Merge branch 'main' of https://github.com/DataTalksClub/data-engineering-zoomcamp into de-zoomcamp-videos

* Update URL in homework 3

URL was incorrect leading to errors in downloading

* Update homework.md

01-docker-terraform/README.md

@@ -0,0 +1,210 @@
+# Introduction
+
+* [![](https://markdown-videos-api.jorgenkh.no/youtube/AtRhA-NfS24)](https://www.youtube.com/watch?v=AtRhA-NfS24&list=PL3MmuxUbc_hKihpnNQ9qtTmWYy26bPrSb&index=3)
+* [Slides](https://www.slideshare.net/AlexeyGrigorev/data-engineering-zoomcamp-introduction)
+* Overview of [Architecture](https://github.com/DataTalksClub/data-engineering-zoomcamp#overview), [Technologies](https://github.com/DataTalksClub/data-engineering-zoomcamp#technologies) & [Pre-Requisites](https://github.com/DataTalksClub/data-engineering-zoomcamp#prerequisites)
+
+
+We suggest watching videos in the same order as in this document.
+
+The last video (setting up the environment) is optional, but you can check it earlier 
+if you have troubles setting up the environment and following along with the videos.
+
+
+# Docker + Postgres
+
+[Code](2_docker_sql)
+
+## :movie_camera: Introduction to Docker
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/EYNwNlOrpr0)](https://youtu.be/EYNwNlOrpr0&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=4)
+
+* Why do we need Docker
+* Creating a simple "data pipeline" in Docker
+
+
+## :movie_camera: Ingesting NY Taxi Data to Postgres
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/2JM-ziJt0WI)](https://youtu.be/2JM-ziJt0WI&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=5)
+
+* Running Postgres locally with Docker
+* Using `pgcli` for connecting to the database
+* Exploring the NY Taxi dataset
+* Ingesting the data into the database
+
+> [!TIP]
+>if you have problems with `pgcli`, check this video for an alternative way to connect to your database in jupyter notebook and pandas.
+>
+> [![](https://markdown-videos-api.jorgenkh.no/youtube/3IkfkTwqHx4)](https://youtu.be/3IkfkTwqHx4&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=6)
+
+
+## :movie_camera: Connecting pgAdmin and Postgres
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/hCAIVe9N0ow)](https://youtu.be/hCAIVe9N0ow&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=7)
+
+* The pgAdmin tool
+* Docker networks
+
+
+> [!IMPORTANT]
+>The UI for PgAdmin 4 has changed, please follow the below steps for creating a server:
+>
+>* After login to PgAdmin, right click Servers in the left sidebar.
+>* Click on Register.
+>* Click on Server.
+>* The remaining steps to create a server are the same as in the videos.
+
+
+## :movie_camera: Putting the ingestion script into Docker
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/B1WwATwf-vY)](https://youtu.be/B1WwATwf-vY&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=8)
+
+* Converting the Jupyter notebook to a Python script
+* Parametrizing the script with argparse
+* Dockerizing the ingestion script
+
+## :movie_camera: Running Postgres and pgAdmin with Docker-Compose
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/hKI6PkPhpa0)](https://youtu.be/hKI6PkPhpa0&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=9)
+
+* Why do we need Docker-compose
+* Docker-compose YAML file
+* Running multiple containers with `docker-compose up`
+
+## :movie_camera: SQL refresher
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/QEcps_iskgg)](https://youtu.be/QEcps_iskgg&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=10)
