How Databricks Enables Decoupling in Modern Data Architectures

Decoupling in data solutions means structuring your architecture so that changes in one area don’t trigger cascading modifications across the entire system. This design principle is critical for building maintainable and future‐proof systems.

In this article, we explore how Databricks naturally supports decoupling across four key aspects,, which I consider essential based on my experience: Decoupling Storage from Compute, Decoupling Execution, Triggers, and Data Processing, Decoupling Governance from Compute and Data, and Decoupling Machine Learning from Data Processing.

I beleive, these aspects demonstrate how Databricks helps organizations build agile and future-proof data solutions.

1?? Decoupling Storage from Compute

Interestingly, many modern data platforms still suffer from tight coupling between storage and compute, forcing users into rigid scaling patterns and unnecessary reconfigurations.

To understand what I mean, take AWS Redshift (a fully managed cloud data warehouse service from AWS) as an example. While Redshift does offer a way to scale storage and compute independently (e.g., RA3 instances), the process is not as seamless as in some truly decoupled architectures.

Similarly, in Azure Synapse Dedicated SQL Pools, storage optimizations like partitioning, indexing, and distribution strategies are tightly linked to compute performance. If you think to modify data storage that can require workload redistribution and query re-optimization.

This tight coupling limits flexibility and increases operational complexity. Data teams looking to optimize partitioning, indexing, or data distribution often need to adjust compute resources (e.g., resizing DWUs in Synapse)s. Furthermore, in some cases, because storage and compute are tightly connected, processing the same dataset across different engines (e.g., SQL and Spark) often requires data movement or duplication.

This is why architects designing modern data systems prioritize decoupling storage and compute. Independent scaling of storage and compute is a key benefit of this! The ultimate goal is providing the ability to upgrade storage without impacting compute resources and vice versa.

Independent scaling of storage and compute eliminates rigid constraints, allowing systems to scale efficiently and adapt to changing needs.

?? How Databricks Delivers Independent Scaling of Storage and Compute

Databricks tackles the challenge of scaling storage and compute independently by building on a cloud-native, decoupled architecture. Unlike traditional data platforms that link storage and compute, Databricks allows organizations to scale each component separately. We can understand that through three key aspects.

1. Separation of Storage and Compute

At the foundation of Databricks is Delta Lake, which stores data on cloud object storage based on user preference (AWS S3, Azure Data Lake...). Because storage is completely independent of compute, clusters can be resized, paused, or terminated without affecting stored data.

Of course, other platforms, such as Google BigQuery and Snowflake, also offer storage-compute separation, but Databricks differentiates itself by using an open format (Delta Lake). This allows for broader compatibility with various compute engines while avoiding vendor lock-in, something proprietary storage layers may not provide.

2. Dynamic Compute Scaling

Databricks clusters auto-scale based on workload demand, dynamically adding or removing resources. When workloads spike, the platform increases compute capacity, and when demand drops, it scales down or shuts off unused clusters to optimize costs.

But wait, you might ask two key arguments:

First, "how is dynamic compute scaling relevant to decoupling at the first place?"

My answer is that while the presence of dynamic compute scaling alone doesn’t confirm decoupling, we can infer that dynamic compute scaling often indicates a decoupled system.

You might also say, "Well, dynamic scaling is a basic cloud principle!"

And that’s true! For instance, Snowflake and Redshift RA3 also support auto-scaling, but how they scale differs, which becomes clear in real-world use.

A key lesson I’ve learned as an architect is that the right question isn’t whether a platform supports scaling (because the answer is always yes), but how it supports scaling!

For instance, in Snowflake, scaling happens at the warehouse level, meaning users must manually configure multi-cluster warehouses. When load increases, additional clusters spin up within predefined limits, but individual node resources within a cluster remain fixed, which can lead to over-provisioning.

Databricks, on the other hand, offers more granular control. Instead of scaling entire clusters, users can set minimum and maximum worker limits per cluster, enabling dynamic resource allocation within a single cluster without spinning up additional clusters. This reduces over-provisioning and ensures resources scale efficiently based on actual workload demands.

Another advantage of Databricks is automatic cluster termination, which shuts down idle clusters without manual intervention, further optimizing costs. In contrast, Snowflake requires explicit configuration to suspend virtual warehouses after inactivity.

These subtle but crucial differences matter when handling varied workloads. I find Databricks' auto-scaling is particularly effective for batch processing, streaming analytics, and ML, where compute demand fluctuates constantly.

3. Workload Isolation for Better Performance

Databricks enables separate clusters for different workloads, ensuring that tasks like ETL processing, ad-hoc analytics, and ML model training do not compete for the same compute resources (resource contention). This enables that each workload runs in its own optimized environment without interference.

