data-virtualization

name	data-virtualization
description	Implement data virtualisation to query distributed data sources without moving data. Outputs virtual layer design, query federation strategy, performance optimisation, and governance approach.
argument-hint	["data sources","query latency requirements","data volume","existing data infrastructure"]
allowed-tools	Read, Write

Data Virtualization

Data virtualisation presents a unified logical view of data from multiple heterogeneous sources — without physically moving or replicating the data. Instead of ETL pipelines that copy data into a central warehouse, virtual layers query data in-place. This reduces latency for time-sensitive data and eliminates the complexity of replication pipelines.

When to Use Data Virtualization

USE VIRTUALIZATION when:
  ✓ Real-time or near-real-time data needed (can't wait for ETL)
  ✓ Data cannot be replicated (regulatory: GDPR data residency)
  ✓ Source system data used only occasionally (not worth ETL investment)
  ✓ Proof-of-concept before building full pipeline
  ✓ Federated query across multiple data warehouses

USE ETL/ELT instead when:
  ✓ Heavy transformations needed (virtualisation adds latency)
  ✓ Offline analytics (source systems unavailable 24/7)
  ✓ Historical analysis requires data retention beyond source
  ✓ Query performance is critical (virtualisation adds overhead)
  ✓ Complex ML feature computation

Query Federation with Trino

-- Trino (formerly PrestoSQL) — federated SQL across any data source
-- No data moved; queries run in-place at each source

-- Example: Join Postgres (production) with Snowflake (warehouse) and S3 (raw files)
SELECT
    p.customer_id,
    p.email,                           -- From PostgreSQL (live production)
    s.total_orders,                    -- From Snowflake (analytics warehouse)
    s.lifetime_value,
    h.page_view_count                  -- From Hive (S3 raw files)
FROM postgresql.production.customers p
JOIN snowflake.analytics.customer_metrics s
    ON p.customer_id = s.customer_id
JOIN hive.raw_events.page_views h
    ON p.customer_id = h.user_id
WHERE p.created_at > DATE '2024-01-01'
  AND s.total_orders > 5;

# Trino catalog configuration
# /etc/trino/catalog/postgresql.properties
connector.name=postgresql
connection-url=jdbc:postgresql://prod-db:5432/production
connection-user=trino_reader
connection-password=${ENV:POSTGRES_PASSWORD}

# /etc/trino/catalog/snowflake.properties
connector.name=snowflake
connection-url=jdbc:snowflake://account.snowflakecomputing.com/
connection-user=trino_reader
connection-password=${ENV:SNOWFLAKE_PASSWORD}
snowflake.warehouse=ANALYTICS_WH
snowflake.database=ANALYTICS

# /etc/trino/catalog/hive.properties
connector.name=hive
hive.metastore.uri=thrift://hive-metastore:9083
hive.s3.aws-access-key=${ENV:AWS_ACCESS_KEY}
hive.s3.aws-secret-key=${ENV:AWS_SECRET_KEY}

dbt + Virtualisation

-- dbt model that queries across sources without moving data
-- Uses Trino as the execution engine

-- models/customer_360.sql
{{
  config(
    materialized='view',   -- Virtual; not materialised
    schema='virtual_layer'
  )
}}

WITH live_customers AS (
    -- Real-time from PostgreSQL via Trino federation
    SELECT
        customer_id,
        email,
        plan_type,
        created_at
    FROM {{ source('postgresql', 'customers') }}
    WHERE created_at >= CURRENT_DATE - INTERVAL '90' DAY
),

analytics_enrichment AS (
    -- Pre-aggregated from Snowflake warehouse
    SELECT
        customer_id,
        total_orders,
        lifetime_value,
        last_order_date
    FROM {{ source('snowflake', 'customer_metrics') }}
)

SELECT
    lc.customer_id,
    lc.email,
    lc.plan_type,
    ae.total_orders,
    ae.lifetime_value,
    ae.last_order_date,
    -- Computed at query time (not stored)
    CASE
        WHEN ae.last_order_date > CURRENT_DATE - INTERVAL '30' DAY THEN 'active'
        WHEN ae.last_order_date > CURRENT_DATE -     
         
      customer_status
 live_customers lc
  analytics_enrichment ae  (customer_id)

Performance Optimisation

-- Virtualisation adds latency — optimise strategically

-- 1. Predicate pushdown: push filters to source systems
-- Trino automatically pushes WHERE clauses to source connectors
-- Verify with EXPLAIN:
EXPLAIN
SELECT * FROM postgresql.production.orders
WHERE created_at > DATE '2024-01-01'
  AND status = 'paid';
-- Should show: ScanFilterProject[table=postgresql, filter=...]

