Data Solutions
Building efficient data pipelines, extracting insights through analytics and machine learning, and modeling complex relationships with knowledge graphs.
What Is the Data Discipline?
Data work combines engineering, science, and architecture. It means building scalable infrastructure for data movement, applying statistical modeling and machine learning, and using semantic structures like knowledge graphs to capture complex relationships.
Guiding Principles
- Business understanding drives technical decisions.
- Data quality and governance are non-negotiable.
- Efficient pipelines enable reliable analytics.
- Models must be explainable and reproducible.
- Complex relationships deserve graph structures.
- Optimize for both performance and maintainability.
- Privacy, safety, compliance, and security by default.
- Test on small datasets before scaling to large ones.
- Each query, each transformation, and each machine has a cost.
Integrated Data Workflow
- Problem Definition & Architecture: Understand business objectives and design the data architecture (warehouse, lake, lakehouse). Establish KPIs and align stakeholder expectations.
- Data Pipelines: Build robust ETL/ELT pipelines from SQL databases, APIs, streaming sources, and files.
- Data Quality & Governance: Clean and transform data at scale. Handle missing values, remove duplicates, and document transformations.
- Exploratory Analysis & Data Modeling: Perform EDA, model data as knowledge graphs, and design ontologies for semantic queries.
- Feature Engineering & Storage: Optimize storage, create features, and leverage databases like Neo4j.
- Modeling & Analytics: Train, tune, and integrate models with knowledge graphs via embeddings.
- Evaluation & Optimization: Assess performance, detect overfitting, optimize queries and pipelines.
- Deployment & Monitoring: Deploy with CI/CD, build dashboards, monitor data quality, and retrain models as needed.