Migrate to Modern Data Platforms, AI-Powered
End-to-end migrations from legacy warehouses and cloud platforms, including Databricks, Snowflake, BigQuery, and Redshift, to the modern data architecture that fits your strategy. Our proprietary AI-powered accelerators compress timelines by up to 30% while automated reconciliation ensures data integrity at every stage.
AI-Powered Data Migration
We simplify migration from legacy platforms to modern cloud ecosystems using AI-driven automation, ensuring zero data loss and minimal business disruption.
Legacy / On-Prem DWH
Cloud Data Warehouses
Fragmented Multi-Cloud Setups
Siloed Data Infrastructure
Modern Lakehouse
Unified Analytics PlatformCloud-Native Data PlatformWhy Data Migrations Stall
Migration projects are high-stakes and high-complexity. The most common failure modes are predictable, and the right tooling and methodology can address each one systematically.
SQL Dialect Complexity
Thousands of queries, stored procedures, and views need conversion across SQL dialects. Manual translation is slow, error-prone, and doesn’t scale beyond a few hundred objects.
Manual Reconciliation
Traditional recon processes lack the scalability to handle large data volumes. Discrepancies slip through, eroding trust in the migrated environment and delaying production cutover.
Business Disruption
Extended timelines, data quality gaps, and missed SLAs create downstream impact across reporting, compliance, and operational teams that depend on the data platform daily.
AI-Powered Query Conversion at Scale
We leverage AI-driven automation to handle SQL dialect conversion for easy-to-medium complexity queries, freeing your engineers to focus on the complex transformations that demand human judgment.
Validation (Count & Hash)
Prompt Processing
SQL Dialect Identification
Schema Matching
Agentic Invocation
SQL Output Generation
Validation (Count & Hash)
Prompt Processing
SQL Dialect Identification
SQL Output Generation
Agentic Invocation
Schema Matching
20–30% Faster Timelines
AI-powered automation handles routine query conversions, compressing overall migration schedules significantly.
Scalable Multi-Dialect Support
Works across SQL dialects and target platforms, with minimal scope for errors in the converted output.
3-Layer Validation Framework
Every converted query passes through count checks, hash checks, and lineage validation to catch errors before downstream impact.
Migration Accelerators and Frameworks
Purpose-built, platform-agnostic tools that eliminate the most time-consuming and error-prone aspects of data platform migrations.
Agentic Query Converter
Automates SQL dialect translation across any source-target combination, handling easy-to-medium complexity queries with AI-powered conversion and multi-layer validation.
- SQL dialect identification and schema matching
- Embedding-based prompt processing
- Count and hash check validation
- Early error detection to prevent downstream ripple
Data Reconciliation Framework
Source-agnostic automated validation that ensures data integrity across any source and target system through multi-dimensional checks and visual dashboards.
- Schema-level and table-level validation
- Row-by-row hash comparison
- Automated dashboards with pass/fail reporting
- Stale data and count mismatch detection
CDC Ingestion Accelerator
Streamlines change data capture from any SQL warehouse into modern platforms with automated schema evolution, data masking, and real-time monitoring.
- Real-time CDC capture and propagation
- Automatic schema evolution handling
- Built-in data masking for compliance
- Monitoring dashboard with status updates
A Proven Four-Phase Framework
Every migration follows a structured approach refined through dozens of enterprise engagements — across platforms like Databricks, Snowflake, BigQuery, and Synapse — designed to minimize disruption and maximize data accuracy regardless of source or target platform.
Discover
- Infrastructure and architecture audit
- Volumetrics and business context mapping
- Stakeholder alignment with platform teams
- Pain point identification (cost, latency, performance)
Plan
- Target architecture and data model alignment
- Scope definition with clear prerequisites
- Sprint planning with detailed milestones
- Code and script inventory handover
Migrate
- Data ingestion, DDL migration, schema creation
- Query conversion with AI-powered acceleration
- Workflow and schedule migration
- Governance and access control implementation
Validate
- Automated multi-dimensional data reconciliation
- Governance and RBAC testing
- Performance benchmarking and optimization
- UAT and production edge case testing
Migration Case Studies
Measurable outcomes from complex, enterprise-scale migration engagements across industries and platforms.
From Cloud Chaos to Clarity: Rethinking Your Migration Strategy
What once felt streamlined can start to feel fragmented: pipelines scattered across tools, rising operational overhead, and performance tuning that eats into agility. Over time, even the most well-intentioned architectures can begin to show their limits.
The Move to Databricks: How This Leading SaaS Player Saved 30% on Data Analysis Costs
A leading SaaS company successfully migrated from Google Cloud Platform to Databricks, resulting in significant cost savings and performance improvements.
QConvert: Automated Databricks SQL Migration
As a Data & AI specialist, we have seen this play out multiple times. To help businesses modernize their data stacks in an automated, quick and error free manner, we have developed an AI-powered tool that automates SQL translation from legacy dialects to Databricks SQL.
We Have Done This Before
Whatever is driving your migration, we bring deep platform expertise and battle-tested tooling to every engagement.
Coupled storage-compute models, unpredictable credit consumption, and rising infrastructure costs push organizations to rethink their data platform strategy. We help assess total cost of ownership, design cost-efficient target architectures, and execute the migration with minimal disruption.
Teams running analytics on one platform and ML on another face data duplication, governance gaps, and operational overhead. We consolidate workloads onto a unified platform that supports both BI and ML natively, reducing fragmentation and enabling faster experimentation.
Strategic vendor realignment, enterprise license negotiations, or multi-cloud simplification often require moving entire data ecosystems between cloud environments. We have delivered multi-petabyte cross-cloud migrations with custom workspace migration utilities and performance parity validation.
Query bottlenecks, missed SLAs, and platforms struggling under growing data volumes are common triggers. We migrate workloads to environments optimized for your performance profile, benchmark rigorously, and deliver measurable latency and throughput improvements.
Fragmented access controls, inconsistent data cataloging, and regulatory pressure drive organizations toward platforms with centralized governance. We implement unified access controls, data lineage, and compliance frameworks as part of every migration.
Aging on-prem infrastructure with rising maintenance costs, shrinking talent pools, and inability to support modern workloads. We assess the existing architecture, design the cloud-native target model, and execute phased migrations with automated reconciliation at every stage.
Ready to Modernize Your Data Platform?
Whether you're migrating from legacy warehouses or consolidating cloud platforms, we'll help you build a modern data architecture that delivers results.