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Introduction
The data lakehouse has been a significant topic in data architecture over the past several years. However, like any high-value trend, it’s easy to get caught up in the hype and lose sight of the real reasons for adopting this new paradigm.
In this article, I aim to clarify the key benefits of a lakehouse, highlight the challenges organizations face in implementing one, and explore practical solutions to overcome those challenges.
The Problems We Are Trying to Solve For
Traditionally, running analytics directly on operational databases (OLTP systems) is neither performant nor efficient, as it creates resource contention with transactional workloads that power enterprise operations. The standard solution has been to offload this data into a data warehouse, which optimizes storage for analytics, manages data efficiently, and provides a processing layer for analytical queries.
However, not all data is structured or fits neatly into a data warehouse. Additionally, storing all structured data in a data warehouse can be cost-prohibitive. As a result, an intermediate layer—a data lake—is often introduced, where copies of data are stored for ad hoc analysis on distributed storage systems like Amazon S3, ADLS, MinIO, NetApp StorageGRID, Vast Data, Pure Storage, Nutanix, and others.
In large enterprises, different business units often choose different data warehouses, leading to multiple copies of the same data, inconsistently modeled across departments. This fragmentation introduces several challenges:
1. Consistency
With multiple copies, business metrics can have different definitions and values depending on which department’s data model you reference, leading to discrepancies in decision-making.
2. Time to Insight
As data volumes grow and the demand for real-time or near real-time insights increases, excessive data movement becomes a bottleneck. Even if individual transactions are fast, the cumulative impact of copying and processing delays data accessibility.
3. Centralization Bottlenecks
To improve consistency, some organizations centralize modeling in an enterprise-wide data warehouse with department-specific data marts. However, this centralization can create bottlenecks, slowing down access to insights.
4. Cost
Every step of data movement incurs costs:
- Compute resources for processing,
- Storage costs for redundant copies, and
- BI tool expenses from multiple teams generating similar data extracts across different tools.
5. Governance
Not all enterprise data resides in a data warehouse. There will always be a long tail of data in external systems, such as:
- Partner-shared data,
- Data marketplaces, or
- Regulatory-restricted environments.
Managing access to a holistic data picture while maintaining governance and security across distributed sources is a significant challenge.
This is where the data lakehouse emerges as a solution.
The Data Lakehouse Solution
Data warehouses provide essential data management capabilities and ACID guarantees, ensuring consistency and reliability in analytics. However, these features have traditionally been absent from data lakes, as data lakes are not inherently data platforms but repositories of raw data stored on open storage.
If we bring data management and ACID transactions to the data lake, organizations can work with a single canonical copy directly within the lake, eliminating the need to replicate data across multiple data warehouses. This transformation turns the data lake into a data warehouse—hence the term data lakehouse.
This is achieved by adopting:
Lakehouse Table Formats like Apache Iceberg, Apache Hudi, Delta Lake, or Apache Paimon, enabling Parquet files to act as structured, ACID-compliant tables optimized for analytics.
Lakehouse Catalogs like Apache Polaris, Nessie, Apache Gravitino, Lakekeeper, and Unity, which provide metadata tracking for seamless data discovery and access.
Managed Catalog Services (e.g., Dremio), which automate data optimization and governance, reducing unnecessary data movement.
Key Benefits of a Lakehouse Approach
✅ Lower costs by reducing data replication and processing overhead.
✅ Improved consistency by maintaining a single source of truth.
✅ Faster time to insight with direct access to analytics-ready data.
Challenges That Remain
Despite its advantages, a lakehouse alone does not completely solve all challenges:
- Migration Delays – Moving existing data takes time, delaying the full benefits of a lakehouse.
- Distributed Data Sources – Not all data resides in the lakehouse; external data remains a challenge.
- BI Tool Extracts – Users may still create redundant isolated extracts, increasing costs.
This is where the Dremio Lakehouse Platform fills the gap.
The Dremio Solution
Dremio is a lakehouse platform that integrates four key capabilities into a holistic data integration solution, addressing the remaining lakehouse challenges.
1. High-Performance Federated Query Engine
- Best-in-class raw query performance.
- Federates queries across lakehouse catalogs, data lakes, databases, and warehouses.
- Provides a centralized experience across disparate data sources.
2. Semantic Layer
- Enables virtual data marts without data duplication.
- Built-in wiki and search for dataset documentation.
- Standardizes business metrics and datasets across all tools.
3. Query Acceleration
- Reflections replace traditional materialized views and BI cubes.
- Raw Reflections (precomputed query results).
- Aggregate Reflections (optimized aggregations for fast analytics).
- Automatic query acceleration, with no effort required from analysts.
4. Integrated Lakehouse Catalog
- Tracks and manages Apache Iceberg tables.
- Automates maintenance and cleanup of data.
- Provides centralized, portable governance across all queries.
The Dremio Advantage
✅ Instant Lakehouse Benefits – Get the advantages immediately, even before full migration.
✅ Improved Consistency – Ensure a unified definition of business metrics.
✅ High-Performance Analytics – Federated queries + Reflections accelerate workloads.
✅ Automated Management – No manual cleanup of lakehouse tables needed.
✅ Centralized Governance – Unified access control across all tools and sources.
Conclusion
The data lakehouse represents a transformative shift in data architecture, solving long-standing challenges around data consistency, cost, and accessibility.
However, simply adopting a lakehouse format isn’t enough. Organizations need a lakehouse solution that integrates data management, acceleration, and governance to fully unlock the benefits.
Dremio provides that missing piece with:
✅ Federated query capabilities
✅ A built-in semantic layer
✅ Automated query acceleration
✅ A fully managed lakehouse catalog
With Dremio, organizations don’t just implement a lakehouse—they enhance it, unlocking its full potential for faster insights, better decision-making, and long-term cost savings.
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