Project 2: Multi-Tenant Analytics Warehouse with Partitioning0%
Project 3: Enterprise Compliance & Automated Event Audit Engine

Project 2: Multi-Tenant Analytics Warehouse with Partitioning

Advanced25 min read•Updated: 2026-09-12
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Project 2: Multi-Tenant Analytics Warehouse with Partitioning

Advanced Capstone Project 2: Multi-Tenant Analytics Warehouse with Partitioning

In software-as-a-service (SaaS) business models, multi-tenant databases store telemetry and analytics data for thousands of corporate tenants in a shared data warehouse. As event volume scales into tens of millions of records per month, architects must leverage Range Partitioning, Virtual Columns, and Window Functions to deliver sub-second analytical reporting.

1. Warehouse Architecture & Schema DDL

Visual Architecture & Process Flow

How data and code flow step-by-step

Flowchart
Step 1
latency_ms INT GENERATED ALWAYS AS (payload
Step 2
>'$.response_time_ms') VIRTUAL,

2. Populating Enterprise Seed Data

SQL
-- Seed Tenants
INSERT INTO tenants (tenant_id, company_name, plan_tier, created_at) VALUES
(1, 'Acme Cloud Corp', 'ENTERPRISE', '2024-01-10'),
(2, 'Starlight Media', 'GROWTH', '2024-06-15'),
(3, 'Nexus Fintech', 'ENTERPRISE', '2025-02-01');
 
-- Seed Event Telemetry
INSERT INTO tenant_event_logs (tenant_id, event_name, event_timestamp, payload) VALUES
(1, 'api_request', '2025-08-10 10:15:00', '{"endpoint": "/v1/users", "response_time_ms": 45, "status": 200}'),
(1, 'api_request', '2025-08-10 10:16:00', '{"endpoint": "/v1/reports", "response_time_ms": 320, "status": 200}'),
(1, 'api_request', '2025-08-10 10:17:00', '{"endpoint": "/v1/users", "response_time_ms": 52, "status": 200}'),
(2, 'api_request', '2025-08-10 11:00:00', '{"endpoint": "/v1/stream", "response_time_ms": 110, "status": 200}'),
(3, 'api_request', '2025-08-10 12:30:00', '{"endpoint": "/v1/transfer", "response_time_ms": 850, "status": 504}'),
(1, 'api_request', '2026-01-05 09:00:00', '{"endpoint": "/v1/billing", "response_time_ms": 65, "status": 200}');

3. High-Performance Executive Analytics Queries

Query 1: Partition-Pruned Latency Percentiles per Tenant

Calculate average, 95th percentile, and moving average latency while pruning to partition p2025:

SQL
EXPLAIN
SELECT
t.company_name,
e.event_timestamp,
e.latency_ms,
-- Running average latency for tenant
ROUND(
AVG(e.latency_ms) OVER (
PARTITION BY e.tenant_id
ORDER BY e.event_timestamp
ROWS BETWEEN 5 PRECEDING AND CURRENT ROW
),
1
) AS rolling_latency_avg,
-- Percentile rank of latency
ROUND(PERCENT_RANK() OVER (PARTITION BY e.tenant_id ORDER BY e.latency_ms ASC), 3) AS latency_percentile
FROM tenant_event_logs e
JOIN tenants t ON e.tenant_id = t.tenant_id
WHERE e.event_timestamp >= '2025-01-01' AND e.event_timestamp < '2026-01-01'
ORDER BY t.company_name, e.event_timestamp;

Query 2: Multi-Tenant JSON SLA Compliance Dashboard View

SQL
CREATE OR REPLACE VIEW v_tenant_sla_metrics AS
SELECT
t.tenant_id,
t.company_name,
t.plan_tier,
COUNT(e.event_id) AS total_requests,
ROUND(AVG(e.latency_ms), 1) AS avg_latency_ms,
MAX(e.latency_ms) AS max_latency_ms,
-- Count SLA breaches (latency > 500ms)
SUM(CASE WHEN e.latency_ms > 500 THEN 1 ELSE 0 END) AS sla_breaches,
ROUND(
(SUM(CASE WHEN e.latency_ms <= 500 THEN 1 ELSE 0 END) / COUNT(e.event_id)) * 100,
2
) AS sla_compliance_percentage
FROM tenants t
LEFT JOIN tenant_event_logs e ON t.tenant_id = e.tenant_id
GROUP BY t.tenant_id, t.company_name, t.plan_tier;
 
-- Query SLA compliance view
SELECT * FROM v_tenant_sla_metrics;

Multiple Choice Questions

1. Why was PRIMARY KEY (event_id, event_timestamp) defined on tenant_event_logs?

A. MySQL requires all primary keys to have two columns B. MySQL mandates that the partitioning column (event_timestamp) must be included in the primary key C. To prevent foreign keys from working D. To disable auto-increment Answer: B Explanation: MySQL table partitioning rules dictate that the partitioning key must be part of every unique index, including the Primary Key.

2. What allows the query WHERE latency_ms > 500 to utilize an index even though the latency value is stored in JSON?

A. Full-Text indexing B. A B+Tree index defined on the VIRTUAL generated column (latency_ms) C. Memory tables D. Adaptive Hash Index only Answer: B Explanation: Virtual generated columns extract scalar JSON values into an indexable B+Tree structure without storing duplicate row text on disk.

3. Which partition is scanned when querying WHERE event_timestamp BETWEEN '2025-06-01' AND '2025-07-01'?

A. All partitions (p2024, p2025, p2026, p_future) B. Only partition p2025 (Partition Pruning) C. None D. p_future only Answer: B Explanation: The optimizer executes partition pruning, reading only the single physical partition corresponding to year 2025.

4. What does the rolling_latency_avg calculation demonstrate?

A. An unindexed full table scan B. A 6-row sliding window frame (ROWS BETWEEN 5 PRECEDING AND CURRENT ROW) smoothing response time spikes C. An uncommitted dirty read D. A deadlock condition Answer: B Explanation: The sliding window frame calculates a rolling average across the 5 preceding rows plus the current row per tenant.

5. How can 2024 telemetry data be instantly removed at the end of its retention window without downtime?

A. DELETE FROM tenant_event_logs WHERE YEAR(event_timestamp) = 2024; B. ALTER TABLE tenant_event_logs DROP PARTITION p2024; C. DROP TABLE tenant_event_logs; D. TRUNCATE DATABASE; Answer: B Explanation: DROP PARTITION p2024 deletes all 2024 records near-instantaneously via file unlinking with minimal lock impact.

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