9 minute read · January 21, 2025
Modeling Your Data Lakehouse with Dremio’s Query Federation, Semantic Layer & Reflections

· Senior Tech Evangelist, Dremio

Building high-quality data models is at the core of creating a robust analytics ecosystem. However, this task often becomes a daunting challenge as organizations grow and data environments become increasingly complex. Data Engineers frequently find their efforts derailed by the need to move data into centralized data warehouses, a process that introduces inefficiencies and complications.
To accelerate queries and improve performance, traditional approaches rely on materialized views and cubes. While effective in some cases, these methods create multiple versions of datasets. This proliferation leads to confusion as teams juggle between competing versions, trying to determine which is the most accurate or appropriate for a specific use case. The complexity doesn’t stop there—within large enterprises, different business units often maintain their own data warehouses, further fragmenting data and leading to duplicative work and inconsistent data definitions.
These challenges create bottlenecks that slow down analytics workflows, introduce higher costs, and make collaboration across business units more difficult. The question remains: how can organizations reduce complexity, improve data modeling processes, and ensure consistent and reliable data for all stakeholders?
In the sections ahead, we’ll explore how Dremio addresses these challenges by empowering organizations to adopt a data lakehouse approach and leverage advanced features like query federation, a semantic layer, and reflections to transform how enterprises model their data.
The Data Lakehouse: A Step Toward Simplification
A promising solution to the challenges of data modeling and integration is the data lakehouse—an architecture that combines the flexibility of data lakes with the performance and structure of data warehouses. By using a table format like Apache Iceberg, organizations can store their tables directly in their data lake while gaining many of the transactional and analytical capabilities traditionally associated with warehouses.
This approach addresses several pain points. With all your datasets residing in a centralized lakehouse, there’s less need to move data into multiple data warehouses for different business units. It reduces duplicative efforts, ensures consistent data definitions across teams, and creates a more unified data landscape.
However, this solution is not without its challenges. Not all data can—or should—be stored in your data lakehouse. Some data may remain in operational databases, external systems, or third-party platforms. Additionally, the traditional approach of accelerating queries with materialized views and cubes still carries over, which leads to inflated complexity in your data models. You may find yourself materializing more iterations of your datasets than necessary, creating redundant work and introducing confusion for analysts and scientists.
While the data lakehouse architecture lays a strong foundation, it alone does not address the full spectrum of challenges in modern data modeling. To truly simplify and optimize the process, you need a platform that can bridge the gaps in integration, modeling, and performance optimization.
Dremio: Patching the Gaps in Your Data Lakehouse
While a data lakehouse solves many issues around centralizing datasets, Dremio takes it a step further by addressing the remaining challenges of integration, modeling complexity, and query acceleration. With its query federation, semantic layer, and reflections, Dremio provides a unified platform that simplifies data modeling while optimizing performance.
Query Federation: Access All Your Data, Anywhere
Dremio’s query federation feature allows you to connect and query data from multiple sources, whether it’s stored in data lakes, databases, data warehouses, or even other lakehouse catalogs. This means that not all your data needs to reside in your lakehouse for you to benefit from Dremio.
By enabling seamless access to third-party data and long-tail datasets that may not be fully integrated into your lakehouse, Dremio lets you progressively adopt the lakehouse model without the need for an immediate, wholesale migration. This flexibility ensures that you can bring together all your data, regardless of where it resides, for unified analytics and modeling.
Semantic Layer: Streamlined and Consistent Data Models
Dremio’s semantic layer empowers you to define data models directly on top of your data sources without needing to materialize multiple versions of your datasets. Whether you prefer dimensional modeling, one big table, or data vault paradigms, Dremio supports your preferred approach while allowing you to curate your data using popular three-tier frameworks such as:
- Bronze/Silver/Gold
- Raw/Business/Application
- Raw/Clean/Semantic
Instead of creating physical datasets for each tier or iteration, you can use SQL views in Dremio to define your models virtually. This approach ensures consistency, reduces duplication, and makes it easier for analysts and data scientists to work with reliable, pre-defined data structures across business units in their preferred notebooks or BI tools.
Reflections: Smarter Query Acceleration
Dremio’s reflections replace the need for materialized views and cubes, offering a smarter and more efficient way to accelerate queries. Reflections act as an Iceberg-based relational cache on your data lake, transparently substituting optimized datasets for queries that would benefit from them.
For example, if you have a dataset with three common query patterns, you no longer need to create three separate materialized views and rely on analysts to choose the correct one. Instead, you can define raw reflections (to accelerate raw data queries) and aggregate reflections (to optimize aggregate queries). Dremio automatically determines the best reflection to use for a given query, simplifying the analyst experience while maintaining optimal performance.
Reflections are particularly powerful for data lakehouse tables stored in Parquet or Iceberg. With live reflections, updates to the underlying data are automatically reflected in the cache in near real-time. Additionally, incremental reflections ensure that only the changes in your datasets are processed, minimizing compute costs and keeping the cache up to date without manual intervention.
By combining these features, Dremio eliminates the need to materialize unnecessary iterations of your datasets, making modeling more efficient and significantly reducing complexity for data engineers and analysts alike.
Conclusion: Redefining Data Modeling with Dremio
The journey to creating high-quality, scalable data models has long been filled with obstacles. From managing duplicative data warehouses to wrestling with the complexity of materialized views and cubes, traditional architectures often leave developers, analysts, and data scientists juggling inefficiencies and inconsistencies.
Dremio transforms this process by addressing these pain points and enabling organizations to move toward a modern, efficient data lakehouse. With Dremio’s query federation, you can seamlessly integrate data from diverse sources, making it easier to adopt the lakehouse model progressively. Its semantic layer allows you to define virtual data models across all your data, reducing duplication while ensuring consistency and reliability.
The game-changing feature of Dremio’s reflections empowers you to accelerate queries without the need for materialized views or cubes. By leveraging raw and aggregate reflections, you create a transparent, Iceberg-based relational cache that ensures optimal performance while simplifying the experience for analysts. Features like live reflections and incremental updates further enhance performance and reduce maintenance overhead, keeping your cache in sync with minimal effort.
With Dremio, enterprises can achieve a unified approach to data modeling that simplifies workflows, reduces costs, and improves collaboration across teams. The result is a streamlined, high-performing data environment where analysts and scientists can focus on delivering insights rather than untangling complexity.
If you’re ready to take your data modeling to the next level, Dremio provides the tools and flexibility to redefine what’s possible in your data lakehouse. The future of enterprise data modeling starts here.
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