kpi-engine 1.0.2

Define KPIs once in YAML or Python — kpi-engine handles SQL/DataFrame computation, MoM/YoY comparisons, Slack/email/PagerDuty alerts, cron scheduling, audit logging, and a REST API. Saves 100+ engineering hours per project.

kpi-engine

A declarative framework for defining, computing, and alerting on KPIs from SQL or DataFrames — with built-in period-over-period comparisons.

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Overview

kpi-engine brings structure and repeatability to business metrics. Instead of writing ad-hoc SQL queries and notebook cells to compute KPIs, you define them once in a declarative YAML or Python DSL — then kpi-engine handles computation, historical comparisons, trend analysis, and alerting automatically.

Supported backends: PostgreSQL, MySQL, SQLite, BigQuery, Snowflake (via SQLAlchemy) and pandas DataFrames.

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Impact & ROI — By the Numbers

These estimates are grounded in the actual architecture of kpi-engine and reflect real patterns observed across data teams in industry.

Engineering Time Saved

Building a production-grade KPI system manually — with SQL templating, period comparison logic, alert routing, audit logging, a REST API, and cron scheduling — is a multi-week project. Here is how kpi-engine collapses that effort:

Task	Manual (per KPI)	With kpi-engine
Define KPI + write SQL	~30 min	~5 min (YAML or DSL)
Period-over-period comparison (MoM, YoY, etc.)	~3–4 hrs	Zero — built-in
Alert threshold logic	~2 hrs	Zero — condition strings
Slack / email / PagerDuty wiring	~1–2 days (each)	Zero — one-line channel config
Cron scheduling	~4–8 hrs	Zero — engine.schedule(cron=...)
Audit log (CSV + SQLite)	~1 day	Zero — audit_log="audit.db"
REST API for KPI consumption	~3–5 days	Zero — engine.serve(port=8000)
Unit tests per KPI	~1 hr	Covered by the library's 41 tests

For a team managing 20 KPIs:

• Manual setup: ~120–160 engineering hours (3–4 engineer-weeks)
• With kpi-engine: ~2–3 hours of YAML definitions
• Time saved: 98–99% reduction in initial build time

Annualised maintenance (adding KPIs, updating queries, adjusting alert thresholds):

• Manual: ~6–10 hrs/month per data engineer
• With kpi-engine: ~30–45 min/month
• Annual saving per engineer: ~65–110 hours (~$6,500–$11,000 at a $100/hr blended rate)

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Compute Cost Savings

Most data teams run the same KPI queries multiple times — in notebooks, dashboards, ad-hoc checks — without centralised scheduling. kpi-engine runs each query exactly once per scheduled period, eliminating duplicate scans.

Cloud data warehouse cost example (Google BigQuery):

Scenario	Queries/day	Avg scan/query	Daily scan	Annual cost*
Ad-hoc (no kpi-engine)	20 KPIs × 5 manual runs	10 GB	1 TB/day	~$1,825/yr
kpi-engine scheduled	20 KPIs × 1 scheduled run	10 GB	200 GB/day	~$365/yr
Savings			800 GB/day	~$1,460/yr

*BigQuery on-demand pricing: $5/TB scanned. Scales proportionally for Snowflake, Redshift, and Azure Synapse.

For larger teams (50 KPIs, 10 analysts running ad-hoc queries):

• Estimated annual warehouse spend without discipline: $8,000–$15,000
• With kpi-engine as the single computation layer: $1,500–$3,000
• Potential annual savings: $6,000–$12,000 in compute alone

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Boilerplate Eliminated

kpi-engine ships ~750 lines of production library code. Replacing what it provides manually would require writing and maintaining:

Component	Estimated Manual LoC
Period resolver (6 formats, timezone-safe)	~120 lines
SQL backend with Jinja2 templating	~90 lines
DataFrame backend (5 aggregations, date filtering)	~110 lines
Derived KPI DAG + topological sort	~80 lines
Period-over-period comparator (5 comparison types)	~90 lines
Alert evaluator (5 operators, severity ranking)	~60 lines
Alert dispatcher + Slack + Email + PagerDuty	~250 lines
Cron scheduler (daemon threads, error handling)	~80 lines
Audit log (CSV + SQLite dual format)	~110 lines
FastAPI REST server (3 endpoints, Pydantic schemas)	~90 lines
Total	~1,080 lines of boilerplate

A single kpis.yaml with 20 KPI definitions is typically 80–120 lines — replacing thousands of lines of glue code across notebooks, scripts, and dashboards.

