Accelerating Analytical Insight – The NetApp & Dremio Hybrid Iceberg Lakehouse Reference Architecture

Organizations are constantly seeking ways to optimize data management and analytics. The Dremio and NetApp Hybrid Iceberg Lakehouse Reference Architecture brings together Dremio’s Unified Lakehouse Platform and NetApp’s advanced data storage solutions to create a high-performance, scalable, and cost-efficient data lakehouse platform. With this solution combining NetApp’s advanced storage technologies with Dremio’s high-performance lakehouse platform, businesses can achieve faster insights, reduce costs, and simplify data management. 

This architecture leverages NetApp StorageGRID, ONTAP, and other NetApp offerings to address common challenges faced by businesses using data lakes, enhancing overall business efficiency. We’ll explore what the Dremio and NetApp Hybrid Iceberg Lakehouse reference architecture is, how it works, and the value it delivers to businesses.

NetApp Overview

NetApp’s offerings, including StorageGRID and ONTAP, provide scalable and flexible storage solutions that complement lakehouse environments by enabling businesses to scale storage independently from compute, optimizing resource utilization based on specific business needs. This architecture ensures that organizations can adapt their storage capabilities without being tied to compute resource scaling, delivering flexibility and cost efficiency. Key features of NetApp’s solutions include:

StorageGRID

NetApp StorageGRID is a software-defined, object-based storage solution that provides scalable and efficient storage for hybrid clouds. Supporting use cases across public, private, and multi-cloud environments, StorageGRID features native support for the Amazon S3 API. With innovations like automated lifecycle management, businesses can store, secure, and protect data over long periods at a low cost. Whether deployed on-premises or in the public cloud, StorageGRID is designed to meet a wide range of data management needs, ensuring cost efficiency through tiering of data. NetApp's StorageGRID is the preferred choice for object storage in Data lake and Lakehouse environments. 

ONTAP Data Management

NetApp ONTAP provides native support for simultaneous access via both NAS (Network Attached Storage) and Object Storage across all storage infrastructure. This integration offers seamless data mobility, disaster recovery, scalability, and high performance in hybrid and multi-cloud environments. When the data resides in ONTAP, it serves as a direct data source for Dremio, eliminating the need to move or copy the data.

Data Efficiency and Security

NetApp’s storage solutions offer advanced features like deduplication, compression, encryption, and data protection. These features ensure that businesses can reduce their storage footprints while securing their data from potential threats and failures. Additionally, StorageGRID supports the ability to transparently tier data dramatically reducing your TCO. This includes tiering from SSD to HDD, from OnPrem to services like Amazon Glacier and Azure Blob Storage allowing extensive data lifecycle management.

By utilizing NetApp’s solutions, organizations can create a flexible data fabric across their hybrid cloud environments, dynamically balancing data durability, performance, cost, and location to grow seamlessly with their business.

Dremio Overview

Dremio is a unified lakehouse platform designed to accelerate data analytics and AI by eliminating the need for traditional data warehouses and complex ETL processes. One of Dremio's core strengths is its ability to operate seamlessly across both on-premises and cloud environments, giving businesses the flexibility to manage their data wherever it resides. Whether deployed on-prem or in the cloud, Dremio's hybrid capabilities allow organizations to leverage the best of both worlds—maintaining control over on-prem data while scaling and optimizing workloads in the cloud. Some of the key features of Dremio include:

Unified Hybrid Analytics

Dremio provides easy-to-use self-service analytics through a universal semantic layer, allowing users to access and query data across on-prem and cloud environments in real-time. This hybrid approach reduces the burden of data engineers to manage data pipelines so they can spend time expanding the data platform, significantly reducing time-to-insight for business users while ensuring data remains accessible, no matter where it’s stored.

High-Performance Hybrid SQL Engine

Dremio’s tightly-integrated SQL query engine enables businesses to run complex SQL queries directly on data stored in both on-premises and cloud data lakes. This hybrid functionality ensures analytics workloads can be executed with high performance, delivering insights faster and at a fraction of the cost compared to traditional data systems. Whether data resides in local infrastructure or in the cloud, Dremio ensures seamless, fast query execution.

Hybrid Enterprise Iceberg Catalog

Dremio's catalog offers Git-like versioning that enables teams to create branches and tags, allowing for advanced data management across multiple tables. This feature supports multi-table transactions, enabling zero-copy environments where physical copies of data are not needed. With the ability to roll back all tables, disaster recovery becomes seamless and efficient. Tags provide historical points in the catalog, making it easy to reproduce past states of your data environment. Every commit in the catalog serves as an auditable history of changes, ensuring full transparency and traceability across your table management operations. This powerful versioning system simplifies data management and provides a robust foundation for development, testing, and production use cases in both on-prem and cloud environments.

