Our Big Data & Analytics Services:

• Data Architecture & Strategy: Design comprehensive data architectures tailored to your business needs and technical requirements. We evaluate your current data landscape, define target architectures for data warehouses, data lakes, and data lakehouses, and create detailed roadmaps for implementation. Our architectures balance scalability, performance, cost, and governance to ensure your data platform grows with your business.
• Legacy Data Extraction & Migration: Extract and migrate data from complex legacy systems, mainframes, and outdated databases to modern platforms. We design custom extraction processes that handle diverse data formats, maintain data integrity, preserve historical context, and minimize disruption to ongoing operations. Our approach ensures complete, accurate data transfer while addressing technical debt and data quality issues.
• Modern Data Pipeline Development: Build robust, scalable data pipelines that automate data ingestion, transformation, and delivery. We implement real-time and batch processing workflows using modern technologies like Apache Kafka, Apache Spark, Apache Airflow, and cloud-native services. Our pipelines are designed for reliability, observability, and easy maintenance, with comprehensive error handling and monitoring.
• ETL/ELT Process Architecture: Design and implement sophisticated Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes optimized for modern cloud and on-premises environments. We architect scalable transformation logic, implement incremental loading strategies, optimize for performance and cost, and ensure data consistency across systems. Our solutions support both traditional batch processing and streaming data workflows.
• Data Warehouse Design & Implementation: Build enterprise-grade data warehouses using platforms like Snowflake, Amazon Redshift, Google BigQuery, and Azure Synapse Analytics. We design optimal data models (star schema, snowflake schema, data vault), implement efficient partitioning and clustering strategies, optimize query performance, and establish governance frameworks that ensure data quality and accessibility.
• Data Lake & Lakehouse Architecture: Create modern data lakes and lakehouses that combine the flexibility of data lakes with the structure and governance of data warehouses. We implement solutions using technologies like Delta Lake, Apache Iceberg, and Apache Hudi that enable ACID transactions on data lakes, support both structured and unstructured data, enable schema evolution, and provide unified batch and streaming analytics.
• Data Product Development: Discover, design, and build data products that deliver specific business value. We work with stakeholders to identify high-impact data opportunities, design intuitive data products and APIs, implement self-service analytics capabilities, and establish data product governance. Our approach treats data as a product with defined owners, consumers, and quality standards.
• Analytics Flow Design with ML Integration: Architect end-to-end analytics workflows that incorporate machine learning models for predictive and prescriptive insights. We design feature engineering pipelines, implement model serving infrastructure, create real-time scoring capabilities, and establish monitoring for model performance and drift. Our solutions seamlessly integrate ML predictions into business processes and decision-making workflows.
• Real-Time Analytics & Streaming: Build real-time analytics platforms that process and analyze data as it arrives. We implement streaming architectures using technologies like Apache Kafka, Apache Flink, and cloud streaming services, design real-time dashboards and alerting systems, and optimize for low-latency processing. Our solutions enable immediate insights and rapid response to changing conditions.
• Data Governance & Security: Implement comprehensive data governance frameworks that ensure data quality, security, and compliance. We design and implement data cataloging and lineage tracking, establish data quality monitoring and validation, implement role-based access controls and data masking, and ensure compliance with regulations including GDPR, HIPAA, CCPA, and industry-specific requirements.
• PHI/PII Protection & Compliance: Protect sensitive Protected Health Information (PHI) and Personally Identifiable Information (PII) with robust security controls and compliance frameworks. We implement data encryption at rest and in transit, design data anonymization and pseudonymization strategies, establish audit trails and access monitoring, implement data retention and deletion policies, and ensure compliance with HIPAA, HITECH, and other healthcare and privacy regulations.
• Business Intelligence & Visualization: Create compelling data visualizations and interactive dashboards that make complex data accessible to all stakeholders. We implement BI solutions using tools like Tableau, Power BI, Looker, and custom visualization frameworks. Our solutions provide intuitive interfaces, self-service analytics capabilities, and embedded analytics within existing applications.
• Data Quality & Observability: Establish comprehensive data quality frameworks and observability practices that ensure trust in your data. We implement automated data quality testing, anomaly detection, data profiling and validation, lineage tracking, and monitoring systems that provide visibility into data health, pipeline performance, and system reliability.
• Cloud Data Platform Migration: Migrate on-premises data infrastructure to modern cloud platforms (AWS, Azure, GCP) or implement hybrid architectures. We design migration strategies that minimize risk and downtime, optimize cloud resource utilization and costs, implement multi-cloud and hybrid solutions, and ensure seamless integration with existing systems.
• DataOps & Automation: Implement DataOps practices that bring DevOps principles to data engineering and analytics. We establish CI/CD pipelines for data code, implement infrastructure as code for data platforms, automate testing and quality validation, and create collaborative workflows that improve speed and reliability of data delivery.
• Data Mesh & Decentralized Architecture: Implement data mesh architectures that distribute data ownership and enable domain-driven data products. We help organizations transition from centralized to federated data architectures, establish data product standards and governance, implement self-serve data infrastructure, and create interoperable data products across domains.