Fixing Data Silos to Scale Enterprise AI: A Cloud Query Playbook

Hook: Why your enterprise AI stalls — and the pragmatic fix

Enterprise AI projects today fail to scale for one repeating reason: data remains trapped in purpose-built repositories and business teams. If your models can’t access consistent, governed signals across the organization, results are brittle, costs spike, and trust collapses. This playbook gives a practical, step-by-step approach to break data silos using federated queries, a modern data catalog, and purpose-built query engines so you can scale enterprise AI while improving data trust.

Executive summary — what you’ll get and why it matters (2026)

In 2026, organizations that combine federated query patterns with strong metadata management and domain-driven publishing (data mesh) gain three wins: lower latency for analytics, reduced ETL overhead, and higher data trust. This guide focuses on actionable steps and a worked example deploying a federated query engine (Trino-style) connecting cloud object storage (Iceberg/Delta), cloud warehouses, and operational databases. We’ll show configuration patterns, governance guardrails, and observability tactics used by teams in late 2025 and early 2026 to support production AI.

Why federated queries and catalogs are the right levers in 2026

Recent industry research — including Salesforce’s 2026 findings — confirms that weak data management and low trust are key bottlenecks for enterprise AI. The response from leading teams has been to:

• Adopt federated queries to avoid expensive, brittle ETL.
• Centralize metadata and lineage in a data catalog to build trust and accelerate discovery.
• Use a high-performance query engine as a unifying fabric across object stores, warehouses, and databases.

These patterns reduce duplicate copies, shorten model iteration loops, and give data teams control without forcing a single monolith.

High-level architecture — the federated query pattern

Here’s the canonical architecture we’ll implement in the worked example. It’s intentionally modular so you can swap components.

• Query plane: Trino/Starburst (open-source or commercial) as the federated query engine.
• Storage plane: Cloud object store with open table formats (Iceberg/Delta) for raw and curated datasets.
• Warehouse plane: Snowflake or cloud native warehouse for curated marts and business data.
• Operational databases: Postgres, MySQL, or cloud OLTP stores for transactional signals.
• Metadata plane: Data catalog (Amundsen, Apache Atlas, or commercial Unity Catalog/Purview) with lineage and quality results.
• Quality and observability: Monitoring (Prometheus/Grafana), query profiler, and data quality tests (Great Expectations/Deequ).

Step-by-step playbook

1) Start with a narrow, high-value pilot

Pick one AI use case (e.g., churn prediction) that touches multiple data domains (product events in object store, CRM in a warehouse, billing in Postgres). A focused pilot reduces blast radius and produces measurable impact.

2) Inventory datasets and stakeholders

Run a quick metadata sweep. Capture:

• Sources and owners
• Data formats (Parquet/Delta/Iceberg/CSV)
• Approximate size, update frequency
• Current transformations

Store results in your catalog as draft entries. This creates early alignment between data producers and consumers.

3) Choose the right query engine and deployment model

When selecting a query engine, evaluate against these criteria:

1. Connector coverage: Can it query object stores, warehouses, and OLTP stores?
2. Performance & scalability: Can it push down predicates and parallelize effectively?
3. Observability: Does it expose query plans, runtime metrics, and profiling hooks?
4. Security & governance integrations: Can it integrate with your catalog, IAM, and data masking tools?

Trino (and Starburst), Dremio, and commercial cloud offerings each map well to these requirements. For the worked example below we use Trino as the federated engine because of its rich connector ecosystem.

4) Adopt an open table format on object storage

For raw and curated datasets on object storage, use Iceberg or Delta. Open formats add atomicity, schema evolution, and partitioning benefits — important for production AI pipelines.

5) Configure the federated engine to talk to each data plane

Practical connectors to configure:

• Iceberg (S3 + Hive metastore or Glue catalog)
• Snowflake or BigQuery connectors for warehouse reads
• RDBMS connectors (Postgres/MySQL) for transactional data

Below is a minimal Trino catalog configuration for Iceberg on S3 (use your IAM credentials or instance role in production):

# etc/catalog/iceberg.properties
connector.name=iceberg
catalog.type=hive
hive.metastore.uri=thrift://hive-metastore:9083
iceberg.catalog.type=hive
iceberg.io-impl=org.apache.iceberg.aws.s3.S3FileIO

And a sample Snowflake connector:

# etc/catalog/snowflake.properties
connector.name=snowflake
snowflake.url=https://.snowflakecomputing.com
snowflake.user=trino_user
snowflake.warehouse=TRINO_WH
snowflake.role=ANALYST

6) Register and sync metadata with your data catalog

Hook your catalog to ingest table schema, ownership, and lineage. Typical steps:

1. Enable catalog ingestion (API or connector) from the federated engine and source systems.
2. Auto-tag tables with classifications (PII, internal, public).
3. Record lineage from raw files & transformations to model inputs.

Automating this reduces manual documentation and gives model owners visibility into dataset provenance.

7) Implement semantic contracts and dataset APIs (data mesh)

Require domain teams to publish datasets with:

• Schema contract and change policy
• SLAs for freshness and completeness
• Instrumentation for quality tests

These contracts are fundamental to a data mesh where domains own their data but expose it in a discoverable, governed way.

8) Replace brittle ETL with targeted virtualization and materialization

Instead of blanket ETL copies, use three patterns:

• Virtualized federated views — on-the-fly joins across systems (fast to implement, lower storage cost).
• Incremental materialized views — precompute high-cost joins or aggregates for frequent queries (balanced cost/latency).
• Targeted ETL — only materialize when necessary for latency or regulatory reasons.

These patterns act as an ETL alternative, reducing duplicate data while preserving performance.