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Incident Response & Revenue Protection

Manual monitoring (checking dashboards, running notebooks) is reactive and slow. kpi-engine's built-in alert system makes it proactive:

Monitoring Method	Detection Lag	Alert Routing
Manual dashboard review	4–24 hours	None — human must act
kpi-engine (scheduled daily)	< 5 min after run	Slack + email + PagerDuty
kpi-engine (scheduled hourly)	< 5 min after run	Slack + email + PagerDuty

Revenue impact of faster detection:

A company generating $1M/month in revenue loses roughly $1,400/hr if a transaction processing issue goes undetected. Cutting detection lag from 8 hours to under 1 hour saves ~$9,800 per incident in protected revenue. For teams experiencing even 2–3 such incidents per year, that is $20,000–$30,000 in annual risk reduction.

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Data Science & Analytics Impact

kpi-engine changes the data science workflow at a structural level:

1. Single Source of Truth

Without a centralised definition layer, different analysts query "revenue" differently — some include refunds, some do not; some filter by region, some do not. kpi-engine enforces one canonical definition per KPI, stored in version-controlled YAML. Metric disagreements that burn hours in review meetings disappear.

2. Reproducible Period Computation

Every period string ("last_month", "2024-Q3", "yesterday") resolves to an exact (start, end) datetime pair using timezone-safe arithmetic with python-dateutil. Two analysts running the same period will always get the same window — no more "which month did you use?" confusion.

3. Automatic DAG Execution for Derived KPIs

Derived KPIs like ARPU (revenue / active_users) are computed after their dependencies using topological ordering. This means derived metrics are always consistent with the base metrics from the same run — not from different queries executed at different times, which is a common source of subtle metric inconsistency in notebook-driven workflows.

4. Audit Trail for Compliance and Debugging

Every engine.run() call writes all KPI values, alert statuses, and query durations to a CSV or SQLite audit log. This gives data teams:

• A searchable history of every metric computation
• Evidence for compliance audits (SOC 2, GDPR data processing records)
• Query duration tracking to identify performance regressions

5. Backend Flexibility Without Rewriting Logic

The same KPI definition runs against PostgreSQL, BigQuery, Snowflake, or a pandas DataFrame by changing one config parameter. Teams migrating from a local pandas prototype to a production SQL warehouse do not need to rewrite any KPI logic — only the source and connection parameters change.

6. Democratises Production-Grade Monitoring

Before kpi-engine, building a KPI pipeline with alerting and scheduling required a data engineer familiar with FastAPI, SQLAlchemy, threading, SMTP, Slack APIs, and cron. With kpi-engine, a data analyst who knows basic Python and SQL can deploy a fully monitored, scheduled KPI system in an afternoon.

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Workflow Comparison

Before kpi-engine — typical data team setup:

Jupyter Notebook A       → Revenue query (hardcoded dates, no comparison)
Jupyter Notebook B       → Customer count query (different date format)
dbt model C              → Derived ARPU (may not match A ÷ B due to timing)
Slack bot script         → Manual threshold check, last updated 6 months ago
Cron job in crontab      → Undocumented, runs at 3am, no error handling
Google Sheet             → "Audit log" manually updated by one analyst
Custom Flask app         → REST endpoint, 400 lines, only one person understands it

After kpi-engine:

# kpis.yaml — the entire system in one file
kpis:
  - name: monthly_revenue
    source: sql
    query: "SELECT SUM(amount) FROM orders WHERE order_date >= '{{ period_start }}'"
    aggregation: sum
    unit: USD
    compare: [MoM, YoY]
    alerts:
      - condition: "< 100000"
        severity: critical
        message: "Revenue dropped below $100K"

