Business-oriented SQL patterns for KPI analytics, customer behavior modeling, anomaly detection, and decision-support workflows.
SQL remains foundational across applied data science, analytics engineering, and AI-enabled operational systems. In real-world environments, SQL is used not just for querying data, but for structuring KPIs, supporting operational monitoring, enabling customer intelligence workflows, and powering decision-support systems.
This repository provides a compact, reusable library of analytical SQL patterns aligned with practical use cases across telecom, digital services, subscription businesses, and enterprise analytics environments.
The examples focus on:
- KPI monitoring and trend analysis
- customer usage and behavioral segmentation
- retention and cohort measurement
- anomaly detection workflows
- deduplication and latest-record extraction
- business-facing reporting logic for operational decisions
Rather than isolated exercises, the repository is structured around reusable patterns that reflect real analytical workflows.
analytics-sql-patterns-for-ai-systems/
│
├── data/ # Synthetic datasets (CSV)
├── sql/ # SQL pattern modules
│ ├── 00_setup_duckdb.sql
│ ├── 01_window_analytics_patterns.sql
│ ├── 02_kpi_monitoring_patterns.sql
│ ├── 03_customer_behavior_patterns.sql
│ ├── 04_retention_and_cohort_patterns.sql
│ ├── 05_anomaly_detection_patterns.sql
│ └── 06_deduplication_and_latest_record_patterns.sql
│
└── README.md
Examples using ranking functions, lag/lead comparisons, running totals, and rolling averages for time-series and behavioral analysis.
Aggregation logic for operational KPIs including service-level summaries, threshold monitoring, and trend tracking.
Segmentation workflows including heavy-user identification, multi-service behavior analysis, and revenue-based ranking.
Cohort assignment and lifecycle analysis using activity-based retention tracking.
Rolling baselines, threshold logic, and deviation-based monitoring approaches for operational workflows.
Reusable approaches for resolving duplicates, tracking latest states, and maintaining clean operational views.
| file | what it demonstrates | typical use |
|---|---|---|
01_window_analytics_patterns.sql |
ranking, lag/lead, rolling averages, running totals | KPI trend analysis, top-N analysis |
02_kpi_monitoring_patterns.sql |
aggregations, threshold flags, service summaries | operational monitoring, SLA-style reporting |
03_customer_behavior_patterns.sql |
segmentation, heavy-user logic, revenue ranking | customer intelligence, monetization analysis |
04_retention_and_cohort_patterns.sql |
cohort assignment, activity tracking | retention and lifecycle analysis |
05_anomaly_detection_patterns.sql |
rolling baselines, deviation alerts | anomaly monitoring, proactive operations |
06_deduplication_and_latest_record_patterns.sql |
latest-state extraction, duplicate handling | clean reporting layers, operational views |
Input: event-level usage and KPI records
Logic: partitions, ordering, temporal comparison
Output: ranked entities, prior-period deltas, rolling metrics
Input: daily KPI records
Logic: aggregation, threshold evaluation, service-level summarization
Output: KPI summaries and monitoring flags
Input: customer usage events and revenue activity
Logic: grouping, segmentation, ranking
Output: heavy-user views, multi-service behavior, top customers
Input: subscription lifecycle data and usage activity
Logic: cohort assignment and activity tracking by month
Output: cohort-based retention views
Input: KPI time-series data
Logic: rolling baseline comparison and deviation checks
Output: anomaly candidates and service alerts
Input: subscription and ticket records
Logic: row-number based latest-state extraction and duplicate detection
Output: clean latest-record views and duplicate identification
The repository uses compact synthetic datasets designed to reflect realistic analytical use cases while remaining lightweight and portable.
Included datasets:
telecom_kpi_daily.csv— daily KPI values by region, service, and sitecustomer_usage_events.csv— customer usage activity across servicescustomer_subscriptions.csv— subscription lifecycle dataservice_tickets.csv— operational issue tracking records
These datasets are generic enough to apply across telecom, subscription-based platforms, and enterprise analytics workflows. should be within
| column | description |
|---|---|
| event_date | KPI observation date |
| region | geography or market |
| service_type | mobile, broadband, 5G, etc. |
| site_id | operational entity identifier |
| active_users | active user count |
| dropped_calls | dropped call volume |
| throughput_mbps | throughput measure |
| latency_ms | latency metric |
| availability_pct | service availability |
| ticket_count | associated support volume |
| column | description |
|---|---|
| customer_id | unique customer identifier |
| event_timestamp | activity timestamp |
| event_type | usage activity type |
| service_type | service category |
| usage_amount | usage quantity |
| revenue_amount | billed amount |
| region | geography |
| column | description |
|---|---|
| customer_id | unique customer identifier |
| subscription_id | subscription record |
| product_name | package or plan |
| activation_date | subscription start |
| renewal_date | renewal reference |
| status | lifecycle state |
| monthly_fee | recurring fee |
| region | geography |
| column | description |
|---|---|
| ticket_id | ticket identifier |
| customer_id | associated customer |
| opened_at | ticket open timestamp |
| closed_at | ticket close timestamp |
| issue_category | issue classification |
| priority | severity level |
| resolution_status | ticket state |
| region | geography |
The repository can be executed quickly using DuckDB. The examples use DuckDB and follow PostgreSQL-compatible analytical SQL syntax for portability across modern data platforms.
pip install duckdb
From the project root:
duckdb analytics_sql_patterns.duckdb < sql/00_setup_duckdb.sql
This loads all datasets into DuckDB tables.
Example:
duckdb analytics_sql_patterns.duckdb
.read sql/01_window_analytics_patterns.sql
The SQL patterns are written in a PostgreSQL-compatible analytical style and remain broadly portable across modern analytical environments.
These SQL patterns reflect common analytical workflows used across telecom, digital services, and enterprise analytics environments, particularly in contexts involving KPI monitoring, customer intelligence, operational analytics, and decision-support systems.
MIT License