+
+* Adding the Zones table
+* Inner joins
+* Basic data quality checks
+* Left, Right and Outer joins
+* Group by
+
+## :movie_camera: Optional: Docker Networking and Port Mapping
+
+> [!TIP]
+> Optional: If you have some problems with docker networking, check **Port Mapping and Networks in Docker video**.
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/tOr4hTsHOzU)](https://youtu.be/tOr4hTsHOzU&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=5)
+
+* Docker networks
+* Port forwarding to the host environment
+* Communicating between containers in the network
+* `.dockerignore` file
+
+## :movie_camera: Optional: Walk-Through on WSL
+
+> [!TIP]
+> Optional: If you are willing to do the steps from "Ingesting NY Taxi Data to Postgres" till "Running Postgres and pgAdmin with Docker-Compose" with Windows Subsystem Linux please check **Docker Module Walk-Through on WSL**.
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/Mv4zFm2AwzQ)](https://youtu.be/Mv4zFm2AwzQ&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=33)
+
+
+# GCP
+
+## :movie_camera: Introduction to GCP (Google Cloud Platform)
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/18jIzE41fJ4)](https://youtu.be/18jIzE41fJ4&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=3)
+
+# Terraform
+
+[Code](1_terraform_gcp)
+
+## :movie_camera: Introduction Terraform: Concepts and Overview, a primer
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/s2bOYDCKl_M)](https://youtu.be/s2bOYDCKl_M&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=11)
+
+* [Companion Notes](1_terraform_gcp)
+
+## :movie_camera: Terraform Basics: Simple one file Terraform Deployment
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/Y2ux7gq3Z0o)](https://youtu.be/Y2ux7gq3Z0o&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=12)
+
+* [Companion Notes](1_terraform_gcp)
+
+## :movie_camera: Deployment with a Variables File
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/PBi0hHjLftk)](https://youtu.be/PBi0hHjLftk&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=13)
+
+* [Companion Notes](1_terraform_gcp)    
+
+## Configuring terraform and GCP SDK on Windows
+
+* [Instructions](1_terraform_gcp/windows.md)
+
+
+# Environment setup 
+
+For the course you'll need:
+
+* Python 3 (e.g. installed with Anaconda)
+* Google Cloud SDK
+* Docker with docker-compose
+* Terraform
+* Git account
+
+> [!NOTE]  
+>If you have problems setting up the environment, you can check these videos.
+>
+>If you already have a working coding environment on local machine, these are optional. And only need to select one method. But if you have time to learn it now, these would be helpful if the local environment suddenly do not work one day.
+
+## :movie_camera: GCP Cloud VM 
+
+### Setting up the environment on cloud VM
+[![](https://markdown-videos-api.jorgenkh.no/youtube/ae-CV2KfoN0)](https://youtu.be/ae-CV2KfoN0&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=14)
+
+* Generating SSH keys
+* Creating a virtual machine on GCP
+* Connecting to the VM with SSH
+* Installing Anaconda
+* Installing Docker
+* Creating SSH `config` file
+* Accessing the remote machine with VS Code and SSH remote
+* Installing docker-compose
+* Installing pgcli
+* Port-forwarding with VS code: connecting to pgAdmin and Jupyter from the local computer
+* Installing Terraform
+* Using `sftp` for putting the credentials to the remote machine
+* Shutting down and removing the instance
+