You can still argue that other platforms, like Snowflake, also support multi-cluster compute, allowing multiple clusters to handle workloads concurrently. However, because Snowflake's compute is structured around virtual warehouses, scaling focuses on query concurrency rather than workload specialization.

In contrast, Databricks extends this flexibility further by enabling clusters optimized for different programming languages (Python, Scala, SQL) and processing engines (Apache Spark, Photon, MLflow). This means that AI/ML workloads, streaming data processing, and SQL queries can run in dedicated, isolated environments, ensuring that high-intensity workloads do not slow down interactive queries.

2?? Decoupling Execution, Triggers, and Data Processing for Flexible Workflows

In traditional ETL architectures, data processing and orchestration are tightly coupled, making pipelines rigid and complex to maintain. Any change in ETL logic, such as modifying ingestion methods or transformations, often requires updates to the orchestration layer. Additionally, changes in upstream data can trigger cascading modifications across multiple pipeline stages.

If you’ve worked with Apache Airflow, you’ve likely encountered cases where adjusting ETL logic requires modifying DAG structures. Since DAGs explicitly define task dependencies, even small changes, like adding a transformation step or adjusting ingestion, often require editing the DAG code itself. Similarly, in Azure Data Factory (ADF), while pipelines are designed visually, they still tie processing logic to orchestration workflows. This means modifying an ETL step, may require restructuring the entire pipeline.

I totally agrree that there are a lot of optimizations and smart ways to make this less coupled, but my point is that such tight coupling can result in cases where minor changes demand reworking dependencies, redeploying jobs, and updating failure-handling mechanisms.

Furthermore, dependencies extend beyond orchestration, if raw data ingestion changes, adjustments may be required all the way to business-ready reporting layers.

This is why decoupling processing from orchestration is crucial. Architects aim to modify ETL logic, adjust scheduling triggers, and refine transformations independently, without affecting the entire workflow.

Modern platforms address this by leveraging APIs, event-driven execution, and layered data architectures that isolate different processing stages. In the following sections, we’ll explore how Databricks enables this aspect of decoupling.

ETL should evolve without breaking workflows! Decoupling processing from orchestration makes that possible!

?? How Databricks Decouples Execution, Triggers, and Data Processing

1. REST API & Webhooks for Job Execution:

Databricks breaks this dependency by allowing jobs to be triggered externally or scheduled internally, without embedding processing details into orchestration workflows.

With Databricks REST APIs and webhook support, external tools like Apache Airflow and ADF can start jobs in Databricks without managing how they run internally. This means that ETL logic can evolve independently.

This decoupling does not mean Databricks "removes" scheduling, it enhances flexibility. Databricks allows jobs to be scheduled internally when needed (I still beleive this is the popular approach to schedule jobs in Databricks) while also enabling external orchestration tools to trigger jobs dynamically without controlling how they run.

Whether jobs are scheduled within Databricks or triggered by an external orchestration tool, the core processing logic remains decoupled from scheduling.

2. Event-Driven Processing:

Databricks also decouples job execution from orchestration systems by allowing jobs to be triggered dynamically by events, such as changes in data or updates to ETL scripts, without needing to modify the entire orchestration layer.

Databricks integrates Kafka and Event Hubs, and uses Delta Live Tables to allow jobs to be triggered by data events in real-time, without the need for predefined scheduling. This dynamic execution ensures that data is processed as soon as it’s available, without waiting for the next scheduled run.

3. Medallion Architecture for Decoupling Data Processing Stages

Databricks’ Medallion Architecture organizes data into three layers: Bronze (raw), Silver (cleansed), and Gold (business-ready). This layered approach ensures that changes in one layer don’t disrupt the others. For example, altering data sources in raw ingestion (Bronze) layer, do not require changes to the business logic or reporting (Gold) layers.

This design decouples data processing stages, allowing teams to modify or optimize each layer independently reducing the impact of changes.

It’s important to note that this Medallion Architecture is a reference framework, and additional layers or logic can be incorporated based on the specific needs of the organization for more customization.

4. Delta Lake Schema Evolution for Flexibility in Data Modeling

Databricks' Delta Lake offers automatic schema evolution, making it highly adaptable to changes in data. When new columns are added or data types are modified in the source data, Delta Lake can handle these changes seamlessly without disrupting downstream queries or processes.

3?? Decoupling Governance from Compute and Data

In traditional data architectures, governance and security are often tightly coupled with compute resources and data storage, creating challenges when changes need to be made.