Automated Data Optimization in Hybrid Environments

Dremio’s automated tools handle data optimization tasks such as compaction, indexing, and data cleaning, ensuring high performance and reliability across both on-premises and cloud data lakes. These optimizations are crucial in maintaining the high-speed performance of the Hybrid Iceberg Lakehouse architecture, allowing businesses to run their analytics workloads with minimal manual intervention, regardless of data location.

Open Standards and Hybrid Flexibility

Built on open-source technologies like Apache Arrow and leveraging Apache Iceberg, Dremio leverages open standards such as Apache Parquet, ensuring no vendor lock-in. This flexibility is especially valuable for hybrid deployments, as businesses can move or access data across on-prem and cloud environments without worrying about being locked into a specific provider. Dremio’s open, hybrid architecture future-proofs your data infrastructure, providing the flexibility to evolve as your data needs grow and change.

By enabling seamless integration and management of data across on-prem and cloud environments, Dremio’s hybrid lakehouse platform provides the flexibility, performance, and scalability that modern businesses need to accelerate their data analytics and AI initiatives.

Solution Overview

The Dremio and NetApp Hybrid Iceberg Lakehouse reference architecture is a framework for creating a powerful analytical solution for managing, accessing, and analyzing large datasets stored across any company's environment. A key value is that data can be on-premises, in the cloud and, most frequently, in both environments. By leveraging NetApp storage solutions, businesses can easily store, manage, and access data using NFS and S3 protocols, and object storage, while Dremio’s powerful lakehouse platform allows users to run analytical SQL queries on this data with sub-second performance and very high levels of concurrency. Dremio’s Unified Analytics capability of the solution allows data to be queried where it resides, both on-premises and in the cloud, with no need for complex and costly ETL (Extract, Transform, Load) processes. This enables organizations to accelerate time to insight and drive business value.

Implementing a Dremio & Netapp Hybrid Iceberg Lakehouse

To begin implementing the NetApp and Dremio Hybrid Iceberg Lakehouse, follow these steps to ensure a smooth migration and optimized setup:

Step 1: Deploy Dremio Using the Official Kubernetes Helm Chart

The first step is to deploy Dremio using their official Kubernetes Helm chart. This deployment will set up a production-grade Dremio environment, which will serve as the central data access layer for all users.

• Install Helm: Ensure you have Helm installed on your Kubernetes cluster.
• Deploy Dremio: Use Dremio's official Helm chart to deploy the Dremio platform on your Kubernetes infrastructure. This setup is essential to allow Dremio to connect to your current and legacy databases, data lakes, and data warehouses.
  Key resources:
  • Dremio Helm Chart Documentation
  • Follow the steps to configure your deployment, including setting up coordinators, executors, and user authentication.

Why this step matters: This deployment will provide your users with a consistent interface to access data from all your data sources, ensuring that they get familiar with Dremio as the central point of access from day one.

Step 2: Setup NetApp Storage

Once Dremio is up and running, the next step is to deploy and configure NetApp storage systems, such as ONTAP or StorageGRID. These systems provide the storage layer in your hybrid environment.

• Deploy ONTAP or StorageGRID: Set up your NetApp storage system to dynamically scale based on your needs.
• Configure S3 or NFS connections: Configure logical network interfaces, assign storage capacity, and set up NFS or S3 configurations depending on the type of NetApp storage you are using.
  Key steps:
  • Initialize the storage system.
  • Configure licensing and storage capacity.
  • Set up NFS or S3 configurations for hybrid cloud access.

Why this step matters: NetApp’s storage solutions allow your data to grow dynamically, and with the integration into Dremio, they ensure your users can access data without interruption, whether it's stored on-prem or in the cloud.

Step 3: Connect NetApp Storage to Dremio

With both Dremio and NetApp systems in place, it's time to connect them and start migrating data gradually.

• Add NetApp as a Data Source in Dremio: Set up your NetApp ONTAP or StorageGRID system as a data source within Dremio. This process includes:
  • Configuring an S3 source in Dremio.
  • Adding NAS (NFS) as a source within Dremio.
• Data Migration: Begin migrating your data from legacy systems to NetApp storage. The migration can be done incrementally, ensuring minimal disruption to end-users. Since Dremio remains the central interface for all users, their access to data remains unchanged before, during, and after the migration.

Why this step matters: Connecting NetApp storage to Dremio ensures a seamless user experience during data migration. Users continue to query data through Dremio while the underlying storage moves to NetApp, allowing you to take full advantage of NetApp’s scalable storage without downtime.

Step 4: Optimize and Configure the Hybrid Iceberg Lakehouse

With Dremio and NetApp fully integrated, optimize your environment for high performance and flexibility.