+## :movie_camera: GitHub Codespaces
+
+### Preparing the environment with GitHub Codespaces
+
+[![](https://markdown-videos-api.jorgenkh.no/youtube/XOSUt8Ih3zA)](https://youtu.be/XOSUt8Ih3zA&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=15)
+
+# Homework
+
+* [Homework](../cohorts/2024/01-docker-terraform/homework.md)
+
+
+# Community notes
+
+Did you take notes? You can share them here
+
+* [Notes from Alvaro Navas](https://github.com/ziritrion/dataeng-zoomcamp/blob/main/notes/1_intro.md)
+* [Notes from Abd](https://itnadigital.notion.site/Week-1-Introduction-f18de7e69eb4453594175d0b1334b2f4)
+* [Notes from Aaron](https://github.com/ABZ-Aaron/DataEngineerZoomCamp/blob/master/week_1_basics_n_setup/README.md)
+* [Notes from Faisal](https://github.com/FaisalMohd/data-engineering-zoomcamp/blob/main/week_1_basics_n_setup/Notes/DE%20Zoomcamp%20Week-1.pdf)
+* [Michael Harty's Notes](https://github.com/mharty3/data_engineering_zoomcamp_2022/tree/main/week01)
+* [Blog post from Isaac Kargar](https://kargarisaac.github.io/blog/data%20engineering/jupyter/2022/01/18/data-engineering-w1.html)
+* [Handwritten Notes By Mahmoud Zaher](https://github.com/zaherweb/DataEngineering/blob/master/week%201.pdf)
+* [Notes from Candace Williams](https://teacherc.github.io/data-engineering/2023/01/18/zoomcamp1.html)
+* [Notes from Marcos Torregrosa](https://www.n4gash.com/2023/data-engineering-zoomcamp-semana-1/)
+* [Notes from Vincenzo Galante](https://binchentso.notion.site/Data-Talks-Club-Data-Engineering-Zoomcamp-8699af8e7ff94ec49e6f9bdec8eb69fd)
+* [Notes from Victor Padilha](https://github.com/padilha/de-zoomcamp/tree/master/week1)
+* [Notes from froukje](https://github.com/froukje/de-zoomcamp/blob/main/week_1_basics_n_setup/notes/notes_week_01.md)
+* [Notes from adamiaonr](https://github.com/adamiaonr/data-engineering-zoomcamp/blob/main/week_1_basics_n_setup/2_docker_sql/NOTES.md)
+* [Notes from Xia He-Bleinagel](https://xiahe-bleinagel.com/2023/01/week-1-data-engineering-zoomcamp-notes/)
+* [Notes from Balaji](https://github.com/Balajirvp/DE-Zoomcamp/blob/main/Week%201/Detailed%20Week%201%20Notes.ipynb)
+* [Notes from Erik](https://twitter.com/ehub96/status/1621351266281730049)
+* [Notes by Alain Boisvert](https://github.com/boisalai/de-zoomcamp-2023/blob/main/week1.md)
+* Notes on [Docker, Docker Compose, and setting up a proper Python environment](https://medium.com/@verazabeida/zoomcamp-2023-week-1-f4f94cb360ae), by Vera
+* [Setting up the development environment on Google Virtual Machine](https://itsadityagupta.hashnode.dev/setting-up-the-development-environment-on-google-virtual-machine), blog post by Aditya Gupta
+* [Notes from Zharko Cekovski](https://www.zharconsulting.com/contents/data/data-engineering-bootcamp-2024/week-1-postgres-docker-and-ingestion-scripts/)
+* [2024 Module-01 Walkthough video by ellacharmed on youtube](https://youtu.be/VUZshlVAnk4)
+* [2024 Companion Module Walkthough slides by ellacharmed](https://github.com/ellacharmed/data-engineering-zoomcamp/blob/ella2024/cohorts/2024/01-docker-terraform/walkthrough-01.pdf)
+* [2024 Module-01 Environment setup video by ellacharmed on youtube](https://youtu.be/Zce_Hd37NGs)
+* [Docker Notes from Linda](https://github.com/inner-outer-space/de-zoomcamp-2024/blob/main/1-basics-n-setup/docker_sql/readme.md) • [Terraform Notes from Linda](https://github.com/inner-outer-space/de-zoomcamp-2024/blob/main/1-basics-n-setup/terraform_gcp/readme.md)
+* Add your notes above this line

