Here’s a power-packed set of 100 prompts using the CRISP AI framework (Context, Role, Instruction, Specification, Performance). I’ve made 60 focused on SQL for analytics and 40 on data management & automation. At the end you’ll find a clear step-by-step implementation workflow so you can operationalize it right away.

60 SQL-for-Analytics Prompts (CRISP)

1. SQL – KPI Rollups (Daily/Weekly/Monthly)
Context: Executive dashboard needs consistent KPI rollups. | Role: Senior Analytics Engineer. | Instruction: Aggregate revenue, orders, AOV by day/week/month with fiscal calendar. | Specification: CTEs; date spine; left join facts; handle late-arriving data; timezone = UTC. | Performance: Query <10s on 30M rows; mismatch <0.5% vs finance.

2. SQL – New vs Returning Customers
Context: Marketing wants acquisition vs retention split. | Role: SQL Analyst. | Instruction: Classify orders by first-purchase date. | Specification: Window MIN(order_date) per customer; flag returning; summarize by week. | Performance: Accuracy ≥99%; refresh daily.

3. SQL – Cohort Retention (Signup → Month N)
Context: Product retention tracking. | Role: Data Scientist (SQL-first). | Instruction: Build retention matrix by signup month and active months since signup. | Specification: Date trunc by month; activity = any event; pivot cohort_size/retention%. | Performance: Run <30s; matrix covers last 24 months.

4. SQL – Revenue Cohort Expansion (N-Day LTV)
Context: Finance needs LTV by cohort. | Role: Analytics Engineer. | Instruction: Compute cumulative revenue at 7/30/60/90 days from first purchase. | Specification: Window SUM over days_since_fp; exclude refunds; currency normalized. | Performance: ≤1% variance vs accounting adjustments.

5. SQL – Churn Rate (Subscription)
Context: CS wants churn insight. | Role: SQL Analyst. | Instruction: Monthly logo churn and net revenue retention. | Specification: Snapshot MRR; SCD2 for plan changes; churned if MRR to 0; include expansions. | Performance: Matches Stripe MRR within 0.5%.

6. SQL – Funnel Conversion (A→B→C)
Context: Growth funnel optimization. | Role: Analyst. | Instruction: Compute step-through rates and avg time between steps. | Specification: Sessionized events; first occurrence per step; median deltas. | Performance: 95% sessions mapped; <5% ambiguous.

7. SQL – A/B Test Readout
Context: Experiment decisioning. | Role: Data Analyst. | Instruction: Aggregate variant metrics (conv, revenue/visitor); sanity checks. | Specification: Randomization unit=user_id; exclude pre-period users; minimum exposure 7 days. | Performance: Reproducible; guardrail flags if sample ratio mismatch.

8. SQL – RFM Segmentation
Context: CRM targeting. | Role: SQL Marketer. | Instruction: Compute Recency, Frequency, Monetary terciles and segment codes. | Specification: Use last 365 days; handle zero orders; output segment labels. | Performance: Percentiles stable; NULL-safe.

9. SQL – Inventory Aging
Context: Ops wants slow movers. | Role: Supply Chain Analyst. | Instruction: Calculate stock age buckets (0–30/31–60/61–90/90+). | Specification: Last receipt date; units and value; join costs. | Performance: Daily job <2 min; correct for negative stocks.

10. SQL – Anomaly Detection Prep
Context: Finance variance alerting. | Role: BI Engineer. | Instruction: Produce z-score features for revenue by product/day. | Specification: 60-day mean/std; holiday flags; min-traffic filter. | Performance: False positive rate <5% (as labeled).

11. SQL – Seasonality Index
Context: Forecast decomposition. | Role: Data Analyst. | Instruction: Derive weekday/month seasonality multipliers for orders. | Specification: 2-year history; normalized factors sum≈period count. | Performance: Stable factors; low noise volatility.

12. SQL – Contribution Margin by SKU
Context: Profit optimization. | Role: FP&A Analyst. | Instruction: Compute CM = Price – COGS – Shipping – Variable Fees. | Specification: Join orders, costs, fees; currency fx to USD. | Performance: Matches finance CM within 1%.

13. SQL – CAC by Channel
Context: Paid media ROI. | Role: Growth Analyst. | Instruction: Join ad spend to first-order attributed by channel. | Specification: UTM rules; 7-day click; dedupe multiple touches; organic bucket. | Performance: Resolves ≥95% users with attribution.

