Dremio is now part of SAP – https://www.dremio.com/ & https://news.sap.com/2026/07/sap-completes-dremio-acquisition/ 30 Days Trial – Try out now: https://app.dremio.cloud/Blog from @SergioZ : https://community.sap.com/t5/data-professionals-blog-posts/from-question-to-insight-in-6-seconds-joule-dremio-sap-ai-core-in-action/bc-p/14458558#M225 |
Why Dremio is Getting Attention
Organizations today are struggling with several challenges:
- Data spread across multiple systems
- High cost of data movement and replication
- Long ETL processing windows
- Multiple versions of the truth
- Business users waiting for curated datasets
Dremio addresses many of these challenges by acting as a data lakehouse query engine and semantic layer. Instead of copying data repeatedly into various data marts, Dremio enables users to query data where it resides and create virtualized business views.
Key capabilities include:
- Data virtualization
- Semantic data layer
- Query acceleration
- Data lakehouse support
- Self-service analytics
- Open table format support such as Apache Iceberg
Generate Token for Integration
client = flight.FlightClient(“grpc+tls://data.dremio.cloud:443″)
options = flight.FlightCallOptions(headers=[
(b”authorization”, f”Bearer {DREMIO_PAT}”.encode()),
(b”catalog”, PROJECT_ID.encode()), # routes to the right Dremio project
])
Why You Should Explore Dremio Early
Many organizations can understand better and avoid common mistake:
you can begin looking into integrations before fully understanding the platform.
A better strategy is to play in a standalone sandbox and evaluate Dremio independently.
Explore the Following Areas
Data Virtualization
Understand:
- What datasets remain virtual
- What requires physical optimization
- Cost implications
Semantic Modeling
Compare Dremio's semantic capabilities against:
- BW Composite Providers
- BW Queries
- Datasphere Business Builder
- HANA Calculation Views
Security Integration
Evaluate:
- Row-level security
- Role-based access
- Integration with enterprise identity providers
Performance Characteristics
Test:
- Large joins
- SAP extraction scenarios
- Historical reporting
- Multi-source analytics
Final Thoughts
Dremio is not merely another reporting tool. It represents a different architectural philosophy centered around data virtualization, semantic abstraction, and lakehouse-driven analytics. For organizations, it offers exciting possibilities to bridge SAP and non-SAP data landscapes while reducing data movement and accelerating self-service analytics.
However, like any strategic platform, success depends on understanding its architecture, strengths, and limitations before deeply coupling it with mission-critical SAP processes. The most effective approach is to experiment early, build proof-of-concepts, compare it with your existing SAP capabilities, and identify where Dremio complements rather than replaces your investments.
By understanding the Dremio landscape first, we as SAP architects can make informed decisions and build a future-ready analytics ecosystem that balances innovation, governance, and operational stability.