Feature stores for micro-apps: powering citizen-built recommendation apps

Hook: Let citizen builders ship recommendation apps — without rebuilding datasets

Non-developers are building micro-apps faster than platform teams can provision data. But every new dining or event recommender re-implements the same user and item datasets, costing time, cloud spend, and reliability. The solution: a central, governed feature store designed so non-developers can compose recommendations with drag‑and‑drop speed, predictable freshness, and enterprise-grade controls.

Executive summary: what this article delivers

This guide explains how to design a central feature store for micro-apps (like a Where2Eat dining recommender) that supports citizen developers. You’ll get an architecture pattern, concrete Delta Live Tables examples, metadata and governance rules, serving and freshness SLAs, and a UX pattern for a low-code composer that exposes reusable, governed features to non-developers.

The 2026 context: why now

In 2025–2026 we saw two converging trends accelerate platform demands: (1) a surge in “micro” or citizen-built apps driven by advanced LLM copilots and low-code tooling, and (2) enterprises standardizing on lakehouse platforms with features like Delta Live Tables and catalog governance. The result: enormous opportunity — and risk. Citizen builders can produce high-value apps quickly, but without a central feature strategy you get duplicated pipelines, inconsistent logic, stale recommendations, and security gaps.

Databases, vector stores, and feature stores matured in late 2025 to support hybrid workloads (real-time features + vector embeddings). Many teams now embrace a pattern where feature engineering is centralized, governed, and exposed through curated APIs and a low-code UX — enabling micro-apps to compose and iterate safely.

High-level architecture for micro-app-ready feature stores

At a glance, build a central feature platform with these components:

• Ingestion & streaming — CDC and event consumers to capture activity, impressions, and transactions.
• Feature computation — Delta Live Tables (DLT) pipelines to compute batch and incremental features with expectations and lineage.
• Feature repository — Governed Delta tables registered in the catalog (offline store) and an online store for low-latency reads.
• Metadata & catalog — Feature metadata (owner, freshness SLA, cardinality, tags, privacy, lineage) surfaced in a searchable catalog.
• Serving API & low-code composer — REST/gRPC endpoints + a UI that lets non-developers compose features into recommendation flavors.
• Governance & security — Column-level policies, audit logs, and certified features managed with your enterprise catalog (e.g., Unity Catalog).
• Monitoring & validation — Feature health dashboards, freshness alerts, data quality expectations (DLT), and rollback policies.

Feature design principles for reuse and reliability

To make features usable by non-developers, design them as modular, well-documented building blocks with strict metadata and SLAs.

1. Single source of truth: One canonical computation per feature. Register its table in the catalog and prevent duplicate derivations.
2. Composable primitives: Favor small, single-purpose features (e.g., cuisine_affinity_vector, avg_rating_28d) over monolithic model outputs.
3. Rich metadata: Attach owner, description, freshness, cardinality, PII flags, and lineage. This empowers search and governance.
4. Freshness SLA and tier: Classify features as real-time, near‑real‑time, or batch and expose that to the consumer UI.
5. Versioning and compatibility: Use semantic versioning for breaking changes and provide backwards-compatible views.
6. Privacy and masking: Embed masking rules in the metadata and enforce via catalog policies.

Implementing features with Delta Live Tables (practical example)

Delta Live Tables is ideal for centralizing feature computation with built-in expectations, incremental materialization, and lineage visibility. Below is a minimal example that computes a user cuisine affinity vector and writes a governed feature table.

# DLT pipeline (Python) - compute user cuisine affinity
import dlt
from pyspark.sql.functions import col, collect_list, explode, expr

@dlt.table(
    comment="User cuisine affinity vector (normalized counts)",
    table_properties={"pipeline.feature": "user.cuisine_affinity", "owner": "data-team"}
)
def user_cuisine_affinity():
    events = dlt.read("bronze_user_events")  # CDC source

    # Aggregate: counts per user per cuisine in last 90 days
    agg = (
        events
        .filter(col("event_ts") >= expr("current_date() - interval 90 days"))
        .groupBy("user_id", "cuisine")
        .count()
    )

    # Normalize to vector (sparse representation)
    vectors = (
        agg.groupBy("user_id")
        .agg(collect_list(expr("struct(cuisine, count)"))..alias("cuisine_counts"))
    )

    return vectors
  

The table property pipeline.feature and owner are lightweight metadata tags surfaced in your catalog. In late 2025 many teams extended DLT to support additional feature metadata (freshness hints, cardinality) — add those as table properties or catalog tags.

