The Storage-Compute Separation Everyone Talks About

If you've been in the big data related industry for the past few years, you've likely heard "storage-compute separation" thrown around in architecture discussions. The concept seems straightforward: separate your compute resources from storage, scale them independently, and voilà—you have a modern data platform.

But here's the reality check: most organizations implementing basic storage-compute separation quickly discover it's just the starting point, not the destination.

Let me walk you through what we've learned from real-world deployments and why the next generation of data platforms looks fundamentally different from what you might expect.

The Problem with "Good Enough" Architecture

Picture this: It's 3 AM, and your phone is buzzing with alerts. Your beautifully architected storage-compute separation—the one that passed every design review and impressed the board—is choking under real-world pressure.

Here's what's happening: Your overnight batch jobs are wrestling with real-time analytics for resources. The executive dashboard that needs to load in under 2 seconds is taking 30. Your data science team is complaining that their ML training jobs are being starved by the marketing team's customer segmentation queries. Everyone's pointing fingers, but the real culprit isn't any single team—it's your architecture.

You see, basic storage-compute separation makes a dangerous assumption: that all workloads are created equal. They're not. Your fraud detection system needs millisecond responses. Your monthly reporting can wait. Your recommendation engine requires massive parallel processing. Your compliance queries need guaranteed resources.

But your current setup? It's like having one highway for sports cars, delivery trucks, and emergency vehicles. Sure, they're all "vehicles," but treating them the same way creates chaos.

The frustrating part? You can't simply buy your way out of this problem. Adding more compute power is like widening that highway—it helps temporarily, but the fundamental traffic management problem remains unsolved.

The Solution: Three Architectural Breakthroughs

The problems we just discussed—resource conflicts, scaling bottlenecks, and operational complexity—aren't inevitable. They're symptoms of architectural limitations that modern platforms have solved through three key innovations.

1. Separate Metadata Management

Traditional systems tie metadata (table structures, permissions, data locations) directly to compute clusters. This creates a bottleneck: when multiple teams need the same data, they're forced to share the same compute resources.

Advanced platforms move metadata into its own independent service. Now your fraud detection team and marketing analytics team can access the same customer data simultaneously, each using their own optimized compute cluster. No more resource wars, no more performance interference.

2. Elastic Compute Scaling

Instead of guessing how much compute power you'll need and pre-purchasing it, modern platforms scale automatically based on actual demand. When your monthly reports start running, new compute nodes appear. When they finish, the nodes disappear.

This works because compute nodes are stateless and can run anywhere—on your EC2 instances, Docker Compose environments, or Kubernetes clusters—while data lives in object storage. The two scale independently, so you only pay for what you actually use.

3. Workload-Specific Clusters

Different jobs need different resources. Real-time fraud detection needs fast response times. Monthly reporting needs massive parallel processing. Data science experiments need specialized hardware.

Advanced platforms create separate compute clusters for each workload type. Your dashboard queries run on low-latency infrastructure. Your batch jobs run on cost-optimized nodes during off-peak hours. Each gets exactly what it needs.

Enhancing Storage-Compute Separation: Performance and Scale

While the three architectural breakthroughs form the foundation, modern data platforms need additional capabilities to deliver optimal performance and scale. Let's explore how these enhancements work together to create a truly effective storage-compute separation architecture.

Intelligent Caching and Multi-Cluster Warehouse

Storage-compute separation creates a new challenge: every data request now travels across the network to remote storage. While object storage has improved dramatically, this round-trip still introduces latency that can impact user experience.

The bigger issue emerges under heavy concurrent load. When hundreds of dashboard queries hit your system simultaneously, they all compete for bandwidth to the same storage layer. This creates bottlenecks that can bring even well-designed systems to their knees.

Modern platforms address these challenges through a three-pronged approach:

Local Disk Caching: Frequently accessed data stays close to compute nodes, eliminating most remote storage calls
Memory Caching: Hot data lives in memory for instant access
Multi-Cluster Warehouse: Instead of scaling up one massive cluster, the system scales out across multiple smaller clusters, each handling a portion of the concurrent load

This layered approach ensures that storage-compute separation doesn't sacrifice the performance your applications require.

Native External Tables: Unlocking Data Lakehouse Flexibility

In storage-compute separation architectures, the choice and capabilities of underlying object storage become critically important. Traditionally, many viewed object storage as suitable only for data archival and backup—inadequate for high-concurrency OLAP scenarios. However, technological advances and the evolution of cloud services have changed this perception dramatically.