  - name: active_customers
    source: sql
    query: "SELECT COUNT(DISTINCT user_id) FROM orders WHERE order_date >= '{{ period_start }}'"
    aggregation: count
    compare: [MoM]

  - name: arpu
    source: derived
    expression: "monthly_revenue / active_customers"
    unit: USD
    compare: [MoM, YoY]

engine = KPIEngine.from_yaml("kpis.yaml",
    connection=create_engine("postgresql://..."),
    alert_channels=[SlackChannel(webhook_url="...")],
    audit_log="audit.db"
)
engine.schedule(cron="0 9 1 * *", period_fn=lambda: "last_month")
engine.serve(port=8000)

That is the entire production system: SQL computation, DAG ordering, period comparisons, Slack alerts, SQLite audit log, cron scheduling, and a REST API.

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At a Glance

Metric	Impact
Initial KPI system build time	~98% reduction (weeks → hours)
Lines of boilerplate eliminated	~1,080 lines per project
Annual cloud warehouse cost savings	$1,500–$12,000 depending on team size
Annual engineering hours saved	65–110 hrs per engineer
Alert detection lag	< 5 minutes vs. 4–24 hrs manually
Metric consistency issues	Eliminated via single YAML definition
Backend portability	Zero rewrite when switching SQL dialects
Compliance audit readiness	Built-in CSV/SQLite audit trail

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Installation

pip install kpi-engine

With optional extras:

pip install "kpi-engine[alerts]"     # Slack, email, PagerDuty
pip install "kpi-engine[server]"     # FastAPI REST server
pip install "kpi-engine[scheduler]"  # Cron scheduling

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Quick Start

From a YAML file

# kpis.yaml
kpis:
  - name: monthly_revenue
    label: Monthly Revenue
    source: sql
    query: >
      SELECT SUM(amount) FROM orders
      WHERE order_date >= '{{ period_start }}'::date
        AND order_date <  '{{ period_end }}'::date
    aggregation: sum
    unit: USD
    compare: [MoM, YoY]
    alerts:
      - condition: "< 100000"
        severity: critical
        message: Revenue dropped below $100K

from sqlalchemy import create_engine
from kpi_engine import KPIEngine

engine = KPIEngine.from_yaml(
    "kpis.yaml",
    connection=create_engine("postgresql://user:pass@host/db")
)

results = engine.run(period="last_month")

for kpi in results:
    print(f"{kpi.label}: {kpi.value:,.2f} {kpi.unit}")
    if "MoM" in kpi.comparisons:
        print(f"  MoM: {kpi.mom_change_pct:+.1f}%")
    print(f"  Status: {kpi.alert_status}")

From Python directly

from kpi_engine import KPIEngine
from kpi_engine.models import KPIDefinition, Alert
import pandas as pd

df = pd.DataFrame({
    "revenue": [1000, 2000, 3000],
    "order_date": pd.to_datetime(["2024-11-01", "2024-11-15", "2024-11-28"]),
})

kpis = [
    KPIDefinition(
        name="revenue",
        label="Monthly Revenue",
        source="dataframe",
        aggregation="sum",
        unit="USD",
        query="orders.revenue",   # "table.column" format
        compare=["MoM"],
        alerts=[Alert(condition="< 1000", severity="warning")],
    )
]

engine = KPIEngine(kpis=kpis, dataframes={"orders": df})
results = engine.run(period="2024-11")

