Dremio — The Agentic Lakehouse

001 — This post provides a gentle introduction to the Dremio lakehouse platform.

I was introduced to Dremio a year ago, and since then I’ve been keen to explore how it enables high performance on open data architectures.

Dremio has become one of the most sought-after tools for Data Engineering teams due to its powerful features like Data reflections, Query Planning, Agentic AI support to query stored data, Data sovereignty and a low learning curve (which kept me motivated to explore it more 😉).

💡A Data Lakehouse Architecture consists of components that enables users to store raw (structured and unstructured) data on data lakes and query them just the way they would do from a data warehouse.

Five Layers of Data Lakehouse — AI Generated

What is Dremio 🤔?

Dremio is a Data Lakehouse platform that unifies data warehouse performance and governance with the scalability and openness of data lakes, enabling faster self-service analytics for data consumers.

Key Features

• 👉Self-Service Data Access — Users can query data directly at the source eliminating the need for ETL.
• ⚡Query Acceleration — Data reflection makes it possible to execute sub-second SQL queries even on large data sets.
• 💽Data Virtualization — Users can connect to multiple data sources like RDMBS, S3 storage, NoSQL etc. and query data across these sources. For instance, users can perform JOINS between a parquet file stored in s3 bucket with a collection available in Mongdb.
• 🔒Security and Governance — OIDC integration, fine-grained RBAC across multiple layers (data sources, workspaces, catalogs, and datasets), integration with Vault for credential management, row-level access control and column masking are some of the key features that enable strong security and governance.
• 🤖AI Agents — The latest version of Dremio comes with an integrated Agentic AI module that helps users to perform semantic searches on data stored in the platform using natural language queries. This makes data accessible to users without the need for writing complex SQL queries.

Architecture

Kubernetes based deployment architecture — source

There are two main classes of components:

• Core Service — Consists of Coordinator and Engine. These components are responsible for providing the core functionalities like query planning, job scheduling, metadata management and optimization. This responsibility is further shared between master coordinator and scale-out coordinator for a scalable architecture.
• Auxiliary Services — Each auxiliary service provides specific functionality to support the core services. Open Catalog provides the functionality of creating and managing Apache Iceberg tables, Semantic search provides AI based data discovery and Metastore manages schemas of datasets, tables etc.

How Dremio delivers high-performance analytics?

Under the hood, the following technologies work together to power Dremio’s performance and scalability.

1. Apache Arrow — A fast, in-memory columnar data format that lets systems process data efficiently without costly data conversions.
2. Arrow Flight — A high-speed data transport protocol built on Arrow for moving large datasets quickly between systems.
3. Gandiva — A runtime expression engine that speeds up query execution by compiling calculations into efficient native code.

AI Generated

4. Columnar Cloud Cache (C3) — C3 caches frequently accessed data from cloud storage (such as S3) on local NVMe disks of executor nodes in a columnar format, significantly speeding up query execution.

5. Vectorization Techniques — SIMD (Single Instruction, Multiple Data) Operations and Vectorized processing. Instead of processing data one value at a time, Dremio processes many values together in batches. First, the data is stored in a columnar format and then similar operations are grouped in a batch to process chunks of data (vectors) simultaneously.

More data processed per CPU cycle = lower latency and higher throughput.

AI Generated

How to get started?

Dremio is available as a:

• Dremio Cloud — A fully managed service provided by Dremio.
• Dremio Software — A deployable version of Dremio Platform on Kubernetes and OpenShift. Dremio Software has an Enterprise edition (with a 30-day trial license) and a community edition (with limited features).

More Information on different editions.

As of today, the quickest way to get started is to sign up for a 30-day free Dremio Cloud account, which includes $400 in credits and access to all features.

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Things to Consider Before Deploying Dremio

For an on-premises deployment of Dremio, while it is relatively easy to get started, some upfront planning is required. Below is my personal, non-exhaustive checklist of things to consider before deployment:

Storage

• Plan object storage capacity and growth
• Ensure low-latency access between Dremio and storage
• Encryption for data-at-rest
• Plan backup, retention, and disaster recovery

Compute

• Size coordinator and executor nodes properly
• Consider engine routing to isolate different workloads (refresh jobs, user queries etc.)
• Ensure sufficient resources for C3 and vectorized executions

Network

• High-bandwidth, low-latency connectivity to storage
• Firewall rules between data sources, BI tools, external credential managers etc.
• TLS encryption for data-in-transit

AI Agents

• Identify features requiring LLM token usage
• Define cost controls and usage limits
• Plan monitoring and reporting for token usage

Closing Notes

I am just getting started in the vast and evolving domain of data engineering. While the field presents a wide range of technical challenges, working with Dremio has helped reduce much of the initial complexity. By enabling direct querying on open data formats, providing a semantic layer for reusable logic, and abstracting away heavy ETL, Dremio has allowed me to focus on understanding how lakehouse architectures actually work in practice and has made it easier to connect architectural concepts with real-world data engineering workflows.

Future Work

In the next blog of Dremio series, I will delve deeper into the deployment of Dremio Software on a Kubernetes cluster. Stay tuned!

Thank you for taking the time to read this blog.