Data Pipeline Patterns

Data Pipeline Patterns

Guide for implementing data pipelines that integrate with OptAIC's orchestration and governance.

When to Use

Apply when:

• Building PipelineDef implementations (ETL, Expression, Training)
• Implementing data ingestion flows
• Creating data quality validation
• Setting up Arrow schemas for datasets
• Integrating with Prefect orchestration

Pipeline Definition Types

Point-in-Time (PIT) Correctness

Critical rule: Always track knowledge_date (when data was known) separately from as_of_date (data's effective date).

# WRONG - lookahead bias
df = pd.read_sql("SELECT * FROM prices WHERE date = ?", [target_date])

# CORRECT - PIT query
df = pd.read_sql("""
    SELECT * FROM prices
    WHERE as_of_date <= ?
    AND knowledge_date <= ?
    ORDER BY knowledge_date DESC
""", [target_date, knowledge_cutoff])

See references/​pit-patterns.md.

Arrow Schema Pattern

import pyarrow as pa

def price_schema() -> pa.Schema:
    return pa.schema([
        pa.field("date", pa.date32(), nullable=False),
        pa.field("symbol", pa.string(), nullable=False),
        pa.field("close", pa.float64(), nullable=False),
        pa.field("knowledge_date", pa.timestamp("us"), nullable=False),
    ])

Prefect Integration

from prefect import flow, task

@task
async def fetch_data(source: str, date: str) -> dict:
    pass

@task
async def validate_schema(data: dict, schema_ref: str) -> bool:
    pass

@flow
async def daily_refresh(dataset_id: UUID, date: str):
    raw = await fetch_data(...)
    if not await validate_schema(raw, schema_ref):
        raise ValidationError()
    await store_data(raw, dataset_id)
    await emit_activity("dataset.refreshed", ...)

See references/​prefect-patterns.md.

Lineage DAG at Creation Time

CRITICAL: Lineage DAG is built when Instances are CREATED, NOT at execution time.

from libs.orchestration import LineageResolver

# At DatasetInstance creation:
async def create_dataset_instance(session, actor, payload):
    resolver = LineageResolver()

    # 1. Build lineage DAG from pipeline config
    dag = await resolver.build_dag_for_instance(session, instance.id, actor.tenant_id)

    # 2. Cache upstream IDs for fast execution checks
    if dag.has_dependencies:
        instance.upstream_resource_ids = dag.upstream_ids
        instance.upstream_status = {str(uid): "unknown" for uid in dag.upstream_ids}

        # 3. Create DatasetLineage + Subscription records
        await resolver.create_lineage_and_subscriptions(session, dag)

Pub/​Sub Observer Pattern

Downstream datasets are notified when upstreams complete:

from libs.orchestration import LineageObserver, CentrifugoNotifier

async def on_run_completed(session, run):
    observer = LineageObserver()

    # Notify downstreams, get those now fully ready
    ready_ids = await observer.on_upstream_completed(
        session,
        upstream_id=run.dataset_instance_id,
        run_id=run.resource_id,
    )

    # Publish real-time notifications
    notifier = CentrifugoNotifier()
    for downstream_id in ready_ids:
        await notifier.notify_upstream_ready(downstream_id, upstream_id, True)

Fast Execution Check

Use cached status for execution checks (no lineage query):

from libs.orchestration import LineageResolver

resolver = LineageResolver()
all_ready = await resolver.check_all_upstreams_ready(session, instance_id)

if not all_ready and not force:
    raise UpstreamNotReadyError(...)

UpdateFrequency Configuration

Configure expected update frequency for freshness calculations:

from libs.orchestration import UpdateFrequency

# Daily data, 1 day grace period
frequency = UpdateFrequency(
    frequency="daily",
    grace_period_days=1,
)

# Business days only (skip weekends)
frequency = UpdateFrequency(
    frequency="daily",
    business_days_only=True,
    grace_period_days=1,
)

# Weekly on Monday
frequency = UpdateFrequency(
    frequency="weekly",
    day_of_week=0,  # 0=Monday
)

# Store in Instance config_json
config = {
    "update_frequency": {
        "frequency": "daily",
        "business_days_only": True,
        "grace_period_days": 1,
    }
}

See references/​lineage-patterns.md.

Data Quality Checks

Standard checks to implement:

• no_future_dates - Prevent lookahead
• no_duplicates - Key uniqueness
• coverage_check - Required dates/​symbols
• schema_conformance - Arrow schema match

See references/​quality-checks.md.

Lazy Import Rule

Heavy deps must be lazy-loaded:

from typing import TYPE_CHECKING

if TYPE_CHECKING:
    import pandas as pd
    import pyarrow as pa

Reference Files

• PIT Patterns - Point-in-time correctness
• Prefect Patterns - Orchestration integration
• Quality Checks - Data validation
• Lineage Patterns - Dependency and freshness tracking