9) Add observability and cost controls

Key observability controls:

• Query profiling: capture runtime metrics, shuffle size, and scan bytes.
• Cost-aware routing: route heavy scans to materialized datasets or warehouses.
• Query governance: enforce limits per user/role and require approval for high-cost queries.

In practice, teams save 20–60% on analytics costs by mixing virtualization with materialization and actively policing exploratory queries.

10) Enforce data quality and trust

Data trust emerges when quality checks and lineage are visible and actionable. Recommended controls:

• Automated profiling on new datasets
• Gate model pipelines with data quality gates (Great Expectations hooks)
• Surface test results in the data catalog and notify owners on regressions

Worked example: federated churn model pipeline

We’ll walk through a minimal deployment that federates three sources: product events in S3 Iceberg tables, sales CRM in Snowflake, and billing in Postgres. The model training job runs in a managed ML environment and queries the federated engine.

Architecture

• Trino cluster on Kubernetes (3 coordinator + worker pool)
• Iceberg tables on S3 with Glue metastore
• Snowflake for customer attributes
• Postgres for invoicing
• Amundsen catalog for metadata and lineage
• Great Expectations for quality checks

Deployment steps (condensed)

1. Deploy Trino with K8s Helm chart and mount configs for catalogs.
2. Create Iceberg catalogs in Glue and register event tables.
3. Configure Snowflake connector for Trino and test basic queries.
4. Connect Postgres via the JDBC connector to Trino.
5. Enable Amundsen ingestion: crawl Trino catalogs, Glue, and Snowflake metadata.
6. Author a federated SQL query that joins across Iceberg, Snowflake, and Postgres and validate the plan (EXPLAIN).
7. Create a materialized view for the heavy join and refresh nightly.
8. Run Great Expectations checks on the materialized view; register results to Amundsen.

Example federated SQL

-- Join events (Iceberg), customer attrs (Snowflake), invoices (Postgres)
SELECT
  c.customer_id,
  count(e.event_id) AS events_30d,
  sum(i.amount) AS invoices_90d,
  MAX(c.last_touch) AS last_touch
FROM iceberg.default.events e
JOIN snowflake.crm.customers c ON e.customer_ref = c.customer_id
LEFT JOIN pg.billing.invoices i ON c.customer_id = i.customer_id
WHERE e.event_time >= current_date - INTERVAL '30' DAY
GROUP BY c.customer_id;

Run EXPLAIN to make sure predicate pushdown and table scans behave as expected. If scans are large, create an incremental materialized view to precompute features.

Governance and metadata management patterns

To scale beyond the pilot, implement these patterns:

• Owner-first metadata: require dataset owner and SLAs on publish.
• Lineage-first troubleshooting: auto-capture lineage for transformations with observability hooks so any incident maps to root causes quickly.
• Access controls with attribute-based policies: enforce masking and row-level security at the federated engine, integrated with your catalog tags.
• Schema contracts & versioning: publish schema changes as proposals that must pass backward-compatibility checks.

"In 2026, teams that treat metadata as product and use federated queries to stitch domains outperform peers on model time-to-value and cost efficiency."

Measuring success: KPIs that matter

Track these KPIs to measure the impact of breaking silos:

• Model iteration time (hours/days)
• Average query latency for model feature queries
• Storage duplication ratio (copies of truth)
• Data quality incident rate and time-to-resolution
• Cost per model training run

Benchmark before the pilot and re-measure after 30/90/180 days.

Common pitfalls and how to avoid them

Pitfall: Blindly virtualizing every query

Virtualization is cheap to start but can create unpredictable latency. Policy: classify queries by cost and frequency, and only virtualize exploratory or infrequent queries. Materialize hot paths.

Pitfall: Missing metadata hygiene

If catalog entries are stale or incomplete, trust drops. Enforce ingestion pipelines as part of the publishing workflow and run daily profilers on new datasets.

Pitfall: Treating the federated engine as a security boundary

Federation centralizes access; it’s critical that the engine integrates with IAM and your catalog’s classification tags for policy enforcement. Use attribute-based access and query-level masking.

Advanced strategies for mature adopters (2026+)

• Use adaptive caching to hold recent or frequently accessed partitions in a fast tier.
• Integrate vector-search indices and feature stores into the federated fabric for retrieval-augmented models.
• Automate cost-aware query rewrites: rewrite heavy joins to use pre-aggregates when appropriate.
• Surface model explainability lineage: ensure feature lineage into model predictions is visible in the catalog.

Real-world evidence and trends

Late 2025 and early 2026 saw multiple large enterprises move to federated patterns. Common outcomes reported are faster model experimentation, 30–50% reduction in storage duplication, and improved governance posture. Salesforce’s recent research reiterated the core issue: poor data management halts AI scale — and the countermeasure is strong metadata and cross-system queryability.

Checklist: Production-ready rollout

• Pilot defined with owners and success metrics
• Federated engine deployed and connectors validated
• Open table formats adopted on object storage
• Data catalog integrated with lineage and quality results
• Semantic contracts and SLAs enforced through the publishing workflow
• Observability and cost controls in place
• Training pipelines gated by data quality checks

Final takeaways

Breaking data silos is not an all-or-nothing migration. The practical path is iterative: start with a focused pilot, federate access with a high-quality query engine, and bake metadata and quality into the publishing process. This approach delivers immediate AI benefits — faster iterations, lower duplication, and higher trust — while keeping costs and complexity under control.

Call to action

Ready to pilot a federated query architecture for your AI workloads? Start with a one-week inventory and a three-week Trino pilot connecting one object store, one warehouse, and one operational DB. If you want a deployment checklist, connector templates, and a prebuilt quality test suite tailored for churn models, request the starter kit and run your first federated query within 30 days.

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