Data Modernization

Migrate to modern cloud-native platforms in weeks, not months—with zero disruption to your business

Legacy Systems Holding You Back

Your data infrastructure is slowing you down. On-premise systems are expensive to maintain, difficult to scale, and can't support real-time analytics or modern AI workloads. Migration projects drag on for months with mounting costs and business risk.

You need a modern, cloud-native data platform that scales effortlessly, reduces costs, and enables your teams to move faster. But you can't afford extended downtime, data loss, or months of disruption.

Proven, Risk-Mitigated Cloud Migration

Joon Solutions specializes in rapid, low-risk migrations to modern cloud data platforms. We've successfully migrated 100+ organizations from legacy systems to Google BigQuery, Snowflake, AWS Redshift, and Azure Synapse—with an average timeline of 8-12 weeks from assessment to production cutover.

Our battle-tested migration framework includes automated data validation, parallel running periods, comprehensive rollback plans, and phased cutover strategies that minimize business disruption. We don't just move your data—we optimize your architecture for cloud-native performance and cost efficiency.

100+ successful migrations

8-12 week average timeline

Zero data loss guarantee

End-to-End Modernization Services

Legacy System Migration

Seamless migration from on-premise data warehouses (Oracle, Teradata, SQL Server) to modern cloud platforms with automated ETL conversion

Cloud Platform Implementation

Ground-up implementation of BigQuery, Snowflake, Redshift, or Synapse with optimized architecture, security, and cost controls

Data Pipeline Modernization

Replace brittle ETL scripts with robust, maintainable ELT pipelines using dbt, Fivetran, and orchestration tools like Airflow

Real-Time Data Streaming

Implement streaming architectures using Kafka, Pub/Sub, and cloud-native event systems for instant data availability

Data Architecture Design

Design scalable data mesh, lakehouse, or warehouse architectures aligned with your organizational structure and use cases

Security & Compliance

Implement encryption, access controls, data masking, and audit logging to meet regulatory requirements (GDPR, HIPAA, SOC 2)

Legacy System Migration

Seamless migration from on-premise data warehouses (Oracle, Teradata, SQL Server) to modern cloud platforms with automated ETL conversion

Cloud Platform Implementation

Ground-up implementation of BigQuery, Snowflake, Redshift, or Synapse with optimized architecture, security, and cost controls

Data Pipeline Modernization

Replace brittle ETL scripts with robust, maintainable ELT pipelines using dbt, Fivetran, and orchestration tools like Airflow

Real-Time Data Streaming

Implement streaming architectures using Kafka, Pub/Sub, and cloud-native event systems for instant data availability

Data Architecture Design

Design scalable data mesh, lakehouse, or warehouse architectures aligned with your organizational structure and use cases

Security & Compliance

Implement encryption, access controls, data masking, and audit logging to meet regulatory requirements (GDPR, HIPAA, SOC 2)

Our Accelerated Migration Methodology

A proven 5-phase approach that delivers cloud-native infrastructure in 8-12 weeks with minimal risk

Assessment & Planning

Week 1-2

Inventory current systems, analyze data volumes and complexity, identify dependencies, and create detailed migration roadmap with timeline and resource requirements.

Architecture Design

Week 2-3

Design target cloud architecture optimized for your use cases. Select appropriate services, define security model, and establish cost optimization strategies.

Migration Execution

Week 3-8

Automated schema conversion, incremental data migration, ETL/ELT pipeline development, and comprehensive validation testing with parallel running.

Testing & Go-Live

Week 8-12

Validation testing, phased production cutover with rollback capability, and post-migration performance tuning and cost optimization.

Assessment & Planning

Week 1-2

Inventory current systems, analyze data volumes and complexity, identify dependencies, and create detailed migration roadmap with timeline and resource requirements.

Architecture Design

Week 2-3

Design target cloud architecture optimized for your use cases. Select appropriate services, define security model, and establish cost optimization strategies.

Migration Execution

Week 3-8

Automated schema conversion, incremental data migration, ETL/ELT pipeline development, and comprehensive validation testing with parallel running.

Testing & Go-Live

Week 8-12

Validation testing, phased production cutover with rollback capability, and post-migration performance tuning and cost optimization.

Proven Results

Real-world outcomes from client engagements

Teradata to BigQuery Migration for Global Retailer

Retail

Challenge

A multinational retailer spent $2M annually maintaining aging Teradata infrastructure. Query performance was degrading, scaling was prohibitively expensive, and their data team couldn't support new real-time analytics requirements.

Solution

Migrated 50TB of data and 2,000+ SQL queries from Teradata to Google BigQuery in 10 weeks. Implemented automated ELT pipelines using dbt, established data governance framework, and trained internal team on cloud-native best practices.