Registering and governing the feature

After materializing to a Delta table, register it in the enterprise catalog (e.g., Unity Catalog) and mark it as a certified feature. This enables lineages, access policies, and discoverability.

Designing the low-code composer for citizen developers

The UX goal: let a non-developer assemble a recommender by picking features and tuning lightweight signals. The composer should:

• Search features by name, tag, owner, and freshness
• Show metadata: description, freshness SLA, cardinality, examples, PII indicator
• Allow feature preview with sample rows and distributions
• Support basic transformations (normalize, weight, threshold) in the UI
• Persist the composed pipeline as a logical recipe (not code) that maps to a serving plan

Example UX flow for a dining app (Where2Eat): the user picks cuisine_affinity_vector, avg_rating_28d, and distance_score, drags sliders to weight each, and hits "Deploy". The platform compiles the recipe into a fast serving pipeline that reads from the online feature store.

Serving patterns: precompute vs on-the-fly vs hybrid

For recommendation micro-apps, serving latency and cost are key. Choose a serving pattern per feature:

• Precompute candidates — use for heavy candidate generation (e.g., collaborative filtering lists). Refresh periodically and store in an online table.
• On-the-fly scoring — compute lightweight scores at query time (distance, recency). Good for low-cardinality features.
• Hybrid — precompute heavy item vectors and user vectors; perform nearest-neighbor retrieval at request time using approximate NN (ANN) for latency.

For Where2Eat, a practical hybrid: precompute restaurant embeddings daily, update user embeddings in near real-time, and compute final reranking in the serving node using ANN + simple linear combination of other features. This minimizes duplicated work while keeping results fresh.

Making freshness explicit and enforceable

Freshness is a first-class citizen for micro-apps. Add these controls:

• Feature-level freshness metadata: last_updated_ts and max_age_seconds in catalog.
• Composer constraints: the UI should warn if you mix real-time and 24-hour batch features or prevent it if the app requires strict freshness.
• Runtime enforcement: the serving layer should validate freshness and either return cached values or fail-safe to last-known-good snapshot if a feature is stale beyond SLA.
• Monitoring: DLT expectations and feature monitors should send alerts when freshness degrades.

Governance and security for citizen-built micro-apps

Citizen builders must not bypass enterprise controls. Implement these governance layers:

• Catalog certification: features are certified by data owners before they appear in the composer.
• Access controls: Unity Catalog or equivalent enforces table- and column-level permissions and policies for PII masking.
• Approval workflows: New micro-apps that request sensitive features trigger a review where owners can add constraints or revoke access.
• Auditability: All compositions, deployments, and serving calls are logged for compliance and debugging.

Testing, validation, and safe rollout

Build tests at multiple layers:

• Unit tests for DLT transformations and edge cases.
• DLT expectations to validate distributions and nulls during pipeline runs.
• Shadow serving where the micro-app receives real traffic but recommendations are not surfaced, allowing A/B analyses of new feature compositions.
• Canary rollouts with throttled user groups, and automatic rollback on metric regressions.

Cost optimization: reuse is the most important lever

Duplicate pipelines equal duplicate costs. A central store reduces compute by sharing materialized features across apps. Additional cost controls:

• Tier features into online (hot), nearline (warm), and offline (cold) stores.
• Use materialized views or cached datasets for popular compositions.
• Leverage incremental updates in DLT to avoid full recomputes.
• Provide metering and cost-per-feature so owners can make tradeoffs explicit to citizen builders.

