We are interested in the following topics

• Analytical Engineering & BI

  • Architectural patterns for data warehouses and their pitfalls.
  • Examples of building enterprise data warehouses and lessons learned.
  • dbt and modern data transformation tools.
  • Superset, DataLens and other relevant BI tools: implementation, application and customization practices.
  • Native embedded engines and use cases for analytics and BI tasks: DuckDB, Polars, chDB and others.
  • Optimizing SQL queries and building efficient analytical models.
• Data Engineering & DB Internals

  • The architecture of analytics platforms: from DWH to Data Lake and Data Lakehouse.
  • Features, use cases and other pitfalls of Open Data Formats (Iceberg, Hudi, Delta Lake and others).
  • MPP databases: architecture, peculiarities of work, successful and not so successful cases of application.
  • Data integration: low-code solutions, CDC and other approaches.
  • Orchestration: from the banal Airflow to the exotic and interesting Kestra and Flyte.
  • Data monitoring and quality assurance: frameworks, platforms and implementation practices.
  • Data stack on Kubernetes and S3: experiences, pitfalls, recommendations.
  • Big and very big data platforms based on good old Hadoop and YT.
  • Database Internals: how modern distributed databases are organized.
• ML Engineering & MLOps

  • Developing own ML platforms or open source implementations (Kubeflow, etc.).
  • Experiment Tracking (Mlflow, neptune.ai, Weights & Biases, etc.).
  • Building own Feature Stores or open source implementation and customization.
  • Monitoring and exploitation of models in production.
  • Vector DBs and modern RAG pipelines.
  • CI/CD for ML projects: automating model training and deployment.
  • Distributed learning platforms for large ML models: design, operation, and internals.