14. SQL – LTV:CAC Ratio
Context: Board KPI. | Role: SQL Analyst. | Instruction: Compute LTV/CAC by cohort and channel at day 90. | Specification: Use cohorts from #4; CAC from #13; output ratio & conf bands. | Performance: Auto-refresh weekly.

15. SQL – Refund & Dispute Rate
Context: Risk monitoring. | Role: BI Engineer. | Instruction: Calculate refund%, dispute% by SKU and week. | Specification: Exclude partial refunds; align to order_date; mark high-risk if >2σ. | Performance: Alert rate <10% SKUs.

16. SQL – Stockout Risk
Context: Ops planning. | Role: Analyst. | Instruction: Days of cover = On-hand / Avg daily sales (30d). | Specification: Ignore zero-sales SKUs; include inbound PO dates. | Performance: >95% SKUs with coverage.

17. SQL – Repeat Purchase Interval
Context: Lifecycle marketing. | Role: SQL Marketer. | Instruction: Median days between purchases by category. | Specification: Window lag per user; outlier winsorization. | Performance: P50 stable week-to-week.

18. SQL – Basket Analysis (Co-Purchase)
Context: Cross-sell ideas. | Role: Data Analyst. | Instruction: Compute pairwise SKU lift. | Specification: Same-order pairs; min support 0.5%; output lift>1.2. | Performance: Query ≤90s with pre-aggregations.

19. SQL – Geo Performance
Context: Territory planning. | Role: Analyst. | Instruction: Revenue and AOV by state/region with population index. | Specification: Join census; per-capita metrics; choropleth-ready. | Performance: Complete coverage; no orphan geos.

20. SQL – User Journey Paths
Context: Product navigation. | Role: Analyst. | Instruction: Top 20 most common 3-step paths. | Specification: Sessionize; n-gram of events; exclude noise. | Performance: 80% coverage of sessions.

21. SQL – Churn Early Warning Signals
Context: CS proactive outreach. | Role: Analyst. | Instruction: Build weekly risk features (login drop, ticket volume). | Specification: 4-week deltas; z-scores; export user_id + risk_flag. | Performance: Recall ≥70% of churners.

22. SQL – SLA Compliance
Context: Support ops. | Role: BI Engineer. | Instruction: % tickets resolved within SLA by severity. | Specification: Work hours calendar; exclude pending-customer. | Performance: Aligns with Zendesk export.

23. SQL – Data Quality (Nulls & Duplicates)
Context: DQ guardrails. | Role: Analytics Engineer. | Instruction: Table-level null %, dup counts, and freshness. | Specification: information_schema; last_updated; row hash for dupes. | Performance: Runs across all schemas <5 min.

24. SQL – PII Discovery
Context: Governance. | Role: Data Steward. | Instruction: Flag columns with potential PII patterns. | Specification: Regex for emails, phones, SSNs; catalog table. | Performance: Precision >95% on known PII.

25. SQL – Role Access Audit
Context: Security review. | Role: DBA (Read-only). | Instruction: List users, roles, object grants. | Specification: Use system tables; map critical tables. | Performance: Accurate snapshot daily.

26. SQL – Slowly Changing Dimension (SCD2) Diff
Context: Dimensions integrity. | Role: Analytics Engineer. | Instruction: Validate SCD2 validity ranges, no overlaps. | Specification: start_ts < end_ts; current flag; overlaps=0. | Performance: Zero violations alert.

27. SQL – Late Arriving Fact Reconciliation
Context: Data drift. | Role: BI Engineer. | Instruction: Compare prior day counts vs today for same date. | Specification: Δ threshold 0.5%; exceptions table. | Performance: Alert within 5 min of load.

28. SQL – Windowed Rolling Metrics
Context: Trend smoothing. | Role: Analyst. | Instruction: 7/28-day rolling sums and pct changes. | Specification: Window frames rows between; fill gaps with date spine. | Performance: Consistent across time zones.

29. SQL – Forecast Input Table
Context: External ML. | Role: DS/ML Ops. | Instruction: Create clean time series with regressors and holidays. | Specification: One row/day/sku; outlier cap; CSV export. | Performance: No missing dates.

30. SQL – Funnel Leakage Reasons
Context: CRO. | Role: Analyst. | Instruction: Attribute drop-offs to top error/event reasons. | Specification: Left join error codes; group by step. | Performance: 90% of drops labeled.