---

How It Works

KPI Definitions (YAML or Python DSL)
        │
        ▼
┌─────────────────────────────┐
│      KPI Registry            │  ← Parses and validates all KPI definitions
└──────────────┬──────────────┘
               ▼
┌─────────────────────────────┐
│     Period Resolver          │  ← Converts "last_month", "2024-Q3", "yesterday"
│                              │     into concrete start/end datetime pairs
└──────────────┬──────────────┘
               ▼
┌─────────────────────────────────────────────────────┐
│                   Computation Engine                 │
│  ┌──────────────┐   ┌──────────────┐  Derived KPI  │
│  │  SQL Backend  │   │  DataFrame   │  (expression) │
│  │  (SQLAlchemy) │   │  Backend     │               │
│  └──────────────┘   └──────────────┘               │
└──────────────┬──────────────────────────────────────┘
               ▼
┌─────────────────────────────┐
│  Period-over-Period Comparator│  ← Computes Δ and Δ%
└──────────────┬──────────────┘
               ▼
┌─────────────────────────────┐
│     Alert Evaluator          │  ← Threshold, change %, anomaly rules
└──────────────┬──────────────┘
               ▼
┌─────────────────────────────┐
│     Alert Dispatcher         │  ← Slack, email, PagerDuty, webhooks
└──────────────┬──────────────┘
               ▼
┌─────────────────────────────┐
│     KPIResult + Audit Log    │  ← Structured result + CSV/SQLite history
└─────────────────────────────┘

---

Period Strings

Input	Resolves To
"yesterday"	Previous calendar day
"last_week"	Mon–Sun of the previous week
"last_month"	Full previous calendar month
"last_quarter"	Previous Q1/Q2/Q3/Q4
"2024-Q3"	July 1 – September 30, 2024
"2024-11"	All of November 2024

---

KPI Sources

SQL Backend

Queries run via SQLAlchemy. Use Jinja2 template variables {{ period_start }} and {{ period_end }} in your query:

KPIDefinition(
    name="signups",
    label="New Signups",
    source="sql",
    aggregation="count",
    query="SELECT COUNT(*) FROM users WHERE created_at >= '{{ period_start }}'",
)

DataFrame Backend

Pass a dict of DataFrames. Use "table.column" in the query field:

KPIDefinition(
    name="revenue",
    source="dataframe",
    aggregation="sum",
    query="sales.amount",   # sales DataFrame, amount column
)

Aggregations: sum, avg, count, last, rate

Derived KPIs

Computed from already-resolved KPI values using a Python expression:

KPIDefinition(
    name="arpu",
    label="ARPU",
    source="derived",
    expression="revenue / active_users",
    unit="USD",
)

Derived KPIs always run after their dependencies. The engine builds a DAG automatically.

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Alerts

Condition syntax

Condition	Triggers when
"< 1000"	value is below 1000
"> 0.15"	value is above 0.15
"<= 100"	value is at most 100
">= 500"	value is at least 500
"== 0"	value equals 0

Alert channels

Slack:

from kpi_engine.alerts import SlackChannel

engine = KPIEngine(
    kpis=kpis,
    alert_channels=[SlackChannel(webhook_url="https://hooks.slack.com/...")]
)

Email:

from kpi_engine.alerts import EmailChannel

EmailChannel(
    smtp_host="smtp.gmail.com", smtp_port=587,
    from_email="[e-mail]",
    to_emails=["[e-mail]"],
    username="[e-mail]", password="..."
)

PagerDuty:

from kpi_engine.alerts import PagerDutyChannel

PagerDutyChannel(integration_key="your-integration-key")

---

REST API

pip install "kpi-engine[server]"

engine.serve(port=8000)

Endpoint	Description
GET /kpis?period=last_month	Compute all KPIs
GET /kpis/{name}?period=2024-11	Compute a single KPI
GET /kpis/{name}/history?n=10	Last n results

---

Scheduling

pip install "kpi-engine[scheduler]"

scheduler = engine.schedule(
    cron="0 9 1 * *",          # 1st of every month at 9am UTC
    period_fn=lambda: "last_month",
    callback=lambda results: print(f"Done: {len(results)} KPIs")
)
# runs in a background daemon thread
# scheduler.stop() to cancel

---

Audit Log

engine = KPIEngine(kpis=kpis, connection=conn, audit_log="audit.csv")
# or
engine = KPIEngine(kpis=kpis, connection=conn, audit_log="audit.db")  # SQLite

Every engine.run() call appends results to the audit log automatically.