For example, in legacy systems like Microsoft SQL Server, security policies, access controls, and compliance requirements are usually embedded within the compute infrastructure. This means that modifying access policies or enforcing compliance changes often requires updates to both the compute environment (SQL Server itself) and the storage systems (where the data resides). This tight coupling introduces risks and inefficiencies and as systems grow, this becomes harder to manage.

A well-architected system seeks a more flexible and scalable approach, where governance and security are decoupled from the compute and data infrastructure. Ideally, changes to governance policies should not require modifications to the underlying compute clusters or storage systems.

A centralized governance framework that enforces policies independently of the compute infrastructure enables this flexibility. Databricks addresses these challenges, offering a centralized governance solution that decouples security from compute and storage.

?? How Databricks Decouples Governance from Compute and Data

Unity Catalog in Databricks provides a powerful solution to decouple governance from compute and data systems. It acts as a centralized governance layer that ensures consistent enforcement of security policies across multiple workspaces and environments.

This means that access control, auditing, and data lineage are all managed independently of the underlying compute infrastructure, allowing security policies to be modified or updated without needing to change the compute or data storage systems themselves.

In addition to Unity Catalog, Databricks integrates with external Identity and Access Management (IAM) systems, such as Microsoft Entra ID and AWS IAM. This integration externalizes authentication and permissions management, meaning that changes in security or user access policies can be made in external IAM systems without requiring any modifications to the Databricks compute clusters or data systems. This provides further decoupling of governance from the infrastructure, making it easier to implement changes to security policies in a centralized manner.

4?? Decoupling Machine Learning from Data Processing

In data platforms with tight coupling, data processing and ML workflows are often integrated into a single pipeline, making it difficult to modify or scale one aspect without affecting the other.

For instance, in a legacy system, when a company wants to adjust its data transformation logic (e.g., adding a new feature or modifying a transformation step), this change often requires retraining the ML model or updating the compute resources tied to the model training. This results in extensive reconfigurations across both the data pipeline and ML workflows.

From an architect's perspective, a decoupled approach is where data processing and ML workflows evolve independently. The ideal setup allows teams to modify data transformations or scale data pipelines without disrupting model development or reconfiguring the underlying infrastructure.

?? How Databricks Addresses Decoupling ML from Data Processing

Databricks offers several features that decouple ML workflows from data processing pipelines.

1. MLflow for Experiment Tracking & Model Registry

Databricks’ MLflow provides a central repository for experiment tracking and model versioning. By separating model training from model deployment, it allows data scientists to track experiments and store models independently of the data pipeline.

This means a lot in practice! First, data scientists can experiment more freely, testing different models, algorithms, and hyperparameters without the fear of disrupting the entire data pipeline. Each iteration can be tracked separately, making it easy to compare the performance of different model versions.

Second, model updates and retraining (MLOps) can happen without requiring changes to the data pipeline. If a model needs to be retrained with new data or optimized for better performance, this can be done independently of the data transformation or ingestion steps.

Lastly, deployment and training are decoupled, meaning that once a model version is trained and tested, it can be deployed to production without affecting the data pipeline or other model versions under development.

2. Feature Store: Decoupling Feature Engineering from Model Training

I know there are many "hot takes" on the Feature Store capability, but it still offers a valid decoupling benefit in ML workflows. Databricks' Feature Store enables data engineers and data scientists to manage and share engineered features independently from the model training process. By storing features centrally, the feature store allows them to be reused across multiple models.

This decoupling allows data scientists to access pre-built features for their models without repeating complex transformation logic. This not only reduces redundancy but also ensures that models are built with consistent data across environments, accelerating model development and reducing the risk of inconsistencies.

Conclusion

In this article, we explored the concept of decoupling in modern data architectures, particularly focusing on how platforms like Databricks enable greater flexibility and scalability by decoupling key components such as storage, compute, governance, and ML workflows.

We examined how traditional systems often face challenges due to the tight coupling of data processing and orchestration, which makes even small changes in the pipeline disruptive. Databricks' ability to decouple these layers offers significant benefits.

Key features, such as auto-scaling, Unity Catalog, MLflow, Feature Store, and the Medallion Architecture, help streamline workflows by ensuring that different aspects of the pipeline can evolve without disrupting other components.

While we’ve covered several key aspects of Databricks' decoupling capabilities, there are still other features and integrations that weren’t addressed here. As you continue learning about Databricks, think about how decoupling is integrated into the platform. Features such as Delta Live Tables, Runtime Environments, Collaborative Notebooks, and Databricks Repos.

I hope this helps you understand how Databricks shows that platforms can adapt to modern business needs by providing a modular, flexible architecture that enables continuous improvement without the limitations of tightly integrated systems.