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AI-Powered Migration Accelerators

Fast-track your move to GCP and Databricks with intelligent automation, agentic transformation, and industry-ready migration blueprints.

 
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I needed a cost-effective transaction monitoring tool which would identify high-risk transactions, flag potential control weaknesses, improve over time through machine learning, reduce the number of false positives reviewed by the compliance team and be user-friendly in terms of configuration and visualization. konaAI delivers on all counts, and I was very pleased with the choice we made.

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Overview

Our Migration Accelerators combine AI agents, unified pipeline models, and domain-aware automation to simplify and speed up migration of enterprise data platforms to GCP and Databricks.

They enable you to:
  • Assess legacy platforms such as Azure + Databricks, CDP, Talend, Informatica, Pentaho, and on-prem systems.
  • Auto-discover pipelines, schemas, lineage, and workloads.
  • Translate legacy ETL into GCP Dataflow or Databricks-native formats using LLM-powered agents.
  • Generate future-state architecture blueprints optimized for each cloud and domain.
  • Provide full-stack visibility with cost dashboards, ROI models, and interactive recommendations.
  • Accelerate streaming adoption with pre-built real-time blueprints.
accelerators-key-capabilities

What Does This Do Better Than Others?

  • AI-Generated Migration Plans: Uses embedded agents to auto-discover, map, translate, and optimize pipelines end-to-end.
  • Unified Pipeline Information Model (UPIM): Normalizes code and metadata for any source platform, eliminating manual reengineering.
  • Gemini-Powered Transformation: Converts ETL from ADF, Informatica, Talend, Pentaho into production-ready GCP Dataflow pipelines.
  • Databricks-Native Modernization: Automatically produces PySpark, Delta Live Tables, Unity Catalog-ready workflows.
  • Streaming Acceleration: Delivers ready-to-deploy structured streaming architectures with governance built in.
  • Cost Control Automation: Real-time Databricks cost visibility with anomaly alerts and optimization insights.
  • Enterprise-Grade Governance: Integrated observability, quality checks, compliance controls, and CI/CD integration.
  • Multi-Cloud Ready: Built for enterprises operating across AWS, Azure, and GCP.
Explore Real-Time Data Ingestion

How It Works

Streaming-Frameworks
Workflow
  • Discovery Layer
    Auto-extracts pipelines, schemas, lineage, configurations, dependencies from legacy platforms.
  • Unified Pipeline Information Model (UPIM)
    Converts ingested metadata into a normalized, platform-agnostic model.
  • Agentic Transformation Layer a. AI agents translate pipelines into GCP Dataflow, Databricks, or PySpark.
    b. Gemini LLM transforms code.
    c. Code Review Agents validate logic, optimize performance, enforce best practices.
  • Human-in-the-Loop Interface
    Allows overrides, reviews, and workflow approvals.
  • Enterprise Integration Layer
    Integrates with CI/CD, DQ frameworks, privacy and compliance agents, and target data models.
  • 6. Deployment + Optimization
    a. Blueprint generation for GCP, Databricks, and hybrid environments.
    b. Cost dashboards, streaming blueprints, and governance layers deployed.
Streaming-Frameworks
Tech Stack
  • GCP: Dataflow, BigQuery, Vertex AI, Cloud Composer
  • Databricks Delta Lake, Unity Catalog, DLT, Auto Loader
  • AI/LLM Layer Gemini LLM, Agentic Code Transformation
  • Governance DQ checks, observability, lineage tracking
  • Integration CI/CD hooks, YAML-based config deployment
Why-It-Unique

Use Cases

These accelerators apply across modernization and cloud-first transformation programs such as:

  • Migration from ADF, Informatica, Pentaho, Talend to GCP Dataflow
  • Migration from Azure, CDP, or on-prem systems to GCP
  • Migration of ETL workloads to Databricks Lakehouse
  • Building real-time streaming solutions (IoT, clickstream, logs, fraud)
  • Cloud cost optimization for Databricks estates
  • Multi-cloud data estate transformation (AWS, Azure, GCP)
  • Industry-specific modernization—BFSI, Healthcare, Manufacturing, Telecom
Frame 20

Business Benefit of Using This

  • Accelerate time-to-value: Reduce migration time by up to 60%.
  • Immediate cost impact: Save 15–30% on Databricks spend with cost controls.
  • De-risk modernization: Automated code translation, best-practice validation, and governance minimize errors.
  • Future-proof architecture: Move to GCP-native and Databricks-native models aligned to modern AI/ML needs.
  • Lower migration cost: Reduce engineering cycles by 70% using automation and ready blueprints.
  • Predictable ROI: AI-generated business case, consumption forecast, and TCO visibility.
  • Enterprise scalability: Standardized, repeatable migration model across multiple business units and regions.

Experience automated migration in action.

See how our AI-powered accelerators reduce months of effort into days.



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Insights

Frequently Asked Questions

What platforms can I migrate from?

Azure, Azure Data Factory, CDP, Informatica, Talend, Pentaho, on-prem data warehouses, and legacy ETL tools.

Can this handle large, complex enterprise estates?

 Yes. The discovery and UPIM layers are built specifically for multi-thousand-pipeline migrations. 

How accurate is the AI-based code translation?

 Code is validated by LLM Review Agents and human-in-the-loop workflows, ensuring production-ready output. 

Is this only for GCP and Databricks?

 The accelerators primarily target GCP and Databricks but support multi-cloud environments. 

Does it support streaming workloads?

 Yes. Streaming Blueprints Hub provides pre-built architectures for real-time ingestion and processing. 

How fast can a migration be completed?

 Typical timelines reduce by 50–60% compared to manual migrations. 

Is governance included?

 Yes. Data quality checks, lineage, observability, and compliance controls are built into the framework.