Anusha Kovi

CORE

Business Intelligence Engineer at Amazon

Joined Dec 2025

https://hackernoon.com/u/anushakovi

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Reputation:	861
Pageviews:	13.9K
Articles:	11
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Articles
Trend Reports

Articles

When Valid SQL Was Still the Wrong Answer

A personal project exploring why AI-generated SQL isn't always trustworthy and how semantic context, validation, and governance improve analytics accuracy.

June 22, 2026

· 763 Views

The Data Warehouse Concurrency Playbook: Surviving the "Super Bowl" Moment

Classify requests (dashboards vs exploration/jobs), cap and prioritize concurrency, and fall back to cache/rollups so critical dashboards stay responsive during spikes.

May 8, 2026

· 2,113 Views

Intelligent Load Management for LLM Calls: From Static Rate Limits to Priority-Aware "Agent QoS"

Use a fair, priority-based tool scheduler instead of static rate limits, leveraging concurrency caps, signals, abort rules, and safe degradation.

February 26, 2026

· 976 Views

Supply Chain Security for Tools and Prompts

Tools, routers, signatures, versioned prompts, and semantic models enforce pinned bundles at runtime and emit audit-proof evidence stamps.

February 23, 2026

· 1,084 Views

The Missing Primitive in Data Platforms: Agent Contracts for Tool Calls

Define agent contracts per tool, including success criteria, SLOs, golden traces, allowed data, rollback triggers, canary releases, and retry limits.

February 20, 2026

· 1,192 Views · 1 Like

Embedding Store as a Platform on AWS: OpenSearch + Bedrock + S3 Needs SLAs, Governance, and Quotas

Vector search is not "just OpenSearch." It just needs to be run as a platform with SLAs, governance, and quotas to control drift, leaks, and out-of-control costs.

February 19, 2026

· 1,316 Views · 1 Like

Production-Ready Observability for Analytics Agents: An Open Telemetry Blueprint Across Retrieval, SQL, Redaction, and Tool Calls

Standardize analytics agent observability with OpenTelemetry spans for policy, retrieval, SQL, verification, redaction, tools, capturing proof without sensitive payloads

February 18, 2026

· 2,090 Views · 1 Like

How to Build Permission-Aware Retrieval That Doesn't Leak Across Teams

Permission-aware retrieval ensures that the assistant uses only allowed information. A context graph enforces access control to prevent cross-team leakage.

February 18, 2026

· 1,397 Views · 1 Like

Query-Aware Retrieval Routing for Analytics on AWS: When to Use Redshift, OpenSearch, Neptune, or Cache

Use a query router for LLM analytics — Redshift (KPIs), OpenSearch (definition), Neptune (lineage), and Cache (repeats) — to improve accuracy, latency, and costs.

February 10, 2026

· 1,013 Views · 1 Like

Agentic DataOps With Guardrails: MCP and MWAA for Pipeline Incident Response

Treat MWAA failures like incident response. Use MCP for safe, bounded tools and a human-approved, audited, validated DAG trigger.

February 9, 2026

· 612 Views · 1 Like

Why Semantic Layers Matter in Analytics: A Deep Dive into RAG Design

Analytics assistants/chatbots should trust the semantic layer — not documents. Retrieve metric definitions, run governed SQL, and attach an audit bundle to every KPI.

January 22, 2026

· 1,320 Views · 1 Like

Trend Reports

Trend Report

Cognitive Databases, Intelligent Data

No longer passive storage and query engines, databases are becoming active, intelligent participants in how modern systems interpret, connect, and act on data. As AI moves deeper into production and enterprises adopt generative and agentic architectures, the database layer is being reshaped to support semantic search, contextual retrieval, and real-time decision-making. Vector databases, semantic indexing, and AI-driven optimization are changing how developers work with both structured and unstructured data, while the line between transactional and analytical systems continues to fade under hybrid workload demands.This report examines these industry shifts in practical terms, exploring how relational, NoSQL, vector, and multi-model systems are coming together to support AI-native applications. Our research, guest thought leadership, and practitioner insights look at how teams are bringing vector search into production, updating architectures for AI workloads, and redesigning data pipelines around semantic and contextual intelligence.