Aether Analytics Platform

01 / Client Business Challenge

The client’s analytics telemetry network was aggregating billions of hourly cloud records, leading to extreme relational database locks, memory-pool exhaustion, and overwhelming monthly cloud hosting expenditures. Reports failed to render, risking enterprise SLA violations.

Enterprise analytics data-lakes often fail to scale because database developers write generic queries that trigger full-table scans. At MAHANTRA, we bypassed monolithic databases, partitioning historical logs into serverless data-buckets and leveraging in-memory Redis matrices for real-time reads. This separation of concern ensures that analytical queries execute independently of persistent master transactions, stabilizing response parameters under extreme concurrent session surges.

02 / Modern Engineering Solution

We engineered a highly optimized serverless data collection layer and deployed a sharded vector-search pipeline on Google Cloud with tight BigQuery and Vertex AI models. Our engineers fine-tuned database cell caching using pre-indexed, multi-regional memory pools to bypass persistent storage reads.

03 / Architectural Decisions & Standards

• ✓Separation of analytical and operational data paths using event message brokering.
• ✓Transition from persistent server relational storage to regionalized BigQuery tables with pre-mapped clustering keys.
• ✓Implementation of an edge-caching layer returning compressed JSON payloads inside double-digit millisecond limits.

04 / Strategic Business Outcomes

• Processed and synthesized 5.4 Billion transaction logs daily with absolute platform consistency.
• Slashed average database read latencies by 58%, facilitating instant operational telemetry metrics across global divisions.
• Eliminated query bottlenecks and deadlocks, providing sub-second load speeds for active client reporting portals.