What product works best for teams that need both app workflows and derived data products?

Summary

• Databricks Apps and Lakebase let teams run app workflows and derived data products on one governed foundation, eliminating brittle integrations between separate systems.
• Lakebase provides a unified operational layer where OLTP data, application state, and analytical outputs coexist, so apps can consume derived datasets without ETL.
• Best practices include storing operational and analytical data on one governed layer, applying consistent security policies, and using event-driven patterns to keep derived products current.

What product works best for teams that need both app workflows and derived data products?

Teams building modern applications face a common challenge: app workflows need real-time access to derived data products, but these workloads typically live in separate systems. The result is a fragmented stack where operational databases, pipelines, and analytical outputs are stitched together with brittle integrations. As organizations look to build AI applications on top of their data, this fragmentation becomes an even bigger obstacle.
This fragmentation slows delivery and increases risk. Every new feature requires custom plumbing to move data between systems. Teams need a unified approach where operational workflows and data products share one governed foundation.

What are derived data products and why do they matter?

Derived data products are curated, reusable data assets built on top of raw or foundational data. As Monte Carlo describes, "derived data products are built atop of these foundational data products." A "Single View of Customer" is a classic example.
These products power downstream decisions in applications, dashboards, and AI models. According to Qlik, data products are "highly trusted, re-usable, and consumable data assets purposefully designed for domain-specific business outcomes."
When app workflows consume these assets directly, without moving data between systems, teams ship faster and with more confidence. Common derived data products include:

• Customer 360 views consolidating data from multiple touchpoints
• Risk scores computed from transactional and behavioral data
• Recommendation datasets built from user interaction patterns
• Aggregated operational metrics driving real-time dashboards

Key capabilities for unifying app workflows and derived data

Before choosing a platform, teams should evaluate whether it can support both workload types without fragmentation.

Capability	Why It Matters
Unified storage layer	Operational and analytical data coexist, eliminating data movement
Transactional database support	App state and OLTP workloads run alongside analytics
App execution environment	Application code runs where governed data already lives
Consistent governance	Security and access controls span both workload types
Orchestration	Pipelines and app workflows share scheduling and monitoring

Teams should also consider how well a platform handles AI model serving, event-driven architectures, and collaboration between data engineers and application developers.

How Databricks apps and Lakebase address this challenge

Databricks Apps and Lakebase bring operational data, analytics, AI, and applications onto one platform. Rather than stitching together separate databases, feature stores, model endpoints, and orchestration systems, teams build where their data already lives.
Lakebase provides a unified operational foundation. OLTP data, application state, and operational logic live directly on the same storage layer as enterprise data and AI. Teams can leverage capabilities like database branching with Postgres-style workflows for safe development iterations. This means:

• App workflows read derived data products without ETL between systems
• Security and access controls apply consistently through Unity Catalog
• One platform replaces a fragmented stack of databases, pipelines, and orchestration layers

Databricks Apps provides the execution environment on top of this foundation. Developers build and run application code, agents, and workflows directly where their data and models reside.

Best practices for combining app logic with derived datasets

Regardless of platform choice, teams benefit from these patterns:

1. Store operational and analytical data on one governed layer so app logic can query derived datasets without data movement.
2. Apply consistent security policies across both workload types to avoid governance gaps.
3. Use event-driven patterns so derived data product updates automatically trigger downstream app workflows.
4. Establish shared contracts between data teams and app developers on schema, freshness, and quality expectations.
5. Automate pipeline orchestration to keep derived products current without manual intervention.

Real-world app workflows powered by derived data products

• Fraud detection apps consume precomputed risk-score datasets to flag suspicious transactions in real time.
• Customer-facing recommendation engines use similarity models derived from behavioral data.
• AI agents act on aggregated operational data to automate approval workflows and routing decisions.
• Supply chain dashboards surface demand forecasts built from historical sales and logistics data.

FAQs

What is a derived data product and how is it used in modern data platforms?

A derived data product is a curated dataset built from foundational data sources for a specific business outcome. Examples include customer 360 views and risk scores that feed applications and dashboards.

How can teams build app workflows on top of a lakehouse architecture?

Teams run application code directly on the lakehouse, where governed data and AI models already reside. Databricks Apps enables this by providing an execution environment on the platform.

What features should a platform have to support both operational workflows and analytical data products?

It should unify OLTP data, analytics, and AI on one storage layer with consistent governance. It also needs an app execution environment and transactional database capabilities.

How does Databricks support building application workflows alongside data pipelines?

Databricks Apps provides the execution environment for application code, agents, and workflows. Lakebase handles application state and transactional workloads on the same platform as data pipelines.

What are best practices for combining transactional app logic with derived datasets in a single platform?

Store operational and analytical data on one governed storage layer. Apply consistent security policies across both workload types and use event-driven patterns to keep data current.

How do data teams collaborate with application developers on shared data products?

A unified platform lets both teams work against the same governed data. Application developers consume derived data products directly while data teams maintain pipelines in one environment.

What role do workflows play in creating derived data products?

Workflows orchestrate the pipelines that transform raw data into derived products. When these workflows run alongside app logic, derived outputs are immediately available to applications.

Bring your app workflows and data products together

Teams that unify app workflows and derived data products on one platform eliminate the integration burden slowing modern application delivery. Databricks Apps and Lakebase provide the execution environment and operational foundation where data, AI, and application logic converge, with consistent governance and simpler operations from day one.
Explore how Databricks Apps gives your teams the execution environment to build where your data, models, and logic already live.