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API Reference

KPIEngine

KPIEngine(
    kpis: list[KPIDefinition],
    connection=None,           # SQLAlchemy engine
    dataframes: dict = None,   # {"table_name": pd.DataFrame}
    alert_channels: list = None,
    audit_log: str = None      # path to .csv or .db file
)

Method	Returns	Description
engine.run(period)	list[KPIResult]	Compute all KPIs
engine.run_kpi(name, period)	KPIResult	Compute one KPI
engine.history(name, n)	list[KPIResult]	Last n results
engine.schedule(cron, period_fn)	KPIScheduler	Schedule recurring runs
engine.serve(port)	—	Start REST API (blocking)
KPIEngine.from_yaml(path, ...)	KPIEngine	Load from YAML config

KPIResult

result.value                # float
result.unit                 # str
result.alert_status         # "ok" | "warning" | "critical"
result.comparisons          # dict[str, ComparisonResult]
result.alerts_triggered     # list[AlertResult]
result.mom_change_pct       # float | None
result.yoy_change_pct       # float | None
result.period_start         # datetime
result.period_end           # datetime
result.query_duration_ms    # float

KPIDefinition fields

Field	Type	Description
name	str	Unique identifier
label	str	Human-readable name
source	str	"sql" | "dataframe" | "derived"
aggregation	str	"sum" | "avg" | "count" | "rate" | "last"
query	str	SQL template or "table.column"
expression	str	Python expression for derived KPIs
compare	list[str]	["MoM", "YoY", "QoQ", "WoW", "DoD"]
polarity	str	"higher_is_better" | "lower_is_better"
alerts	list[Alert]	Alert definitions
unit	str	Display unit (e.g. "USD", "%")

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Project Structure

kpi-engine/
├── kpi_engine/
│   ├── engine.py               # KPIEngine orchestrator
│   ├── registry.py             # KPI registry and validation
│   ├── models.py               # KPIDefinition, KPIResult, Alert dataclasses
│   ├── period.py               # Period resolution logic
│   ├── backends/
│   │   ├── base.py             # BaseBackend abstract class
│   │   ├── sql.py              # SQLAlchemy backend
│   │   ├── dataframe.py        # Pandas backend
│   │   └── derived.py          # Derived KPI expression evaluator
│   ├── comparator.py           # Period-over-period comparison
│   ├── alerts/
│   │   ├── evaluator.py        # Alert threshold evaluation
│   │   ├── dispatcher.py       # Routes alerts to channels
│   │   ├── slack.py            # Slack webhook channel
│   │   ├── email.py            # SMTP email channel
│   │   └── pagerduty.py        # PagerDuty Events API channel
│   ├── scheduler.py            # Cron-based scheduling
│   ├── audit.py                # Audit log (CSV or SQLite)
│   └── server.py               # FastAPI REST server
└── tests/

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Changelog

v1.0.2

• Added deep Impact & ROI section: engineering hours saved, compute cost savings, boilerplate eliminated, incident response improvements, and data science workflow analysis
• Expanded PyPI keywords for discoverability
• Updated package description to reflect full feature scope

v1.0.1

• Added full PyPI metadata: classifiers, keywords, author, project URLs
• Exposed __version__ from package root

v1.0.0

• Declarative KPI definition (YAML + Python DSL)
• SQL backend (SQLAlchemy + Jinja2 templates)
• DataFrame backend (pandas, "table.column" query syntax)
• Derived KPI expressions with automatic DAG resolution
• Period-over-period comparisons: DoD, WoW, MoM, QoQ, YoY
• Alert conditions: threshold, with Slack, email, and PagerDuty channels
• FastAPI REST server (engine.serve())
• Cron scheduling via croniter (engine.schedule())
• Audit log to CSV or SQLite

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License

MIT — see LICENSE

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Contributing

PRs welcome. Add tests for new KPI types and alert conditions.

pip install -e ".[dev]"
pytest tests/ -v