31. SQL – Marketing Mix Inputs
Context: MMM prep. | Role: Analyst. | Instruction: Daily spend, impressions, clicks per channel with lag features. | Specification: 0–28 day lags; cost normalization. | Performance: Wide table ready for regression.

32. SQL – NPS Driver Analysis Prep
Context: VOC insights. | Role: Analyst. | Instruction: Join NPS to product usage by user-month. | Specification: Last response per month; bin scores; top 10 features. | Performance: Balanced panel coverage >85%.

33. SQL – Fraud Signals
Context: Risk. | Role: Analyst. | Instruction: Flag orders with mismatched IP/geo, velocity spikes. | Specification: 3σ rules; rule_id, score. | Performance: Catch rate >80% of labeled fraud.

34. SQL – AR Aging
Context: Collections. | Role: FP&A. | Instruction: AR buckets by invoice age. | Specification: 0–30/31–60/61–90/90+; include disputes. | Performance: Matches ERP aging.

35. SQL – Unit Economics by Channel
Context: Profit focus. | Role: FP&A. | Instruction: CM per acquisition channel and month. | Specification: Merge spend, CAC, CM; normalize returns. | Performance: Variance <1% vs finance.

36. SQL – Trial→Paid Conversion
Context: PLG motion. | Role: Analyst. | Instruction: % trials converting within 30 days; top usage predictors. | Specification: Feature thresholds; logistic prep table. | Performance: Stable across cohorts.

37. SQL – Lead Velocity Rate (LVR)
Context: Sales pipeline. | Role: RevOps. | Instruction: Month-over-month growth of qualified leads. | Specification: Stage=SQL; dedupe by email/domain. | Performance: Consistent vs CRM.

38. SQL – Sales Cycle Length
Context: Deal hygiene. | Role: RevOps Analyst. | Instruction: Median days from first meeting to close by segment. | Specification: Exclude stalled; censor 180+ days. | Performance: Robust medians.

39. SQL – Win/Loss Reasons
Context: GTM learning. | Role: Analyst. | Instruction: Top loss reasons share by segment. | Specification: Normalize free text to categories. | Performance: ≥90% reason coverage.

40. SQL – Territory Quota Attainment
Context: Sales ops. | Role: Analyst. | Instruction: Attainment% by rep, quarter. | Specification: Join quotas; prorate start dates. | Performance: Matches CRM.

41. SQL – Revenue Waterfall
Context: B2B SaaS finance. | Role: FP&A. | Instruction: New/Expansion/Contraction/Churn decomposition. | Specification: Monthly MRR delta classification. | Performance: Balanced totals.

42. SQL – Product Stickiness (DAU/MAU)
Context: Engagement. | Role: Analyst. | Instruction: DAU/MAU ratio and trend. | Specification: Distinct users; 30-day MAU window. | Performance: Accurate deduping.

43. SQL – Feature Adoption Curves
Context: PM insights. | Role: Analyst. | Instruction: Adoption % over time since feature release. | Specification: Release flag; user cohorts. | Performance: Consistent cohort sizes.

44. SQL – Event Outlier Detection (IQR)
Context: Data sanity. | Role: BI. | Instruction: Flag outliers with IQR method. | Specification: Q1/Q3 per day; cap or exclude. | Performance: Low false positives.

45. SQL – Duplicate Entity Resolution
Context: Data hygiene. | Role: Data Engineer. | Instruction: Candidate duplicates by email/phone/fuzzy name. | Specification: Soundex/levenshtein; merge recommendations. | Performance: Precision ≥90%.

46. SQL – Fill Rate & OTIF
Context: Logistics. | Role: Ops Analyst. | Instruction: Fill rate and On-Time-In-Full by warehouse. | Specification: Planned vs actual; window weights. | Performance: Warehouse coverage 100%.

47. SQL – Price Elasticity Prep
Context: Pricing tests. | Role: Analyst. | Instruction: Create price vs qty tables with controls. | Specification: Cluster by SKU; promo flags. | Performance: Ready for regression.

48. SQL – Backlog & Throughput (Kanban)
Context: Eng productivity. | Role: Analytics. | Instruction: WIP, throughput/week. | Specification: From issue tracker; cycle time. | Performance: Consistent with Jira.

