5 Ways Dremio Makes Apache Iceberg Lakehouses Easy

Building a modern data platform often feels like navigating a maze of trade-offs. On one side, different teams want the freedom to use their preferred tools and environments. On the other, central engineering teams strive to unify data to reduce inconsistencies and manage costs. The result? A tangled web of duplicated data pipelines, silos, and governance headaches.

That’s where the data lakehouse enters the picture. By combining the openness of data lakes with the performance and management features of data warehouses, lakehouses offer a modular, scalable foundation. But with that flexibility comes complexity—managing catalogs, query engines, optimization jobs, and access controls can become a full-time job in itself.

Dremio helps cut through this complexity. It brings together key technologies like Apache Iceberg, query federation, semantic modeling, and autonomous performance tuning—all in a single platform. In this post, we’ll explore five ways Dremio makes deploying and operating an Iceberg-based lakehouse not just possible, but easy.

The Challenge of Siloed Data and Centralized Bottlenecks

In many organizations, data lives in silos. Different departments spin up their own data warehouses or analytics stacks, each optimized for their specific workflows. While this gives teams autonomy, it often leads to redundant data copies, inconsistent results, and rising infrastructure costs.

To address this, some companies try to centralize everything, pulling data from every system into a single platform managed by a central engineering team. But this approach introduces its own challenges. The backlog for new datasets grows, self-service becomes limited, and engineers become the bottleneck.

Data teams are stuck between two hard choices: maintain agility with silos or enforce consistency through centralization.

Lakehouses offer a third path. By decoupling storage from compute and embracing open table formats like Apache Iceberg, a lakehouse enables multiple tools to access the same data without copying it around. Teams get the freedom to use the tools they want while still maintaining a single source of truth.

The problem? Standing up a modular lakehouse stack requires wiring together catalogs, query engines, access controls, and optimization strategies—all while tuning for performance. That’s a lot of moving parts. This is where Dremio comes in. It simplifies the entire lakehouse experience by offering a unified platform designed to work with Apache Iceberg from day one.

Let’s walk through the five ways Dremio makes this all easier.

Query Federation: Bring All Your Data to the Table

Even in a well-designed lakehouse, not all your data will live in Apache Iceberg tables. You’ll likely need to work with external sources, like relational databases, NoSQL systems, or third-party APIs, that update frequently or aren’t worth ingesting into your lakehouse.

This is where query federation shines. Rather than forcing all data into Iceberg, Dremio lets you query data in place, wherever it lives. Whether it’s a transactional table in PostgreSQL, a time-series index in OpenSearch, or a real-time feed from MongoDB, Dremio allows you to combine that data with your Iceberg datasets in a single SQL query.

This means:

• You can avoid unnecessary data pipelines and duplication by querying external data directly
• Your analysts and engineers can work with a unified SQL interface across all sources

Federation also simplifies governance. Dremio integrates with major identity providers, so users can authenticate once and access both Iceberg and external data with consistent access policies. That’s one set of credentials and one set of permissions—streamlining compliance without slowing down access.

In short, Dremio doesn’t force you to choose between agility and architecture. You can maintain an open Iceberg lakehouse while still leveraging the rest of your data ecosystem.

Integrated Catalog: Organize Without the Overhead

At the heart of any lakehouse is the catalog—the system that keeps track of your tables, schemas, metadata, and access policies. It’s what makes your data discoverable, governable, and interoperable across tools. But managing a catalog can be a burden, especially when it's yet another standalone service to deploy, secure, and scale.

Dremio takes a different approach by bundling a fully integrated catalog into its platform. Built on Apache Polaris, the Dremio Enterprise Catalog supports the open Iceberg REST API, so it works seamlessly with engines like Spark, Flink, and others—no vendor lock-in required.

This catalog brings a lot of convenience:

• You don’t need to stand up and manage a separate catalog service—Dremio handles it for you
• Access controls and optimizations are applied consistently across tools and users

It also takes care of performance under the hood. Instead of manually scheduling compaction jobs or cleanup operations, the Dremio catalog automatically optimizes your Iceberg tables. That includes merging small files, cleaning up old metadata, and organizing data based on clustering keys—keeping your tables fast and your costs low.

