End-to-end Data Lakehouse project built on Databricks, following the Medallion Architecture (Bronze, Silver, Gold). Covers real-world data engineering and analytics workflows using Spark, PySpark, SQL, Delta Lake, and Unity Catalog. Designed for learning, portfolio building, and job interviews.

FinOps repository: An open-source framework for multi-cloud cost visibility. Extendable with dlt.

Building a next-generation hybrid data pipeline architecture that combines the power of Microsoft Fabric, Azure Cloud, and Power BI. This pipeline is engineered to tackle the challenges of real-time data ingestion, multi-layered processing, and analytics, delivering business-critical insights.

MCP agent for semantic metrics governance with built-in trust scoring, lineage visualization, and conversational metric definition. Designed for data teams working with dbt, LookML, and modern semantic layers.

Enterprise-grade ingestion blueprint for Postgres to Databricks powered by dlt. Features dual-mode operation (Full Load + CDC Load) and robust CI/CD via Databricks Asset Bundles.

Фабрика DAG

Realtime Data Streaming | End-to-End Data Engineering Project

Build a data pipeline on Google Cloud using an event-driven architecture, leveraging GCS, Cloud Run functions, and BigQuery. Explore both VM and Composer options for Airflow management, and utilize Logging & Monitoring for pipeline health. Discover how SQL-based BigQuery ML can be used for initial ML implementation in specific scenarios.

End-to-end Azure Data Engineering pipeline using ADF, Databricks (PySpark), ADLS Gen2, Azure SQL, and Power BI for COVID-19 analytics

infrastructure_for_data_engineer_kafka

Инфраструктура для data engineer S3

Фабрика DAG через SCD-таблицу с конфигурациями

Power BI dashboard analyzing client credit default patterns

Всё что нужно знать про DuckDB

A collection of real-world projects, code-alongs, and competitions completed on DataCamp.

Leveraging AWS Cloud Services, an ETL pipeline transforms YouTube video statistics data. Data is downloaded from Kaggle, uploaded to an S3 bucket, and cataloged using AWS Glue for querying with Athena. AWS Lambda and Glue converts to Parquet format and stores it in a cleansed S3 bucket. AWS QuickSight then visualizes the materialised data.