49. SQL – Data Freshness SLA
Context: Reliability. | Role: BI. | Instruction: Track last_loaded_at vs SLA target. | Specification: Alert if >X minutes late. | Performance: Mean delay <5 min.

50. SQL – Materialized Mart for BI
Context: Dashboard speed. | Role: Analytics Engineer. | Instruction: Build denormalized fact table for BI. | Specification: Grain=order_id_day; surrogate keys. | Performance: BI queries <2s.

51. SQL – Partition & Cluster Strategy
Context: Cost control. | Role: Data Engineer. | Instruction: Recommend partitions/clusters from query history. | Specification: Scan top 50 queries; propose columns. | Performance: Reduce scan bytes by 40%+.

52. SQL – EXPLAIN Plan Linter
Context: Perf tuning. | Role: SQL Perf Analyst. | Instruction: Surface red flags (full scan, skew, cross join). | Specification: Parse EXPLAIN; rule-based flags. | Performance: Flag recall >90%.

53. SQL – Incremental Load Merge
Context: ELT efficiency. | Role: Analytics Eng. | Instruction: Upsert daily changes via MERGE. | Specification: business_key; updated_at; soft deletes. | Performance: <5 min on 10M delta.

54. SQL – Timezone Normalization
Context: Global reporting. | Role: Analyst. | Instruction: Convert mixed timezones to UTC and local. | Specification: TZ map by account; daylight savings awareness. | Performance: No double counts.

55. SQL – Holiday Effects Table
Context: Forecast inputs. | Role: Analyst. | Instruction: Build holiday calendar per country. | Specification: Country_code; movable feasts; joinable keys. | Performance: Complete up to 3 years ahead.

56. SQL – KPI Definitions Registry
Context: Governance. | Role: Data Steward. | Instruction: Table of KPI name, SQL source, owner, refresh. | Specification: One row/KPI; link to dbt model. | Performance: 100% KPIs registered.

57. SQL – Data Lineage Map (Lightweight)
Context: Trust. | Role: BI Engineer. | Instruction: Map dependencies from view definitions. | Specification: Parse information_schema; upstream tables. | Performance: Completes under 2 min.

58. SQL – SLA Breach Root Cause
Context: Incident review. | Role: Analyst. | Instruction: Trace failed job to upstream table freshness. | Specification: Store job logs; join to lineage. | Performance: RCA within 1 min.

59. SQL – KPI Reconciliation (Finance vs Ops)
Context: Single source of truth. | Role: Analyst. | Instruction: Side-by-side totals; variance reasons. | Specification: Align dimensions; calendar; refunds timing. | Performance: Variance notes auto-filled.

60. SQL – Data Sandbox Extract
Context: Ad-hoc exploration. | Role: Analyst. | Instruction: Create de-identified sample dataset. | Specification: 1% stratified sample; mask PII. | Performance: Complete in <2 min.

40 Database Management & Automation Prompts (CRISP)

61. dbt Model Refactor Plan
Context: Spaghetti models. | Role: Analytics Eng. | Instruction: Break monolith into staging/intermediate/mart. | Specification: Naming conventions; tests; owners. | Performance: Build time −40%.

62. Airflow DAG for Daily Mart
Context: Scheduling. | Role: Data Eng. | Instruction: Orchestrate extract→stage→mart→tests. | Specification: Retries, SLAs, backfills, alerts. | Performance: On-time ≥99%.

63. Great Expectations DQ Suite
Context: Quality gates. | Role: Data Steward. | Instruction: Add checks for schema, ranges, nulls, uniqueness. | Specification: Fail → quarantine; pass → promote. | Performance: FP <3%.

64. Data Catalog Onboarding
Context: Discovery pain. | Role: Data Steward. | Instruction: Register datasets, owners, tags, lineage. | Specification: OpenLineage/Atlas; auto-harvest. | Performance: 95% assets cataloged.

65. CDC Setup (Debezium/Fivetran)
Context: Near-real-time. | Role: Data Eng. | Instruction: Enable CDC for core OLTP tables. | Specification: Primary keys; ordering; idempotent loads. | Performance: Lag <5 min.

66. SCD2 in dbt
Context: History tracking. | Role: Analytics Eng. | Instruction: Implement SCD2 macros for dims. | Specification: validity windows, current_flag, hash diff. | Performance: Zero overlaps.

