{"id":24287611,"url":"https://github.com/manuelguerra1987/data-engineering-zoomcamp-notes","last_synced_at":"2025-08-23T02:34:52.236Z","repository":{"id":268804462,"uuid":"899787667","full_name":"ManuelGuerra1987/data-engineering-zoomcamp-notes","owner":"ManuelGuerra1987","description":"Notes and material from 2025 Data Engineering Zoomcamp by 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Data Engineering Zoomcamp 2025\n\nThis repo contains notes and homeworks for the [Data Engineering Zoomcamp](https://github.com/DataTalksClub/data-engineering-zoomcamp) by [Datatalks.Club](https://datatalks.club/)\n\n\n\n\n## Syllabus\n\n### [Module 1: Containerization and Infrastructure as Code](1_Containerization-and-Infrastructure-as-Code/)\n\n* Docker and docker-compose\n* Running Postgres in a container\n* Ingesting data to Postgres with Python\n* Running Postgres and pgAdmin with Docker-compose\n* Google Cloud Platform (GCP)\n* Terraform\n* Setting up infrastructure on GCP with Terraform\n\n### [Module 2: Workflow Orchestration with Kestra](2_Workflow-Orchestration-(Kestra)/)\n\n* Introduction to Workflow Orchestration\n* Introduction to Kestra\n* Launch Kestra using Docker Compose\n* ETL Pipelines: Load Data to Local Postgres\n* ETL Pipelines: Load Data to Google Cloud Platform (GCP)\n\n### [Module 3: Data Warehouse with BigQuery](3_Data-Warehouse/)\n\n* OLAP vs OLTP\n* Data Warehouse\n* BigQuery\n* Creating an external table\n* Partitioning and clustering\n* BigQuery best practices\n\n### [Module 4: Analytics Engineering with dbt](4_Analytics-Engineering/)\n\n* Introduction to analytics engineering\n* Introduction to dbt\n* Setting up dbt with bigquery\n* Development of dbt Models\n* Building the model\n* Testing and documenting\n* Deployment \n* Visualizing the data \n\n### [Module 5: Batch Processing with spark](5_Batch-Processing-Spark/)\n\n* Introduction to Batch Processing\n* Introduction to Spark\n* Spark SQL and DataFrames\n* Spark Internals\n* Running Spark in the Cloud\n\n\n## Extra:\n\n### [Module 2: Workflow Orchestration with Airflow](2_Workflow-Orchestration-AirFlow/)\n\n* Data Lake vs Data Warehouse\n* ETL vs ELT\n* Introduction to Workflow Orchestration\n* Airflow architecture\n* Setting up Airflow with Docker\n* Ingesting data to local Postgres with Airflow\n* Ingesting data to GCP with Airflow\n* Airflow with kubernetes\n\n\n\n### Environment setup \n\nYou can set it up on your laptop or PC if you prefer to work locally or you can set up a virtual machine in Google Cloud Platform.\n\nIn this repo we will use windows + WSL2 locally\n\nFor the course you'll need:\n\n* Python 3 \n* Google Cloud SDK (explained in module 1)\n* Docker with docker-compose\n* Terraform (explained in module 1)\n* Git account\n* Google Cloud Platform account","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanuelguerra1987%2Fdata-engineering-zoomcamp-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmanuelguerra1987%2Fdata-engineering-zoomcamp-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanuelguerra1987%2Fdata-engineering-zoomcamp-notes/lists"}