Today, enterprises are increasingly adopting object storage solutions tailored to their specific needs:

Cloud Deployments: Services like AWS S3 provide the foundation for high-performance analytics workloads.

On-Premises Solutions: Organizations can build cost-effective, high-performance private object storage using open-source solutions. For instance, deploying MinIO on SSD-backed infrastructure delivers the performance needed for real-time data analysis and AI computing scenarios, while HDD-based storage handles batch analysis workloads efficiently.

Databend takes this foundation further with its "native external tables" approach, offering two powerful features for working with external data:

External Tables: These are tables that point to data stored in your own object storage (like AWS S3,Cloudflare R2, MinIO, etc.). The data remains in your storage while Databend provides the compute layer.

** Creating an External Table**

-- Connect to your S3 data

CREATE EXTERNAL TABLE population_data

(

   city VARCHAR,

   population INT,

   year INT

)

LOCATION = 's3://your-bucket/population/'

CONNECTION = (

   REGION = 'us-east-2',

   AWS_KEY_ID = 'your_key',

   AWS_SECRET_KEY = 'your_secret'

);

This functionality allows organizations to utlize storage-compute separation in an ideal way like:

• Store high-frequency concurrent data directly on high-speed object storage
• Leverage all-flash object storage for analytical workloads
• Combine these capabilities with multi-cluster architecture to achieve true lakehouse integration with elastic resources and rapid response times

Here's where it gets interesting: Databend Cloud lets you keep your data in your own storage buckets while providing a fully managed compute layer. You get enterprise-grade architecture, 99.9% availability, and automatic scaling—without nealy 0 migration cost and operational complexity.

The economics speak for themselves. Organizations are seeing 50% cost reductions compared to platforms like Snowflake. One retail client dropped from $40,000 to $18,000 monthly—same workloads, better performance, half the cost.

Turns out advanced architecture doesn't have to break the budget.

Zero Copy data migration with attach table

Here's a scenario every data team knows too well: your analytics team needs access to production data, but copying terabytes for every analysis creates a nightmare of version conflicts, storage costs, and stale insights. Meanwhile, your data engineers spend countless hours building and maintaining synchronization pipelines that break whenever schemas change.

Traditional data architectures force this painful choice between data freshness and operational complexity. But what if you could eliminate the choice entirely?

Databend introduce a game-changing capability: seamless data sharing without the overhead. Instead of moving data around, you only need to point table to your storage location.

Attach Tables: This feature lets you create logical connections between different Databend deployments, treating remote data as if it were local. Think of it as creating a symbolic link to data that lives elsewhere—no copying, no synchronization delays, no storage duplication.

The practical applications are transformative:

• Zero-copy migrations: Move from on-premises to cloud by gradually shifting compute while keeping data in place
• Instant analytics environments: Spin up read-only analytical workspaces that always reflect current production data
• Cross-environment collaboration: Development, staging, and production teams work with the same underlying datasets

The below SQL is all you need to connect another tenants' data with selected columns

-- Attach a table from your on-premises deployment to cloud

ATTACH TABLE cloud_population (city, population) 

's3://databend-doc/1/16/' 

CONNECTION = (

   REGION = 'us-east-2',

   AWS_KEY_ID = 'your_key',

   AWS_SECRET_KEY = 'your_secret'

);

The Path Forward

The evolution of storage-compute separation architecture is like injecting infinite vitality and elasticity into enterprise data infrastructure. From the initial decoupling of compute and storage, to multi-cluster high-concurrency support and object storage upgrades, to seamless data sharing within and between enterprises—each innovation reshapes the future of data processing.

Databend closely aligns with the evolution of cloud platform infrastructure, cleverly leveraging cloud resources to create simple, cost-effective architecture for users, serving as a solid bridge between users and cloud providers. Behind every technological breakthrough lies deep insight into users' core needs and innovative spirit.

Today, Databend has successfully deployed as a leading cloud-native storage-compute separation platform across multiple industries including high-frequency trading, biopharmaceuticals, data trading, gaming, and e-commerce. We've helped enterprises significantly reduce costs, improve efficiency, and unlock unlimited innovation potential.

The question isn't whether to evolve your data architecture—it's how quickly you can make the transition while minimizing disruption to existing operations. Organizations that recognize this early and adopt advanced architectures will have significant competitive advantages in terms of both cost and capability.

Looking ahead, as artificial intelligence and data lakehouses deeply integrate, storage-compute separation architecture will lead more industry-level applications and new models of data collaboration, opening a new chapter in the intelligent data era. The future of data platforms isn't just about storing and processing information more efficiently—it's about creating infrastructure that adapts to your business needs rather than constraining them.