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Natural Language to SQL Solution

Natural Language to SQL Solution: 85% faster data querying with AI-powered analytics

InsightsWave Technologies implemented a cutting-edge RAG-based solution that transformed natural language queries into optimized SQL, dramatically improving data accessibility and analysis efficiency at every level in the company.

Our client is a pharmaceutical corporation handling massive amounts of data across multiple cloud platforms, processing over 100 petabytes of data monthly across various business units.

Detailed information about the client cannot be disclosed under the provisions of the NDA.

  The Challenge

Massive data volumes and complex querying needs created significant operational bottlenecks. The client faced mounting challenges in managing their expanding data infrastructure. Their teams struggled with:

  • Processing and analyzing up to 100 petabytes of data monthly across multiple cloud platforms
  • High operational costs from existing solutions like Databricks and Snowflake
  • Limited data accessibility for business users who lacked SQL expertise
  • Complex governance requirements across diverse data sources and formats
  • Performance issues with cross-cloud queries and dynamic schema requirements

The company needed a solution that could democratize data access while maintaining performance and cost-effectiveness across their massive data infrastructure.

  The Solution

AI-powered Natural Language to SQL platform with advanced RAG framework

InsightsWave Technologies developed a comprehensive solution leveraging the latest in AI and data engineering. Our approach centered on a Retrieval-Augmented Generation (RAG) framework that transformed natural language queries into optimized SQL, while ensuring robust governance and scalability.

Intelligent Query Processing
  • Implemented advanced natural language processing using Amazon Bedrock for query understanding
  • Developed a sophisticated context-aware query generation system using Claude v2
  • Created an intelligent error handling and query correction mechanism
  • Integrated automated SQL optimization for enhanced performance
Dynamic Metadata Management
  • Designed a flexible catalog system using AWS Glue for comprehensive metadata tracking
  • Implemented Titan Embeddings for efficient semantic search and context retrieval
  • Created an automated metadata enrichment pipeline
  • Built dynamic schema handling mechanisms for diverse data formats
Infrastructure Optimization
  • Developed a multi-cloud query federation system across AWS, Azure, and GCP
  • Implemented intelligent caching and batch processing mechanisms
  • Created cost-optimized storage solutions using a combination of technologies
  • Established automated performance monitoring and optimization
Advanced Analytics Integration
  • Incorporated machine learning models for anomaly detection
  • Implemented predictive query optimization
  • Developed automated performance tuning mechanisms
  • Created intelligent data sampling techniques for large-scale analytics
Technologies Used:
  • Cloud & Infrastructure: AWS (Glue, OpenSearch, Athena, S3), Azure, GCP
  • AI/ML: Amazon Bedrock, Claude v2, Titan Embeddings
  • Data Processing: Apache Spark, ClickHouse, Vector.dev
  • Development: Python, React, TypeScript

  The Results

85% reduction in query processing time with 60% cost savings

The implementation delivered transformative results across multiple dimensions:

Performance Improvements:
  • 85% faster query processing times
  • 90% reduction in failed queries
  • 3x improvement in cross-cloud query performance
Cost Optimization:
  • 60% reduction in infrastructure costs
  • 40% decrease in operational overhead
  • 70% improvement in resource utilization
Business Impact:
  • Democratized data access across 1000+ users
  • 95% user satisfaction rate
  • 80% reduction in time-to-insight
  • 4x increase in data-driven decision making

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