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Each project demonstrates the solution of a real-world problem using production-grade practices.\n\n⸻\n\nRoadmap\n\n\t•\tdbt NYC Taxi Analytics project\n\t•\tDatabricks Lakehouse project\n\t•\tAzure Data Engineering pipelines\n\t•\tAWS Data Engineering pipelines\n\t•\tSnowflake Data Warehouse\n\t•\tAirflow orchestration example\n\t•\tReal-time streaming (Kafka, Kinesis, Event Hubs)\n\n⸻ \n\nProjects\n\n1. NYC Taxi Analytics (dbt + Postgres) -- Implemented\n\nTech: dbt, PostgreSQL, GitHub Actions (optional CI), VS Code Power User extension\n\n\t•\tProblem: NYC taxi data is large and messy (millions of rows, multiple schemas).\n\t•\tGoal: Transform raw taxi trips into clean, analytics-ready marts for insights like revenue, trip volume, and airport traffic.\n\t•\tHighlights:\n\t•\tStaging → Intermediate → Marts modeling with dbt\n\t•\tIncremental model for fct_trip (~3.4M rows handled efficiently)\n\t•\tSource freshness checks \u0026 dbt tests (not null, unique, constraints)\n\t•\tExposures defined for a dashboard dependency graph\n\t•\tDAG lineage visualized via dbt Docs\n\nFolder: dbt-project/nyc_taxi_analytics\n\n2. Databricks Lakehouse Project (coming soon)\n\nTech: Databricks, Delta Lake, PySpark\n\n\t•\tFocus on ingesting raw JSON/Parquet data into a bronze-silver-gold pipeline.\n\t•\tDemonstrates Delta Lake merges, deletes, and time travel.\n\t•\tWill include feature engineering with PySpark.\n\nFolder: databricks-lakehouse (planned)\n\n⸻\n\n3. Azure Data Engineering Project -- Partially Implemented\n\nTech: Azure Data Factory, Synapse, Databricks, Fabric, Event Hubs\n\n\t•\tEvent-driven ETL pipelines with ADF + Synapse.\n\t•\tReal-time ingestion with Event Hubs → CosmosDB → Synapse Link.\n\t•\tCost optimization \u0026 monitoring with Application Insights.\n\nFolder: azure-etl (partially implemented)\n\n⸻\n\n4. AWS Data Engineering Project (coming soon)\n\nTech: AWS Glue, Redshift, S3, Lambda, Kinesis\n\n\t•\tServerless ETL with Glue + Lambda.\n\t•\tReal-time streaming ingestion with Kinesis → S3 → Redshift.\n\t•\tData Lakehouse architecture on S3 with partitioning \u0026 lifecycle policies.\n\nFolder: aws-etl (planned)\n\n⸻\n\n5. Snowflake Data Warehouse Project (coming soon)\n\nTech: Snowflake, dbt, Airflow\n\n\t•\tBuilding a cloud-native data warehouse with Snowflake.\n\t•\tOrchestration and scheduling via Airflow.\n\t•\tShowcases Snowflake features: zero-copy cloning, streams \u0026 tasks, and time travel.\n\nFolder: snowflake-dwh (planned)\n\n⸻\n\nSetup Instructions (for dbt project)\n\nRequirements\n\n\t•\tPython 3.11+\n\t•\tdbt-core \u0026 dbt-postgres\n\t•\tPostgreSQL running locally (or Docker)\n\nRun Locally\n\n# Clone repo\n\ngit clone https://github.com/williamnsambu/modern-data-stack-lab.git\n\ncd modern-data-stack-lab/dbt-project/nyc_taxi_analytics\n\n# Create virtual env\npython3.11 -m venv .venv\nsource .venv/bin/activate\n\n# Install dependencies\npip install -r requirements.txt   # or install dbt manually\n\n# Run dbt models\nexport DBT_PROFILES_DIR=$(pwd)\ndbt debug\ndbt run\ndbt test\n\n# Generate docs\ndbt docs generate\ndbt docs serve\n\n⸻\n\nContributing\n\nThis is a personal lab/portfolio, but feel free to open issues or PRs with suggestions.\n\n⸻\n\nAuthor\n\nWilliam Nsambu\nSoftware \u0026 Data Engineer | Cloud Solutions | Modern Data Stack Enthusiast\n\nLinkedIn: www.linkedin.com/in/william-nsambu-a5467ab2 | GitHub: https://github.com/williamnsambu\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwilliamnsambu%2Fmodern-data-stack-lab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwilliamnsambu%2Fmodern-data-stack-lab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwilliamnsambu%2Fmodern-data-stack-lab/lists"}