02-workflow-orchestration/README.md

@@ -0,0 +1,154 @@
+> If you're looking for Airflow videos from the 2022 edition,
+> check the [2022 cohort folder](../cohorts/2022/week_2_data_ingestion/). <br>
+> If you're looking for Prefect videos from the 2023 edition,
+> check the [2023 cohort folder](../cohorts/2023/week_2_data_ingestion/).
+
+# Week 2: Workflow Orchestration
+
+Welcome to Week 2 of the Data Engineering Zoomcamp! 🚀😤 This week, we'll be covering workflow orchestration with Mage.
+
+Mage is an open-source, hybrid framework for transforming and integrating data. ✨
+
+This week, you'll learn how to use the Mage platform to author and share _magical_ data pipelines. This will all be covered in the course, but if you'd like to learn a bit more about Mage, check out our docs [here](https://docs.mage.ai/introduction/overview). 
+
+* [2.2.1 - 📯 Intro to Orchestration](#221----intro-to-orchestration)
+* [2.2.2 - 🧙‍♂️ Intro to Mage](#222---%EF%B8%8F-intro-to-mage)
+* [2.2.3 - 🐘 ETL: API to Postgres](#223----etl-api-to-postgres)
+* [2.2.4 - 🤓 ETL: API to GCS](#224----etl-api-to-gcs)
+* [2.2.5 - 🔍 ETL: GCS to BigQuery](#225----etl-gcs-to-bigquery)
+* [2.2.6 - 👨‍💻 Parameterized Execution](#226----parameterized-execution)
+* [2.2.7 - 🤖 Deployment (Optional)](#227----deployment-optional)
+* [2.2.8 - 🧱 Advanced Blocks (Optional)](#228----advanced-blocks-optional)
+* [2.2.9 - 🗒️ Homework](#229---%EF%B8%8F-homework)
+* [2.2.10 - 👣 Next Steps](#2210----next-steps)
+
+## 📕 Course Resources
+
+### 2.2.1 - 📯 Intro to Orchestration
+
+In this section, we'll cover the basics of workflow orchestration. We'll discuss what it is, why it's important, and how it can be used to build data pipelines.
+
+Videos
+- 2.2.1a - [What is Orchestration?](https://www.youtube.com/watch?v=Li8-MWHhTbo&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+
+Resources
+- [Slides](https://docs.google.com/presentation/d/17zSxG5Z-tidmgY-9l7Al1cPmz4Slh4VPK6o2sryFYvw/)
+
+### 2.2.2 - 🧙‍♂️ Intro to Mage
+
+In this section, we'll introduce the Mage platform. We'll cover what makes Mage different from other orchestrators, the fundamental concepts behind Mage, and how to get started. To cap it off, we'll spin Mage up via Docker 🐳 and run a simple pipeline.
+
+Videos
+- 2.2.2a - [What is Mage?](https://www.youtube.com/watch?v=AicKRcK3pa4&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+- 
+- 2.2.2b - [Configuring Mage](https://www.youtube.com/watch?v=tNiV7Wp08XE?list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+- 2.2.2c - [A Simple Pipeline](https://www.youtube.com/watch?v=stI-gg4QBnI&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+
+Resources
+- [Getting Started Repo](https://github.com/mage-ai/mage-zoomcamp)
+- [Slides](https://docs.google.com/presentation/d/1y_5p3sxr6Xh1RqE6N8o2280gUzAdiic2hPhYUUD6l88/)
+
+### 2.2.3 - 🐘 ETL: API to Postgres
+
+Hooray! Mage is up and running. Now, let's build a _real_ pipeline. In this section, we'll build a simple ETL pipeline that loads data from an API into a Postgres database. Our database will be built using Docker— it will be running locally, but it's the same as if it were running in the cloud.
+
+Videos
+- 2.2.3a - [Configuring Postgres](https://www.youtube.com/watch?v=pmhI-ezd3BE&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+- 2.2.3b - [Writing an ETL Pipeline](https://www.youtube.com/watch?v=Maidfe7oKLs&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+
+Resources
+- [Taxi Dataset](https://github.com/DataTalksClub/nyc-tlc-data/releases/download/yellow/yellow_tripdata_2021-01.csv.gz)
+- [Sample loading block](https://github.com/mage-ai/mage-zoomcamp/blob/solutions/magic-zoomcamp/data_loaders/load_nyc_taxi_data.py)
+
+
+### 2.2.4 - 🤓 ETL: API to GCS
+
+Ok, so we've written data _locally_ to a database, but what about the cloud? In this tutorial, we'll walk through the process of using Mage to extract, transform, and load data from an API to Google Cloud Storage (GCS). 
+
+We'll cover both writing _partitioned_ and _unpartitioned_ data to GCS and discuss _why_ you might want to do one over the other. Many data teams start with extracting data from a source and writing it to a data lake _before_ loading it to a structured data source, like a database.
+
+Videos
+- 2.2.4a - [Configuring GCP](https://www.youtube.com/watch?v=00LP360iYvE&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+- 2.2.4b - [Writing an ETL Pipeline](https://www.youtube.com/watch?v=w0XmcASRUnc&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+