• Configure Dremio Coordinators and Executors: Tune Dremio’s settings to ensure coordinators and executors are optimized for hybrid workloads. Ensure data caching, spillover, and query planning are all aligned for efficient use of both on-prem and cloud storage.
• Manage Data with the Iceberg Catalog: Use Dremio’s Iceberg catalog to manage your data as code. Set up branches and tags to create zero-copy environments for testing and production, and use the versioning features to roll back data as needed, providing easy disaster recovery and an auditable history.

Why this step matters: Optimizing Dremio and NetApp settings will maximize performance across hybrid environments, ensuring that your data workflows are seamless and efficient.

Step 5: Validate the Solution

After setup, it's critical to validate the solution to ensure it performs as expected.

• Run SQL Query Tests: Execute a suite of SQL queries across your data sources to validate that Dremio can access and analyze data stored in NetApp storage. Use standardized queries such as the TPC-DS queries for comprehensive testing.
• Test Spillover to NetApp Storage: Ensure that Dremio can handle spillover for large datasets, seamlessly caching data on NetApp storage when in-memory limits are reached.

Why this step matters: Validating the setup ensures the solution meets your performance and scalability requirements, particularly for memory-intensive workloads where spillover to NetApp storage is crucial.

Combining Dremio and NetApp simplifies data management and accelerates analytics by leveraging the hybrid capabilities of both platforms. By following these prescriptive steps, you can migrate your data over time with minimal disruption and unlock the full potential of a Dremio and NetApp Hybrid Iceberg Lakehouse solution.

Value of the Dremio & NetApp Hybrid Iceberg Lakehouse Solution

The integration of the Dremio analytics platform with NetApp storage systems delivers significant value to businesses, enhancing performance, scalability, and cost-efficiency. Here are the key benefits:

1. Improved Data Management and Accessibility

The Dremio and NetApp solution improves data management by providing a unified platform for accessing, managing, and analyzing data across multiple environments. NetApp ONTAP and StorageGRID offer flexible storage options that work seamlessly with Dremio’s self-service analytics platform, ensuring that businesses can access their data from any location, at any time.

2. Performance Optimization

Dremio's SQL Query Engine, powered by Apache Arrow, is core to delivering the best price-performance for queries across all of a company's data. When using Dremio Reflections to accelerate query performance, organizations can achieve sub-second SQL query response times on their analytical workloads. When coupled with NetApp’s optimized storage solutions, businesses can achieve faster time-to-insight with minimal latency. This is particularly valuable for businesses dealing with large-scale datasets and requiring real-time or near-real-time analytics.

3. Scalability

Both Dremio and NetApp offer scalable solutions designed to grow with the business. NetApp storage systems are designed for petabyte-scale environments, and Dremio’s lakehouse platform can scale to handle any size datasets as well as concurrent users and queries. This ensures that businesses can scale their data environments easily and efficiently.

4. Data Security and Governance

Data security is a top priority for both Dremio and NetApp. Together, the platforms offer comprehensive security features, including encryption, access controls, and data governance tools, ensuring that sensitive data is protected. This is especially critical for businesses operating in industries with strict data governance regulations, such as finance and healthcare.

5. Cost Efficiency

One of the most significant benefits of this architecture is the potential for cost savings. By eliminating the need for data duplication and movement between storage environments, businesses can reduce costs associated with data management. Additionally, the benefit to many organizations moving off of legacy data lake environments to a modern Hybrid Iceberg Lakehouse reduces the need for expensive licensing, compute and storage resources, enabling businesses to perform analytics at a fraction of the cost of traditional solutions.

Customer Use Case

Numerous customers have already benefited from the Dremio and NetApp Hybrid Iceberg Lakehouse solution:

NetApp’s own Active IQ data analytics platform was modernized using this architecture, resulting in significant improvements in query performance and cost reductions. NetApp reduced its compute footprint and reduced query runtime from 45 minutes to just two minutes, highlighting the architecture’s ability to accelerate insights while reducing infrastructure costs.

A large global auto parts sales customer successfully leveraged the Dremio and NetApp Hybrid Iceberg Lakehouse solution to optimize their data management and analytics. The organization was able to improve and optimize existing data architecture and reduced time to insights from four weeks only hours. At the same time, they reduced troubleshooting and data management tasks from three days to hours while decreasing data platform & management costs by over $380,000.

Conclusion

The Dremio and NetApp Hybrid Iceberg Lakehouse reference architecture provides businesses with a powerful, flexible, and cost-efficient solution for managing and analyzing large-scale datasets. By combining NetApp’s advanced storage technologies with Dremio’s high-performance lakehouse platform, businesses can achieve faster insights, reduce costs, and simplify data management. Whether in hybrid or multi-cloud environments, this architecture is designed to scale with the business, ensuring long-term performance and flexibility in today’s data-driven world.