With Dremio’s integrated catalog, managing Iceberg becomes a background task rather than a daily concern, so your team can stay focused on delivering insights, not infrastructure.

Built-in Semantic Layer: Make Data Meaningful and Accessible

Raw tables are great for machines, but not always for humans. Business users need curated views, clear definitions, and a way to find the right dataset without wading through hundreds of cryptic table names. That’s the role of a semantic layer: to bridge the gap between raw data and usable insights.

Dremio comes with a built-in semantic layer that lets you define reusable views, apply business logic, and document datasets—all without leaving the platform. This gives teams a shared vocabulary and a consistent experience, regardless of how technical they are.

Some key capabilities include:

• Defining and organizing virtual datasets (views) to represent business concepts like “revenue by region” or “active users last 30 days”
• Adding wiki-style documentation directly to each view so users understand how and when to use them

There’s also AI-powered semantic search. Instead of knowing the exact table name, users can search using natural language—like “monthly sales by product”—and Dremio will surface the most relevant datasets based on metadata and context.

And when it comes to security, Dremio supports both role-based and fine-grained access controls. You can control access not just at the dataset level, but down to specific rows and columns—ensuring that users see only what they’re supposed to.

The result is a more intuitive, governed experience that helps teams find, understand, and trust the data they work with every day.

Autonomous Performance Management: Speed Without the Tuning

Performance tuning in a lakehouse can quickly become a time sink. You need to compact files, configure caching, manage accelerations, and constantly monitor query workloads. If left unchecked, even well-architected systems can become sluggish and expensive to run.

Dremio tackles this by automating performance optimization across the board. When your Iceberg tables are managed by the Dremio catalog, maintenance becomes hands-off. Dremio takes care of file compaction, snapshot cleanup, and clustering—ensuring your tables stay query-friendly without manual intervention.

But the real magic comes from query acceleration through reflections. A reflection is an optimized, precomputed version of your data or a query result. Unlike traditional materialized views, reflections are:

• Automatically substituted into queries without users needing to reference them
• Updated incrementally and reused across workloads
• Managed autonomously by Dremio, which creates and drops them based on actual usage patterns

On top of reflections, Dremio adds multiple layers of caching. There’s a query plan cache for speeding up query compilation, a results cache for reusing answers to identical queries, and the columnar cloud cache, which stores frequently accessed data from object storage right on the compute nodes. That means fewer reads from S3 or other cloud storage—and lower compute bills too.

With all of this running behind the scenes, Dremio shifts performance from a manual task to an intelligent system. You get faster queries and lower costs without needing a full-time optimization team.

Flexible Deployment Options: Run Dremio Your Way

Every organization has different infrastructure needs. Some want the convenience of a fully managed cloud service. Others need to run workloads in a specific region, on-premises, or within their own Kubernetes environment for compliance or control.

Dremio supports both. You can choose between Dremio Cloud—a managed service where Dremio handles the operations—or Dremio Software, which you can deploy in any Kubernetes cluster across cloud or on-prem environments.

If you go with Dremio Cloud, the platform handles provisioning, scaling, upgrades, and monitoring. It’s designed for ease of use, so you can focus on building data products and delivering insights instead of maintaining infrastructure.

For teams opting for a self-managed setup, Dremio provides the tools to succeed:

• Observability features to monitor job activity, query performance, and resource usage
• A Well-Architected Framework to guide you in building reliable, efficient deployments

This flexibility allows you to align Dremio with your organization’s cloud strategy and operational preferences—whether you're optimizing for cost, compliance, or control.

Regardless of where or how you deploy, the experience remains consistent. Same query engine, same catalog, same performance features—just deployed your way.

Conclusion

Lakehouses promise a unified, open, and scalable approach to analytics—but getting there can be complex. From connecting diverse data sources and managing catalogs to optimizing performance and securing access, the operational load adds up fast.

Dremio simplifies all of it. By bringing together query federation, an integrated Iceberg catalog, a built-in semantic layer, autonomous performance tuning, and flexible deployment options, Dremio makes it easier to build and run a lakehouse without stitching together multiple tools.

The result is a platform that helps you move faster, reduce costs, and give teams consistent access to high-quality data, no matter how your data stack evolves.

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