{"id":26019862,"url":"https://github.com/pavans01/data-engineering-bootcamp","last_synced_at":"2026-05-29T08:31:57.518Z","repository":{"id":278670584,"uuid":"924157579","full_name":"PavanS01/Data-Engineering-Bootcamp","owner":"PavanS01","description":null,"archived":false,"fork":false,"pushed_at":"2025-03-10T14:44:55.000Z","size":61933,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-10T15:35:37.341Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PavanS01.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-29T14:17:21.000Z","updated_at":"2025-03-10T14:45:01.000Z","dependencies_parsed_at":"2025-03-10T15:37:27.777Z","dependency_job_id":null,"html_url":"https://github.com/PavanS01/Data-Engineering-Bootcamp","commit_stats":null,"previous_names":["pavans01/data-engineering-bootcamp"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/PavanS01/Data-Engineering-Bootcamp","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PavanS01%2FData-Engineering-Bootcamp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PavanS01%2FData-Engineering-Bootcamp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PavanS01%2FData-Engineering-Bootcamp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PavanS01%2FData-Engineering-Bootcamp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PavanS01","download_url":"https://codeload.github.com/PavanS01/Data-Engineering-Bootcamp/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PavanS01%2FData-Engineering-Bootcamp/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33644236,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-29T02:00:06.066Z","response_time":107,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-03-06T07:56:29.388Z","updated_at":"2026-05-29T08:31:57.507Z","avatar_url":"https://github.com/PavanS01.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **Data Engineering Bootcamp Portfolio**  \n\nThis repository documents my hands-on exploration of modern data engineering tools and architectures. Below are highlights of the projects I’ve built, optimized, and deployed:  \n\n---\n\n## **🚀 Infrastructure as Code (IaC) \u0026 Cloud Deployment**  \n- **Containerization**: Engineered Docker containers for **Kestra** (data orchestration) and **Airflow** (workflow management), optimizing resource allocation, networking, and persistent storage.  \n- **Terraform on GCP**: Deployed scalable cloud infrastructure (Compute Engine, Cloud Storage, VPC) using Terraform, enabling infrastructure reproducibility and environment parity.  \n- **CI/CD Pipelines**: Integrated Docker Compose and GCP Artifact Registry for automated container builds and deployments.  \n\n---\n\n## **⚡ Orchestrating Data Pipelines**  \n- **NYC Taxi Data ETL**: Designed Airflow DAGs and Kestra flows to ingest, validate, and load 10M+ NYC taxi records into GCP Cloud Storage.  \n  - Backfilled historical data using Airflow’s `TimeDeltaSensor` and dynamic task generation.  \n  - Implemented Kestra’s event-driven triggers for automated pipeline execution based on GCP storage events.  \n- **Observability**: Monitored pipelines via Airflow’s metrics dashboards and Kestra’s execution logs, with alerts routed to Slack.  \n\n---\n\n## **📊 Data Warehousing with BigQuery**  \n- **Schema Design**: Built a partitioned and clustered data warehouse for NYC taxi data, reducing query costs by 60%.  \n- **Advanced SQL**: Wrote complex queries for:  \n  - Time-series analysis of ride trends.  \n  - Geospatial aggregation of pickup/dropoff hotspots.  \n  - Revenue analytics by payment type and vendor.   \n\n---\n\n## **🔧 Analytics Engineering with dbt**  \n- **dbt Project Setup**: Configured dbt Core with BigQuery adapters and automated testing via `dbt test`.  \n- **Modular Data Models**:  \n  - **Staging**: Cleaned raw data using seed files and Jinja macros.  \n  - **Mart Layer**: Built star-schema models (fact trips, dim drivers, dim payment types) for BI tools.  \n- **Documentation**: Generated data lineage graphs and column-level docs using `dbt docs generate`.  \n\n---\n\n## **🛠️ Batch Processing with Apache Spark**  \n- **Spark SQL on Dataproc**: Processed 50GB+ datasets to derive insights:  \n  - Top 10 revenue zones by hour.  \n  - Driver performance rankings.  \n- **ML Modeling**:  \n  - Trained a gradient-boosted tree model (PySpark MLLib) to predict ride durations (RMSE: 8.2 mins).  \n  - Feature engineering: Derived weekday/weekend flags and haversine trip distances.  \n\n---\n\n## **🌊 Streaming with Kafka \u0026 Flink**  \n- **Redpanda Deployment**: Streamed real-time taxi data into topics using Python producers.  \n- **Flink SQL Analytics**:  \n  - **Tumbling Windows**: Rides per 15-minute window.  \n  - **Session Windows**: Identify surge pricing periods.  \n  - **Cumulative Windows**: Running total of daily revenue.  \n- **Sinks**: Stored aggregated results in BigQuery via JDBC connectors with exactly-once semantics.  \n\n---\n\n## **🛠️ Tech Stack**  \n- **Cloud**: GCP (BigQuery, Cloud Storage, Dataproc), Terraform  \n- **Orchestration**: Airflow, Kestra  \n- **Processing**: Spark (PySpark, SQL, MLLib), dbt, Flink SQL  \n- **Streaming**: Kafka (Redpanda), Debezium  \n- **Containers**: Docker, Docker Compose  \n\n---\n\n**💡 Explore the Code**: Dive into the project folders for detailed implementations, architectures, and documentation!  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