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Years Experience
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Conceptual Data Modeling
Logical Data Modeling
Physical Data Modeling
Data Warehouse & BI Modeling
NoSQL & Big Data Schema Design
Metadata & Master Data Management (MDM)
Data Integration & ETL/ELT Modeling
Reference & Lookup Data Modeling
Data Vault & Anchor Modeling
Performance Tuning & Index Strategy
We design high-level conceptual data models that represent the core business entities and their relationships. This forms the foundation for aligning data architecture with business goals, helping stakeholders understand and validate data requirements before technical design begins.
Our logical data models translate business concepts into structured models with defined attributes, keys, and relationships—independent of any physical database. This stage ensures consistency, normalization, and scalability of your data design, preparing it for future integration and system growth.
We create optimized physical models tailored to specific databases like SQL Server, PostgreSQL, Oracle, or Snowflake. With an emphasis on indexing, storage, partitions, and performance, our physical models ensure your systems are secure, efficient, and production-ready from day one.
Build reliable and scalable BI platforms with dimensional modeling techniques like star and snowflake schemas. We design modern data warehouse architectures that support reporting, dashboards, and ad hoc analysis—enabling fast, consistent, and high-quality data delivery to decision-makers.
We architect flexible schemas for NoSQL databases like MongoDB, Cassandra, and HBase to support high-velocity and semi-structured data. Our designs follow best practices for performance, scalability, and data access patterns—ideal for big data and real-time analytics environments.
We structure metadata and master data frameworks that unify data definitions, hierarchies, and attributes across platforms. This improves data governance, reduces duplication, and ensures accurate, trusted data across business domains such as customers, vendors, products, and more.
We design ETL/ELT data models for seamless data extraction, transformation, and loading between sources and targets. Our models ensure data accuracy, lineage traceability, and compatibility with tools like Talend, Informatica, Azure Data Factory, and Databricks—supporting efficient pipelines.
We create well-structured models for reference and lookup data—ensuring standardization across systems, easy maintenance, and better validation. Our approach reduces redundancy, enhances data quality, and supports smoother integration and reporting across enterprise applications.
Our experts design data vault and anchor models for agile, scalable, and auditable enterprise data warehouses. These models support historical tracking, change management, and regulatory compliance, making them ideal for large-scale systems with complex data history needs.
We optimize your database performance through tailored indexing strategies, query optimization, partitioning, and schema refactoring. Our data models are designed for performance at scale, ensuring faster response times, reduced I/O overhead, and improved system reliability.
Conceptual Data Modeling . Logical Data Modeling . Physical Data Modeling . NoSQL & Big Data Schema Design . Reference & Lookup Data Modeling . NoSQL & Big Data Schema Design
Conceptual Data Modeling . Logical Data Modeling . Physical Data Modeling . NoSQL & Big Data Schema Design . Reference & Lookup Data Modeling . NoSQL & Big Data Schema Design