Data Analytics
Intelligence &
Quality
Visualize insights, ensure system reliability, and guarantee data quality across your organization.
Phase 7: Advanced Analytics & Visualization
From Query to Insight
Execute complex SQL analysis, perform data cleansing, and build interactive dashboards using a unified interface. Choose the right compute engine for the job.
Query Editor
-- Complex Aggregation with Window Functions
SELECT
product_category,
order_date,
sum(revenue) as daily_sales,
AVG(sum(revenue)) OVER (
PARTITION BY product_category
ORDER BY order_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
) as 7_day_rolling_avg
FROM sales_fact
WHERE order_date >= DATE_TRUNC('month', CURRENT_DATE)
GROUP BY 1, 2
ORDER BY 1, 2;
Cost: $0.0005 per scan
Phase 8: Reliability Engineering & Observability
Maintain, Monitor, and Scale
Pipelines break. The difference between a glitch and a disaster is Observability. We implement centralized logging, automated performance tuning, and strict audit trails to keep your data flowing.
Live Log Stream
10:00:01INFO[Ingestion]Stream started. Topic: clickstream-v1
10:00:05INFO[Transformer]Batch #4492 processed (500 records)
10:02:12WARN[ComputeNode-04]Memory utilization > 85%. Garbage collection triggered.
10:02:45ERROR[DataWriter]WriteTimeout: Partition "date=2024-05-20" is locked.
10:02:46INFO[Orchestrator]Auto-Scaling triggered. Added 2 worker nodes.
Anomaly Detection: Pattern "WriteTimeout" detected 3 times in 5m.
Auto-Remediation: Scaled up cluster. Issue resolved.
Phase 9: Data Quality Assurance
Trust Your Data
Bad data breaks pipelines and biases models. We implement automated Data Profiling and Validation Gates to ensure every record meets your strict quality standards before it enters the warehouse.
Automated Profiling Report
Scanned: 1.2M Records| Column | Type | Completeness | Valid % | Status |
|---|---|---|---|---|
| user_id | UUID | 100% | 100% | PASS |
| String | 98% | 95% | PASS | |
| age | Integer | 85% | 15% | WARN |
| zip_code | String | 100% | 40% | FAIL |
Deep Inspection
Issue Detected: Column zip_code has 60% invalid format.
Recommendation: Apply regex filter ^\d5(-\d4)?$ in the transformation layer.
Skew Manager
Performance Bottleneck
Partition P0 is processing 6x more data than others (Straggler Task). This causes the entire job to wait.
Data Quality Rules Engine
// Rule: Mandatory Fields
expect_column_values_to_not_be_null(column="transaction_id")
expect_column_values_to_not_be_null(column="timestamp")
Result: 100% Pass. No orphaned records found.
Smart Sampling
Running quality checks on Petabytes of data is expensive. We use **Stratified Sampling** to validate a statistically significant subset (e.g., 5%) to detect errors early without scanning the full dataset.
Scanning 5% of Total