Legacy systems create hidden costs and bottlenecks:
Why Modernize Your Data Architecture?
Slow, expensive analytics
Queries take hours, not seconds.
Poor scalability
Data growth breaks monolithic solutions.
Silos and duplication
Teams can’t trust or find the data they need.
AI/ML roadblocks
Models fail due to weak pipelines and missing metadata.
We build modern, agile, and cost-efficient architectures tailored to your business, whether you’re migrating to the cloud, implementing a lakehouse, or enabling real-time AI.
Our Core Principles for Modern Data Architecture
Principle
Description
Technology Example
Hybrid
Supports cloud, on-premise, and edge deployments
Databricks Lakehouse on AWS + Azure
Data as a Product
Domain-oriented ownership following Data Mesh patterns
Uber-style data domain ownership
Automation
AI-assisted metadata, quality, and security management
Informatica CLAIRE, IBM Watson
Real-Time
Combines streaming and batch data flows
Kafka + Spark Structured Streaming
Openness
API-first and open-source friendly architectures
Snowflake + Apache Iceberg
Audit & Roadmap:
Full audit of existing systems (DWH, lakes, pipelines).
Identify cost and performance inefficiencies.
ROI-based modernization roadmap.
Architecture Design
We deliver cloud, hybrid, and multi-cloud architectures, including:
Cloud Data Warehouses (Snowflake, BigQuery, Redshift).
Lakehouse Platforms (Delta Lake, Apache Iceberg).
Data Fabric (IBM Cloud Pak for Data, Talend Data Fabric, Informatica IDMC, Denodo)