. Dremio: The Clear Choice
CAPABILITY
Amazon Redshift
dremio
Concurrent Users
Limited - performance degrades with more users
Unlimited concurrent users with consistent performance
Query Performance
Requires constant manual tuning of sort/distribution keys
Autonomous optimization with sub-second queries
Data Architecture
Separate data lake + warehouse with ETL complexity
Unified lakehouse with zero ETL
Pricing Model
Always-on pricing - pay even when not using
Pay-as-you-go - only pay for what you use
Vendor Lock-in
AWS proprietary formats and ecosystem lock-in
Open standards (Iceberg, Arrow) - cloud agnostic
Maintenance Overhead
Manual vacuuming, partitioning, optimization tasks
Autonomous maintenance and optimization