-- 2. Materialise hot virtual views
-- For frequently-queried virtual tables, materialise in DWH
CREATE TABLE snowflake.analytics.customer_360_snapshot AS
SELECT * FROM trino_virtual_layer.customer_360
WHERE last_order_date >= CURRENT_DATE - INTERVAL '30' DAY;

-- Schedule refresh: every 4 hours via Airflow/Prefect

-- 3. Columnar pruning: select only needed columns
-- Never SELECT * from virtualised sources
SELECT customer_id, email, plan_type  -- Not SELECT *
FROM postgresql.production.customers;

-- 4. Partition pruning: always include partition key in WHERE
SELECT *
FROM hive.events.page_views
WHERE dt = '2024-03-15'  -- dt is the S3 partition key
  AND event_type  ;

Anti-Patterns to Avoid

Anti-Pattern	Problem	Fix
Virtualising everything	High latency; source systems overloaded	Virtualise for real-time; ETL for historical/heavy analytics
No query limits	Expensive federated queries cause source outages	Query timeout; row limits; connection pooling
SELECT * on virtual tables	Fetches all columns from source	Always specify required columns
Joining large virtual tables	Massive cross-source data movement	Pre-aggregate at source; use virtual layer for final join
No governance on virtual layer	PII in virtual views accessible to all	Apply column-level security at virtual layer

10 Rules

1. Data virtualisation is for real-time access and federation — not for replacing ETL at scale.
2. Always push predicates (WHERE clauses) to source systems — verify with EXPLAIN.
3. Never SELECT * from a virtual table — specify only required columns.
4. Materialise frequently-queried virtual views for performance — refreshed on a schedule.
5. Set query timeouts and row limits — runaway federated queries can take down source systems.
6. Governance applies at the virtual layer — column-level security filters PII before users query.
7. Source systems must be designed for additional read load — virtualisation adds queries.
8. Trino/Presto is the most mature open-source federation engine — preferred over building custom.
9. Latency budgets: virtual queries take 10-100x longer than warehouse queries — plan accordingly.
10. Test query plans with EXPLAIN before production deployment — unexpected full scans are common.

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Deep Reference Playbook

The sections below extend this skill into a complete operating playbook so it can run end-to-end inside Claude Code, CoWork, or any agentic tool without further prompting. Pull only the sections you need for a given engagement.

Inputs the skill must collect

Before producing any output, the skill confirms:

1. Objective — the single decision or artifact the user wants out of this session.
2. Context — system, team, customer, product, or domain the work sits inside.
3. Constraints — time, budget, headcount, regulatory, technical, political.
4. Definition of done — what "good" looks like and who signs it off.
5. Audience — who reads or consumes the output (engineer, exec, customer, regulator).
6. Existing artifacts — prior versions, related docs, dashboards, tickets.
7. Risk appetite — how reversible the decision is and how much ambiguity is acceptable.

If any of these are missing, the skill asks targeted clarifying questions before generating output. It never invents constraints the user did not state.

Operating workflow

The canonical workflow for Data Virtualization runs in five stages. Each stage has an explicit exit criterion so the skill knows when to advance.

Stage 1 — Frame. Restate the problem in one paragraph. Name the decision, the deadline, the stakeholders, and the success metric. Surface assumptions explicitly so they can be challenged.

Stage 2 — Diagnose. Inventory the current state with concrete evidence: metrics, quotes, screenshots, configs, tickets. Separate facts from interpretations. Identify the two or three root causes that explain most of the gap, not the long tail of symptoms.

Stage 3 — Design. Generate at least two viable options. For each option, capture: what changes, who owns it, what it costs, what it unblocks, what it risks, and how it could fail. Recommend one with a written rationale.

