Data Engineering

Data Engineering is the backbone of every data-driven organization. It involves the design, construction, and optimization of systems that collect, store, and process massive volumes of structured and unstructured data. Our expertise lies in building robust, scalable, and secure data pipelines using both Linux and .NET ecosystems, ensuring smooth data flow from source to destination.

Technical Focus

  • Infrastructure & OS: Optimized for Linux-based systems, leveraging tools such as systemd, cron, and Bash for automation, and Docker and Kubernetes for containerized deployments.
  • .NET & Integration: High-performance data ingestion and ETL services using .NET 8, C#, and Entity Framework Core, integrating with SQL and NoSQL data stores.
  • Languages & Frameworks: Python (Pandas, PySpark), Scala/Java (Apache Spark, Kafka Streams), and C# for integration layers.
  • Data Systems & Tools: PostgreSQL, SQL Server, MongoDB, Cassandra, Apache Spark, Kafka, Airflow, NiFi, AWS Glue, Azure Data Factory, and GCP Dataflow.

Core Competence: We design data pipelines that are modular, resilient, and cloud-agnostic — ensuring rapid data availability and transformation at scale.

Data Modeling

Data Modeling defines the structure, relationships, and flow of data across an organization’s ecosystem. It acts as the blueprint for data storage, integration, and analytics. Our approach unites modern modeling methodologies with both Linux and .NET environments to ensure consistency, scalability, and interoperability across all data systems.

Technical Focus

  • Modeling Layers: We design conceptual, logical, and physical models that align with business domains and technical requirements, following established principles such as 3NF, dimensional modeling, and data vault techniques.
  • Tools & Frameworks: Leverage ER/Studio, SQL Server Data Tools (SSDT), and open-source alternatives like pgModeler and DbSchema for visual and automated modeling workflows.
  • Integration with .NET: Using Entity Framework Core, LINQ, and Code-First or Database-First approaches, we ensure seamless synchronization between conceptual data models and .NET application domains.
  • Linux Ecosystem: Our modeling environments operate smoothly on Linux, utilizing command-line tools, Docker containers, and automation scripts for schema management, migrations, and version control.
  • Data Platforms: Experience with PostgreSQL, Microsoft SQL Server, MySQL, and Snowflake, as well as modern analytical models on Azure Synapse and Databricks.
  • Languages & Standards: SQL (DDL/DML), C#, Python (SQLAlchemy, Alembic), and JSON Schema for structured and semi-structured data modeling.

Best Practices

  • Adherence to data normalization and referential integrity standards.
  • Design for performance optimization — including indexing, partitioning, and caching strategies.
  • Support for versioned data models using Git-based workflows and automated migration pipelines.
  • Integration of semantic models (e.g., Power BI Tabular or Azure Analysis Services) for enterprise analytics.

Core Competence: We design consistent and future-ready data architectures that bridge analytics, engineering, and development teams — ensuring data integrity, adaptability, and enterprise-wide alignment under Linux and .NET environments.

Data Science

Data Science transforms raw data into actionable insights using statistical modeling, machine learning, and AI. We specialize in end-to-end data science workflows — from data exploration to production-ready model deployment — using Linux environments for computation and .NET for integration with enterprise applications.

Technical Focus

  • Model Development: Using Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and R for statistical analysis and predictive modeling.
  • Computing Environment: Linux provides the foundation for reproducible research, GPU acceleration, and scalable training via Docker, Kubernetes, and MLflow.
  • Integration with .NET: Using ML.NET, ONNX Runtime, and .NET APIs to bridge trained models with enterprise systems written in C#, ensuring smooth deployment into production pipelines.
  • Languages & Libraries: Python for modeling, C#/.NET for service orchestration, Bash/YAML for automation and CI/CD.
  • Platforms & Tools: JupyterLab, VS Code, JetBrains Rider, GitHub Actions, Azure ML, and Kubeflow.

Core Competence: We deliver data-driven intelligence by merging the statistical power of open-source ecosystems with the reliability and performance of the .NET platform.

Data Analysis

Data Analysis focuses on interpreting, visualizing, and reporting data to guide strategic decisions. We provide analytical frameworks that combine Linux-based environments with .NET backends and APIs to deliver insights efficiently and interactively.

Technical Focus

  • Analytical Stack: Python (Pandas, Matplotlib, Seaborn), Power BI, Tableau, and .NET Blazor dashboards for data visualization, with SQL and LINQ for querying large datasets.
  • .NET Integration: Using ASP.NET Core and Entity Framework to build analytical APIs and reporting backends connected to live data sources for real-time insights.
  • Linux Operations: Analytical workloads run on Linux servers, leveraging cron jobs, Shell scripts, and container orchestration for automation and reliability.
  • Data Sources: Integration with relational, NoSQL, and cloud storage including PostgreSQL, MySQL, ElasticSearch, and Azure Data Lake.
  • Visualization & Reporting: From interactive dashboards to static PDF reports, powered by open-source and Microsoft ecosystems alike.

Core Competence: We empower organizations with data visibility — combining analytical depth, automation, and enterprise-grade integration under Linux and .NET platforms.