Case study: Where2Eat — from idea to production in days

Imagine Rebecca Yu builds Where2Eat as a micro-app for friend groups. Here’s a realistic, platform-enabled flow that avoids rebuilding datasets every time.

Features in the central store

• user.cuisine_affinity_v1 — sparse cuisine vector, updated near real-time, owner: data-team, freshness: 1h
• item.restaurant_embedding_v2 — nightly item embeddings for ANN retrieval, owner: ml-team, freshness: 24h
• user.recent_visits_count_14d — 14-day aggregation, owner: analytics, freshness: 1h
• item.distance_bucket — precomputed distance bucketing, owner: geo-team, freshness: 6h

Rebecca uses the composer to pick the three features and choose weights. The platform compiles a recipe into:

1. ANN lookup using item.restaurant_embedding_v2 and user.cuisine_affinity_v1 to produce 200 candidates.
2. Join candidates with user.recent_visits_count_14d and item.distance_bucket in the online store.
3. Final scoring: linear combination with weights from the composer UI followed by business rules (e.g., exclude closed restaurants).

All joins read from the online store (or a warm cache). The serving code validates each feature’s freshness metadata and falls back to the offline snapshot if needed. The result: Where2Eat launches in days, reuses enterprise features, and avoids ad-hoc datasets.

Advanced strategies and predictions for 2026+

Expect these platform trends through 2026:

• Feature marketplaces: internal catalogs functioning like marketplaces where teams publish certified features with SLAs and cost metrics.
• LLM-enabled feature discovery: natural language queries against the feature catalog ("give me a feature that measures dining preference") with automated suggestion of compositions.
• Automated feature synthesis: platforms will propose candidate features or embeddings derived from raw tables using automated pipelines, subject to owner review.
• Lineage-aware prompt engineering: generating safe prompts for LLMs using feature lineage to avoid leaking PII or stale data.
• Tighter integration of vector stores: embeddings treated as first-class features with cataloged freshness and lifecycle policies.

Actionable checklist: implement a micro-app-ready feature store

1. Inventory existing signals and tag candidate features with owner and freshness hints.
2. Choose Delta Live Tables for canonical, incremental feature computation and add DLT expectations for quality gates.
3. Register feature tables in your enterprise catalog and add metadata: description, SLA, cardinality, PII flags.
4. Implement an online store for low-latency reads and tier features by access pattern.
5. Build a low-code composer UI that surfaces metadata, previews, and enforces freshness constraints.
6. Enforce governance: certification process, access workflows, audit logs, and masking rules.
7. Instrument monitoring: feature freshness dashboards, DLT job alerts, and shadow-serving A/B tests.
8. Expose versioned APIs for micro-apps to fetch recipes and feature vectors.

Practical pitfalls and how to avoid them

• Pitfall: Allowing ad-hoc copies of features. Fix: Block unaudited writes to the feature namespaces and surface certified features first.
• Pitfall: Mixing features with incompatible freshness. Fix: Composer enforces freshness compatibility rules and warns users with tradeoff explanations.
• Pitfall: No ownership, so no one fixes breakages. Fix: Require feature owner metadata and SLA with on-call rotation.

Closing: why central features power faster, safer micro-apps

Citizen-built micro-apps are a major productivity win, but without a feature-first platform they produce technical debt and inconsistent recommendations. A well-designed feature store — governed, cataloged, and exposed via a low-code composer — enables non-developers to assemble high-quality recommendation experiences quickly while preserving control over freshness, privacy, and cost.

"Once vibe-coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps." — anecdote driving the micro-app trend in 2025

Next steps (clear call-to-action)

Ready to enable citizen-built recommendations in your organization? Start with a 4-week sprint: 1) catalog and certify 10 core features, 2) implement DLT pipelines with expectations, and 3) prototype a composer that exposes those features to non-developers. If you want a reference implementation, reach out for a workshop or request the Where2Eat demo repo and pipeline templates.

Want the demo code and an implementation checklist tailored to your stack? Contact our platform engineering team to schedule a 1:1 workshop.

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