Stage 4 — Execute. Convert the chosen option into a sequenced plan: milestones, owners, dependencies, gating checks, communication cadence, and rollback triggers. Anything that cannot be assigned an owner and a date is not yet a plan.

Stage 5 — Validate. Define how success will be measured, when the measurement happens, and what action follows each possible result. Schedule the retrospective before the work starts, not after.

Outputs the skill produces

Depending on the request, the skill returns one or more of:

• A one-page brief suitable for an executive reader.
• A detailed working document for the delivery team.
• A decision record capturing the choice, the alternatives, and the rationale.
• A risk register with probability, impact, owner, and mitigation.
• A sequenced action plan with named owners and explicit due dates.
• A measurement plan tied to the success metric.
• A communication plan for stakeholders inside and outside the team.

Every artifact uses clear headings, short paragraphs, and tables where comparison helps. No filler. No restating the prompt. No hedging language when a recommendation is warranted.

Decision logic and trade-offs

The skill applies the following heuristics when choices are not obvious:

• Prefer reversible decisions taken quickly over irreversible decisions taken slowly.
• Optimise for the constraint that bites first — usually time, attention, or trust, not money.
• Default to the simplest design that meets the stated definition of done; add complexity only when a specific requirement forces it.
• Make the cost of being wrong visible so the reader can judge whether the recommendation is proportionate.
• Name the people, not the roles, when assigning ownership; ambiguous ownership produces ambiguous outcomes.

Anti-patterns the skill refuses to emit

Anti-pattern	Why it fails	What the skill does instead
Generic best-practice list with no context	Reader cannot act on it	Tailors recommendations to the stated constraints
Recommendation without trade-offs	Hides the cost of being wrong	Names the price paid for the recommendation
Plan with no owners or dates	Cannot be executed or tracked	Assigns a named owner and a date to every action
Metrics theatre	Measures activity, not outcome	Ties every metric back to the user or business outcome
Boil-the-ocean scope	Nothing ships	Cuts scope to the smallest valuable slice
Buried recommendation	Reader misses the point	Leads with the recommendation in the first paragraph

Quality bar

The skill self-checks each output against these gates before returning it:

1. Can a busy executive understand the recommendation from the first 150 words?
2. Is every claim either evidenced, labelled as an assumption, or removed?
3. Does every action have an owner and a date?
4. Are the trade-offs of the recommendation stated honestly?
5. Is there a measurable success criterion?
6. Would the author be comfortable defending this artifact in a review meeting?

If any gate fails, the skill rewrites the section before returning it.

Worked micro-example

Context: Implement data virtualisation to query distributed data sources without moving data. Outputs virtual layer design, query federation strategy, performance optimi

Frame: the team needs a defensible recommendation within five working days; the audience is a cross-functional steering group; the cost of delay is higher than the cost of being slightly wrong.

Diagnose: the dominant constraint is decision latency, not analytical depth. Existing data is sufficient for a directional call.

Design: two viable options surfaced. Option A optimises for speed and reversibility. Option B optimises for completeness but slips the deadline by two weeks.

Execute: Option A recommended. Plan sequenced into a two-week sprint with named owners, a mid-point checkpoint, and a clear rollback trigger.

Validate: success measured against a single leading indicator at day 30 and a single lagging indicator at day 90. Retrospective scheduled for day 35.

Cadence and follow-through

A one-shot artifact rarely changes outcomes. The skill recommends a lightweight cadence to keep the work alive:

• Weekly: owner posts a five-line status (done, doing, blocked, risk, ask).
• Fortnightly: steering group reviews leading indicators and unblocks dependencies.
• Monthly: retrospective on what the data is teaching the team; adjust plan accordingly.
• Quarterly: revisit the original objective and decide whether to continue, pivot, or stop.

Closing rules of thumb

1. Lead with the recommendation; supporting analysis follows.
2. Treat every output as a draft that will be challenged; pre-empt the obvious objections.
3. Prefer one strong recommendation over three weak options.
4. When the evidence is thin, say so; do not launder uncertainty as confidence.
5. Optimise for the next decision, not for the perfect document.
6. Make it easy for the reader to disagree with you in a structured way.
7. Ship the artifact; iterate against feedback rather than in private.