+Resources
+- [DTC Zoomcamp GCP Setup](../week_1_basics_n_setup/1_terraform_gcp/2_gcp_overview.md)
+
+### 2.2.5 - 🔍 ETL: GCS to BigQuery
+
+Now that we've written data to GCS, let's load it into BigQuery. In this section, we'll walk through the process of using Mage to load our data from GCS to BigQuery. This closely mirrors a very common data engineering workflow: loading data from a data lake into a data warehouse.
+
+Videos
+- 2.2.5a - [Writing an ETL Pipeline](https://www.youtube.com/watch?v=JKp_uzM-XsM)
+
+### 2.2.6 - 👨‍💻 Parameterized Execution
+
+By now you're familiar with building pipelines, but what about adding parameters? In this video, we'll discuss some built-in runtime variables that exist in Mage and show you how to define your own! We'll also cover how to use these variables to parameterize your pipelines. Finally, we'll talk about what it means to *backfill* a pipeline and how to do it in Mage.
+
+Videos
+- 2.2.6a - [Parameterized Execution](https://www.youtube.com/watch?v=H0hWjWxB-rg&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+- 2.2.6b - [Backfills](https://www.youtube.com/watch?v=ZoeC6Ag5gQc&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+
+Resources
+- [Mage Variables Overview](https://docs.mage.ai/development/variables/overview)
+- [Mage Runtime Variables](https://docs.mage.ai/getting-started/runtime-variable)
+
+### 2.2.7 - 🤖 Deployment (Optional)
+
+In this section, we'll cover deploying Mage using Terraform and Google Cloud. This section is optional— it's not *necessary* to learn Mage, but it might be helpful if you're interested in creating a fully deployed project. If you're using Mage in your final project, you'll need to deploy it to the cloud.
+
+Videos
+- 2.2.7a - [Deployment Prerequisites](https://www.youtube.com/watch?v=zAwAX5sxqsg&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+- 2.2.7b - [Google Cloud Permissions](https://www.youtube.com/watch?v=O_H7DCmq2rA&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+- 2.2.7c - [Deploying to Google Cloud - Part 1](https://www.youtube.com/watch?v=9A872B5hb_0&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+- 2.2.7d - [Deploying to Google Cloud - Part 2](https://www.youtube.com/watch?v=0YExsb2HgLI&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+
+Resources
+- [Installing Terraform](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli)
+- [Installing `gcloud` CLI](https://cloud.google.com/sdk/docs/install)
+- [Mage Terraform Templates](https://github.com/mage-ai/mage-ai-terraform-templates)
+
+Additional Mage Guides
+- [Terraform](https://docs.mage.ai/production/deploying-to-cloud/using-terraform)
+- [Deploying to GCP with Terraform](https://docs.mage.ai/production/deploying-to-cloud/gcp/setup)
+
+### 2.2.8 - 🗒️ Homework 
+
+We've prepared a short exercise to test you on what you've learned this week. You can find the homework [here](../cohorts/2024/02-workflow-orchestration/homework.md). This follows closely from the contents of the course and shouldn't take more than an hour or two to complete. 😄
+
+### 2.2.9 - 👣 Next Steps
+
+Congratulations! You've completed Week 2 of the Data Engineering Zoomcamp. We hope you've enjoyed learning about Mage and that you're excited to use it in your final project. If you have any questions, feel free to reach out to us on Slack. Be sure to check out our "Next Steps" video for some inspiration for the rest of your journey 😄.
+
+Videos
+- 2.2.9a - [Next Steps](https://www.youtube.com/watch?v=uUtj7N0TleQ)
+
+Resources
+- [Slides](https://docs.google.com/presentation/d/1yN-e22VNwezmPfKrZkgXQVrX5owDb285I2HxHWgmAEQ/edit#slide=id.g262fb0d2905_0_12)
+
+### 📑 Additional Resources
+
+- [Mage Docs](https://docs.mage.ai/)
+- [Mage Guides](https://docs.mage.ai/guides)
+- [Mage Slack](https://www.mage.ai/chat)
+
+
+# Community notes
+
+Did you take notes? You can share them here:
+
+## 2024 notes
+
+* [2024 Videos transcripts week 2](https://drive.google.com/drive/folders/1yxT0uMMYKa6YOxanh91wGqmQUMS7yYW7?usp=sharing) by Maria Fisher
+* [Notes from Jonah Oliver](https://www.jonahboliver.com/blog/de-zc-w2)
+* [Notes from Linda](https://github.com/inner-outer-space/de-zoomcamp-2024/blob/main/2-workflow-orchestration/readme.md)
+* Add your notes above this line
+
+## 2023 notes
+
+See [here](../cohorts/2023/week_2_workflow_orchestration#community-notes)
+
+
+## 2022 notes
+
+See [here](../cohorts/2022/week_2_data_ingestion#community-notes)

