Natural Language → Spark SQL
Qrynix transforms plain English business questions into optimized Spark SQL queries — helping enterprises accelerate analytics, reduce engineering effort, and democratize access to modern data platforms.
Qrynix enables analysts, engineers, and business teams to interact with Spark-powered data platforms using natural language. Instead of manually writing complex SQL, teams generate optimized queries instantly — reducing friction and accelerating time-to-insight across the entire organization.
A simple, powerful loop that transforms how your team interacts with data
Users ask business questions in plain English — no SQL knowledge required. Qrynix understands context, intent, and your data schema.
AI generates optimized, production-ready Spark SQL queries automatically — taking schema structure, relationships, and best practices into account.
Teams analyze data faster, reduce engineering bottlenecks, and improve decision-making velocity across every function in the business.
Everything your team needs to unlock self-service analytics on enterprise-scale data platforms
Convert plain English business questions into optimized Spark SQL queries using AI-assisted generation — with context awareness and schema understanding built in.
GenAI · Schema-AwareReduce development effort by up to 10x. Accelerate reporting cycles, data exploration, and analytics workflows — without sacrificing query quality or performance.
10x Speed · Lower CostDesigned for modern lakehouse ecosystems — natively compatible with Apache Spark, Snowflake, and Microsoft Fabric environments at any scale.
Spark · Snowflake · FabricQrynix understands your table relationships, column types, and naming conventions — generating queries that reflect your actual data model, not generic templates.
Metadata-Aware · Context-RichDesigned with security and governance in mind — integrates with your existing access controls and data governance frameworks without exposing raw model internals.
Secure · GovernedInstall with a single pip command and integrate into any Python data workflow — works with notebooks, pipelines, applications, and CLI tooling out of the box.
pip install qrynixInstall Qrynix directly from PyPI and start building AI-powered analytics workflows in your existing Python environment — no complex setup, no infrastructure changes required.
One command gets you started — works in any Python 3.8+ environment
Pass in your table schema so Qrynix understands your data model
Ask business questions in plain English — get production-ready Spark SQL instantly
# Install pip install qrynix # Import and initialise from qrynix import Qrynix # Define your schema schema = { "sales.transactions": [ "customer_id", "revenue", "region", "quarter" ] } # Initialise Qrynix q = Qrynix(schema=schema) # Ask a business question sql = q.ask( "Top 10 customers by revenue, broken down by region" ) print(sql) # → Optimized Spark SQL ready to run
Built for every role that needs fast, reliable answers from enterprise data
Enable business analysts and non-technical stakeholders to explore enterprise data without SQL expertise — democratizing access to insights across the organization.
Improve productivity for data engineers and analytics teams working with Spark ecosystems — accelerating query development, prototyping, and reporting workflows.
Bring conversational AI experiences to modern lakehouse and enterprise data platforms — making your Fabric, Spark, or Snowflake investment accessible to every team.
Qrynix is designed to integrate seamlessly with the platforms you already use
Decyra helps enterprises design AI-native architectures, modern data platforms, and intelligent analytics systems — with Qrynix at the core of the analytics experience.
