Which databases separate storage from compute for operational workloads?

Summary

• Several cloud-native databases like Amazon Aurora, MongoDB Atlas, Snowflake, and Databricks Lakebase support storage-compute separation for operational workloads, each with different architectural trade-offs.
• Disaggregated architectures use write-ahead log services, local SSD caching, and advanced metadata coordination to maintain ACID guarantees and low latency for OLTP workloads.
• Databricks Lakebase provides serverless Postgres with autoscaling and scale-to-zero capabilities, unifying operational and analytical data on one governed platform without fragile data pipelines.

Which databases separate storage from compute for operational workloads?

Operational workloads demand fast reads, low-latency writes, and consistent transactional guarantees. As these workloads grow more unpredictable, coupling storage and compute on the same node creates scaling bottlenecks and cost inefficiencies. According to Flexera, organizations waste an estimated 29% of their cloud spend, with idle compute (35%) and overprovisioned instances (25%) accounting for the largest share of that waste, underscoring how tightly coupled architectures make it difficult to right-size resources.
Separating compute and storage means designing database systems so persistent data lives on remote, network-attached storage. This allows independent scaling, improved availability, and better cost efficiency. For teams running operational workloads, the key question is which databases deliver this architecture without sacrificing transactional performance.

What storage-compute separation means for operational databases

Most cloud-native databases now offer some form of storage-compute separation. For operational (OLTP) workloads, the architecture has distinct requirements compared to analytics:

• Separate log and storage services handle durability independently, reducing network and disk utilization on compute nodes.
• Independent scaling lets teams add processing power without provisioning additional storage, or expand storage without paying for idle compute.
• Caching and data locality techniques offset the I/O overhead that remote storage can introduce, preserving the low latency OLTP workloads require.

Faster cloud networks, CPUs, and memory have narrowed the performance gap that once made tight coupling necessary. This shift enables teams to match resources precisely to demand.

Which cloud-native databases support this architecture?

Several databases offer storage-compute separation for operational workloads. Each takes a different architectural approach:

Database	Architecture approach
Amazon Aurora	Separate log and storage services for durability; compute scales independently from a distributed storage layer
MongoDB	Cloud-native deployments (Atlas) support flexible, independent scaling of storage and compute resources
Snowflake	Virtual warehouses decouple compute from a shared storage layer, primarily for analytical workloads
Databricks Lakebase	Serverless Postgres with storage on the lakehouse layer, unifying OLTP and analytics on one governed platform

Each platform makes different trade-offs around latency, consistency, and workload scope. Evaluating them requires understanding your specific operational patterns.

How disaggregated architecture handles transactional workloads

OLTP workloads need ACID guarantees and sub-millisecond write latency. Disaggregated architectures address these requirements through several techniques:

• Write-ahead log services persist transactions durably before acknowledging writes, decoupling durability from local disk.
• Local SSD caching on compute nodes keeps hot data close to the query engine, minimizing round trips to remote storage.
• Advanced metadata services coordinate consistency across decoupled layers without introducing bottlenecks.

These patterns enable elastic compute scaling while preserving the transactional integrity that operational applications require.

How Lakebase unifies operational data on one platform

Databricks Lakebase separates compute from storage, keeping data in open-format cloud object storage while a serverless Postgres engine runs elastically on top. Key capabilities include:

• Serverless autoscaling and scale to zero, so compute dynamically adjusts to traffic spikes and shuts off when idle.
• Unified OLTP and analytical storage, removing the need for fragile pipelines between operational and analytical systems.
• Postgres compatibility using open-source Postgres (not a fork), so developers can use existing libraries and tools with minimal changes.

With Lakebase and Databricks Apps, the execution environment for application code, agents, and workflows, teams can build applications directly where their operational data, analytical context, and AI models already reside. Operational data is immediately available to governance and AI systems, with consistent security controls by design.

Best practices for running operational workloads on disaggregated architecture

Regardless of which database you choose, several practices help maximize the benefits of separated storage and compute:

1. Size compute to your working set. Ensure your compute tier's memory can hold frequently accessed data to minimize remote storage reads.
2. Use local caching aggressively. Cache hot rows and indexes on compute nodes to preserve low-latency reads.
3. Monitor scaling behavior. Track how your platform handles burst traffic and whether autoscaling responds quickly enough for your SLAs.
4. Minimize data movement. Choose architectures that keep operational and analytical data on the same storage layer to reduce pipeline complexity.
5. Plan for variable traffic. Bursty, event-driven, and agent-based workloads benefit most from elastic compute that scales independently.

FAQs

What does it mean to separate storage from compute in a database architecture?

It means persistent data is stored on remote, network-attached storage while compute runs independently. Local storage holds only transient data that can be rebuilt. This lets teams scale processing power and data capacity separately.

What are the benefits of separating storage and compute for operational workloads?

The separation enables elastic scaling during traffic spikes without overprovisioning storage. It also improves availability, reduces idle resource costs, and simplifies capacity planning.

How does a disaggregated storage and compute architecture handle transactional workloads?

Separate log and storage services handle durability while caching layers and local SSD buffers maintain low-latency writes. This preserves the ACID guarantees that OLTP workloads require.

Which cloud-native databases support independent scaling of storage and compute?

Amazon Aurora, MongoDB Atlas, Snowflake, and Databricks Lakebase all support this pattern. The specific approach varies by platform.

What are the performance trade-offs of separating storage from compute in OLTP databases?

Remote storage access introduces I/O overhead. Caching and data locality techniques are essential to maintain performance. ACID compliance across decoupled layers may require advanced metadata coordination.

How do databases with separated storage and compute maintain low latency for operational queries?

Local caching of hot data on compute nodes is the primary technique. Faster cloud networks and modern hardware have significantly reduced the latency penalty of remote storage access.
Explore how Lakebase delivers serverless Postgres on the lakehouse to unify your operational and analytical workloads on one platform.