Data Modernization & Migration Services.

Audit sources, quality, pipelines, and AI readiness

Deliverables: roadmap, architecture blueprint, risk plan

On-prem → Cloud, SQL ↔ NoSQL, legacy → modern distributed DBs

Tools: AWS DMS, SCT, native replication engines

Outcomes: scalable, high-performance systems

AWS/GCP/Azure cloud-native data architecture

Data lakes, real-time pipelines, serverless processing

Outcomes: AI-ready, low-cost, unified data

Automated ingestion, batch & streaming workflows, schema evolution

Tools: Airflow, Glue, dbt, Spark, Lambda

Outcomes: faster, reliable pipelines

S3 lakehouse, Redshift, Snowflake, BigQuery

Outcomes: faster analytics, unified BI, improved governance

Validation, lineage, anomaly detection, PII masking

Tools: Great Expectations, Amundsen, Collibra

Outcomes: trusted, compliant data

Feature pipelines, embeddings, vector DB integration, training data prep

Tools: SageMaker, MLflow, LlamaIndex, Pinecone

Outcomes: AI-powered applications and reliable ML pipelines

RBAC, encryption, audit logs, GDPR/SOC2/HIPAA readiness

Outcomes: enterprise-grade security