67. Cost Governance Policy
Context: Cloud cost spike. | Role: Data Eng. | Instruction: Set budgets, alerts, and query usage policies. | Specification: Bytes scanned caps; warehouse sizing. | Performance: Cost −30% MoM.

68. Data Access RBAC
Context: Security. | Role: DBA. | Instruction: Role hierarchy with least privilege. | Specification: Schema- and object-level grants; PII isolation. | Performance: Zero privilege escalations.

69. Backup & Restore Runbook
Context: DR readiness. | Role: DBA. | Instruction: Define RPO/RTO, backup cadence, restore drills. | Specification: Point-in-time; encrypted storage. | Performance: Recovery test <30 min.

70. Data Masking Policy
Context: Test environments. | Role: Data Steward. | Instruction: Mask PII for non-prod copies. | Specification: Consistent tokenization; referential integrity. | Performance: 0 PII leaks.

71. Warehouse Benchmarking
Context: Slow queries. | Role: Perf Engineer. | Instruction: Benchmark TPC-like workloads across configs. | Specification: Scale factors; cache warm/cold. | Performance: 95th latency target.

72. Schema Versioning
Context: Breaking changes. | Role: Data Eng. | Instruction: Introduce migration strategy (Liquibase/Flyway). | Specification: SemVer; rollback scripts. | Performance: Zero failed deploys.

73. Data Contracts with Producers
Context: Upstream instability. | Role: Analytics Eng. | Instruction: Define schema contracts & SLAs. | Specification: Required fields; change windows. | Performance: Contract breach alerts.

74. API-to-Lake ELT Pattern
Context: Third-party data. | Role: Data Eng. | Instruction: Land raw JSON, bronze→silver→gold. | Specification: Auto schema inference; drift handling. | Performance: Pipeline success ≥99%.

75. Metadata-Driven Ingestion
Context: Many tables. | Role: Data Eng. | Instruction: Parameterize sources with configs. | Specification: YAML/JSON descriptors; dynamic DAGs. | Performance: Build time −50%.

76. Column-Level Lineage
Context: Audits. | Role: Data Steward. | Instruction: Capture column-level transforms. | Specification: Parser or compiler hooks. | Performance: Coverage ≥90%.

77. BI Semantic Layer
Context: Metric drift. | Role: BI Architect. | Instruction: Centralize KPIs in semantic layer (LookML/Transform/MetricFlow). | Specification: dims, measures, time grains. | Performance: One-definition KPI.

78. SLA Monitoring Dashboard
Context: Reliability. | Role: BI Dev. | Instruction: Visualize freshness, volume, failures. | Specification: Alerts via Slack/Email/Pager. | Performance: MTTR <30 min.

79. Data Sharing Hub
Context: External partners. | Role: Data Eng. | Instruction: Secure share of curated marts. | Specification: Row/column filters; token auth. | Performance: Access audit weekly.

80. Governance Playbook
Context: Scale. | Role: Data Leader. | Instruction: Define policies for access, lifecycle, naming, reviews. | Specification: Confluence pages; owner sign-off. | Performance: Audit-ready in 2 weeks.

81. Query Review Program
Context: Cost + speed. | Role: Perf Analyst. | Instruction: Set weekly review of top 20 expensive queries. | Specification: Index hints, predicate pushdown. | Performance: 30% cost drop.

82. Data Product SLAs
Context: Treat data as product. | Role: Product Owner. | Instruction: Define SLA for freshness, accuracy, uptime. | Specification: SLOs + error budgets. | Performance: 99.9% availability.

83. Event Tracking Spec
Context: Analytics gaps. | Role: PM + Analyst. | Instruction: Author tracking plan (names, properties). | Specification: JSON schema; version control. | Performance: 100% events typed.

84. Warehouse Multi-Env Strategy
Context: Dev/test/prod safety. | Role: Data Eng. | Instruction: Isolate envs, promote via CI/CD. | Specification: Feature branches; approvals. | Performance: Zero prod hotfixes.

85. Data Observability Rollout
Context: Blind spots. | Role: Data Eng. | Instruction: Implement freshness, volume, distribution monitors. | Specification: Monte Carlo/Bigeye/etc. | Performance: Catch 95% incidents.

86. Lakehouse Compaction
Context: Small files problem. | Role: Data Eng. | Instruction: Optimize/Compact Delta/Iceberg tables. | Specification: Z-order/cluster; vacuum policies. | Performance: Query time −40%.

