Build Data Platforms That Power Innovation
Design and deploy scalable data platforms, ML infrastructure, and analytics solutions. We help organizations build the data foundations needed for AI, machine learning, and advanced analytics.
Data Platform Design
Design modern data platforms using cloud-native technologies
Cloud Data Warehouses
Build scalable data warehouse architectures on modern cloud platforms. Design for performance, cost efficiency, and analytical workloads.
- Cloud data warehouse architecture (Snowflake, BigQuery, Redshift)
- Schema design and data modeling best practices
- Query optimization and performance tuning
- Cost monitoring and resource optimization
Data Lakes & Lakehouses
Implement data lakes and lakehouse architectures that unify structured and unstructured data. Enable both analytics and ML workloads on a single platform.
- Data lake and lakehouse implementation (Databricks, Delta Lake)
- Real-time streaming architectures (Kafka, Kinesis)
- Data mesh and decentralized architecture design
- Object storage optimization and partitioning strategies
ML Infrastructure
Build production-grade machine learning infrastructure
MLOps Platforms
Implement MLOps platforms for automated model training, deployment, and monitoring. Establish CI/CD pipelines for machine learning workflows.
- MLOps platform setup (MLflow, Kubeflow, SageMaker)
- Model training and deployment pipelines
- Experiment tracking and model versioning
- Automated retraining and deployment workflows
Feature Engineering
Build feature stores and engineering pipelines that standardize feature creation across teams and projects. Enable feature reuse and consistency.
- Feature store implementation and management
- Online and offline feature serving
- Model performance monitoring and drift detection
- A/B testing and model comparison frameworks
Data Engineering & Pipelines
Design robust data pipelines for ETL/ELT processes
ETL/ELT Pipelines
Build reliable data pipelines that extract, transform, and load data at scale. Ensure data quality and consistency across your data ecosystem.
- ETL/ELT pipeline development (Airflow, dbt, Fivetran)
- Data transformation and business logic implementation
- Incremental data processing and optimization
- Error handling and data recovery strategies
Data Quality & Orchestration
Implement data quality frameworks and orchestration tools. Monitor data health and automate pipeline workflows across your data infrastructure.
- Data quality and validation frameworks
- Data profiling and anomaly detection
- Data orchestration and workflow automation
- SLA monitoring and alerting
Analytics & Governance
Enable self-service analytics with robust governance
Business Intelligence
Implement BI platforms that empower business users to explore data and create insights. Design semantic layers and data models for self-service analytics.
- BI platform implementation (Tableau, Power BI, Looker)
- Semantic layer and metrics framework design
- Dashboard and report development
- User training and adoption programs
Data Governance
Establish data governance frameworks that ensure data quality, security, and compliance. Implement policies and tools for responsible data management.
- Data catalog and metadata management
- Data governance policies and compliance frameworks
- Access control and data lineage tracking
- Data privacy and security compliance
Real Results
E-Commerce Platform
Challenge
A growing e-commerce platform needed to consolidate data from 15+ sources and build ML models for personalization, but their legacy infrastructure couldn't scale.
Our Approach
We designed a modern data lakehouse on Databricks, implemented real-time data pipelines, and built MLOps infrastructure for recommendation models.
Results
Ready to Build Your Data Platform?
Let's discuss how modern data architecture can power your AI and analytics initiatives.
Schedule Data Strategy Session