87. Privacy-by-Design Review
Context: Compliance. | Role: Data Steward. | Instruction: Review flows for purpose limitation, minimization. | Specification: DSR workflows; retention rules. | Performance: DSR SLA <7 days.

88. Access Request Automation
Context: Manual bottlenecks. | Role: Data Ops. | Instruction: Self-serve access workflow with approvals. | Specification: JIT roles; audit trail. | Performance: Grant time <1 hour.

89. KPI Onboarding Kit
Context: New stakeholders. | Role: BI Lead. | Instruction: Create KPI one-pagers and demo queries. | Specification: Definition, owner, source tables. | Performance: Fewer ad-hoc requests.

90. Golden Dataset Certification
Context: Trust in data. | Role: Data Steward. | Instruction: Certify curated datasets with badges. | Specification: Tests passed; owner; SLA. | Performance: 90% BI usage on certified.

91. Storage Tiering Policy
Context: Cost optimization. | Role: DBA. | Instruction: Move cold data to cheaper storage. | Specification: Access frequency threshold; lifecycle rules. | Performance: Storage cost −35%.

92. Cross-Org Data Exchange
Context: Subsidiaries sharing. | Role: Data Eng. | Instruction: Standardize schemas and keys. | Specification: Master data IDs; privacy filters. | Performance: Harmonized KPIs.

93. Automated Data Docs
Context: Documentation lag. | Role: Analytics Eng. | Instruction: Generate docs from code/comments. | Specification: dbt docs or similar; publish nightly. | Performance: Docs freshness 100%.

94. Incident Postmortem Template
Context: Reliability culture. | Role: Data Ops Lead. | Instruction: Standard template for RCA & actions. | Specification: Five whys; owner due dates. | Performance: Repeat issue rate <5%.

95. Batch→Streaming Transition Plan
Context: Latency needs. | Role: Data Eng. | Instruction: Identify pipelines to convert to streaming. | Specification: Kafka/Kinesis; exactly-once. | Performance: 95th latency <2 min.

96. BI Access Patterns Tuning
Context: Dashboard slowness. | Role: BI Eng. | Instruction: Cache strategy + extracts. | Specification: Materialized views; index hints. | Performance: Dashboard P95 <2s.

97. Data Literacy Program
Context: Self-serve goals. | Role: BI Lead. | Instruction: Run trainings, office hours, playbooks. | Specification: Role-based paths; quizzes. | Performance: Support tickets −40%.

98. KPI Review Cadence
Context: KPI creep. | Role: Data PM. | Instruction: Quarterly KPI pruning & redefinition. | Specification: Usage telemetry; owner sign-off. | Performance: KPI count −20% w/o value loss.

99. Golden Path Templates
Context: Consistency. | Role: Data Eng. | Instruction: Provide cookie-cutter project templates. | Specification: Repo skeletons; lint/test configs. | Performance: New project setup <30 min.

100. Value Realization Tracker
Context: Proving impact. | Role: BI Lead. | Instruction: Tie data initiatives to $$ outcomes. | Specification: Baseline, counterfactual, savings. | Performance: Quarterly ROI report.

Step-by-step Implementation (fast + practical)

Define the target business question → write 1–2 sentences and the exact KPIs needed.

Locate truth tables → confirm source-of-truth for events, orders, customers, products, finance, etc.

Profile data → row counts, date ranges, nulls, duplicates, basic stats.

Map logic to SQL → window functions, joins, time-bucketing, cohort logic, and guardrails.

Create staging views → clean types, normalize timestamps, standardize ids, deduplicate.

Write analytic SQL → build modular CTEs (base → transforms → metric), add comments, add tests.

Validate → reconcile row counts and totals vs. source; sample business cases with stakeholders.

Optimize → add predicates early, minimize scans, use partitions/clustering/indexes, check EXPLAIN plan.

Automate → package as dbt models or SQL scripts in Airflow/Dagster with data quality checks.

Publish & monitor → persist to marts, expose to BI, add SLAs, anomaly alerts, and lineage docs.

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Mahaman Sani Dan Mallam.

SQL Data Analytics Prompts, AI Automation Fram, Data Engineering Best Practices, CRISP AI Framework, Data Analytics Automation Tools, Advanced SQL for BI, Enterprise Data Strategy, AI-Driven Data Insights