03-data-warehouse/README.md

@@ -1,51 +1,54 @@
-## Data Warehouse and BigQuery
+# Data Warehouse and BigQuery
 
 - [Slides](https://docs.google.com/presentation/d/1a3ZoBAXFk8-EhUsd7rAZd-5p_HpltkzSeujjRGB2TAI/edit?usp=sharing)  
 - [Big Query basic SQL](big_query.sql)
 
+# Videos
 
-### Data Warehouse
+## Data Warehouse
 
-- [Data Warehouse and BigQuery](https://youtu.be/jrHljAoD6nM)
+- [Data Warehouse and BigQuery](https://www.youtube.com/watch?v=jrHljAoD6nM&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
-### Partitoning and clustering
+## :movie_camera: Partitoning and clustering
 
-- [Partioning and Clustering](https://youtu.be/jrHljAoD6nM?t=726)  
-- [Partioning vs Clustering](https://youtu.be/-CqXf7vhhDs)  
+- [Partioning and Clustering](https://www.youtube.com/watch?v=jrHljAoD6nM&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)  
+- [Partioning vs Clustering](https://www.youtube.com/watch?v=-CqXf7vhhDs&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
-### Best practices
+## :movie_camera: Best practices
 
-- [BigQuery Best Practices](https://youtu.be/k81mLJVX08w)  
+- [BigQuery Best Practices](https://www.youtube.com/watch?v=k81mLJVX08w&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
-### Internals of BigQuery
+## :movie_camera: Internals of BigQuery
 
-- [Internals of Big Query](https://youtu.be/eduHi1inM4s)  
+- [Internals of Big Query](https://www.youtube.com/watch?v=eduHi1inM4s&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
-### Advanced
+## Advanced topics
 
-#### ML
-[BigQuery Machine Learning](https://youtu.be/B-WtpB0PuG4)  
-[SQL for ML in BigQuery](big_query_ml.sql)
+### :movie_camera: Machine Learning in Big Query
+
+* [BigQuery Machine Learning](https://www.youtube.com/watch?v=B-WtpB0PuG4&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
+* [SQL for ML in BigQuery](big_query_ml.sql)
 
 **Important links**
+
 - [BigQuery ML Tutorials](https://cloud.google.com/bigquery-ml/docs/tutorials)
 - [BigQuery ML Reference Parameter](https://cloud.google.com/bigquery-ml/docs/analytics-reference-patterns)
 - [Hyper Parameter tuning](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-glm)
 - [Feature preprocessing](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-preprocess-overview)
 
-##### Deploying ML model
+### :movie_camera: Deploying ML model
 
-- [BigQuery Machine Learning Deployment](https://youtu.be/BjARzEWaznU)  
+- [BigQuery Machine Learning Deployment](https://www.youtube.com/watch?v=BjARzEWaznU&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 - [Steps to extract and deploy model with docker](extract_model.md)  
 
 
 
-### Homework
+# Homework
 
-* [Homework](../cohorts/2023/week_3_data_warehouse/homework.md)
+* [2024 Homework](../cohorts/2024/03-data-warehouse/homework.md)
 
 
-## Community notes
+# Community notes
 
 Did you take notes? You can share them here.
 
@@ -58,4 +61,6 @@ Did you take notes? You can share them here.
 * [Notes by froukje](https://github.com/froukje/de-zoomcamp/blob/main/week_3_data_warehouse/notes/notes_week_03.md)
 * [Notes by Alain Boisvert](https://github.com/boisalai/de-zoomcamp-2023/blob/main/week3.md)
 * [Notes from Vincenzo Galante](https://binchentso.notion.site/Data-Talks-Club-Data-Engineering-Zoomcamp-8699af8e7ff94ec49e6f9bdec8eb69fd)
+* [2024 videos transcript week3](https://drive.google.com/drive/folders/1quIiwWO-tJCruqvtlqe_Olw8nvYSmmDJ?usp=sharing) by Maria Fisher 
+* [Notes by Linda](https://github.com/inner-outer-space/de-zoomcamp-2024/blob/main/3-data-warehouse/readme.md)
 * Add your notes here (above this line)

04-analytics-engineering/README.md

@@ -0,0 +1,141 @@
+# Week 4: Analytics Engineering 
+Goal: Transforming the data loaded in DWH into Analytical Views developing a [dbt project](taxi_rides_ny/README.md).
+
+### Prerequisites
+By this stage of the course you should have already: 
+
+- A running warehouse (BigQuery or postgres) 
+- A set of running pipelines ingesting the project dataset (week 3 completed)
+- The following datasets ingested from the course [Datasets list](https://github.com/DataTalksClub/nyc-tlc-data/): 
+  * Yellow taxi data - Years 2019 and 2020
+  * Green taxi data - Years 2019 and 2020 
+  * fhv data - Year 2019. 
+
+Note:
+* A quick hack has been shared to load that data quicker, check instructions in [week3/extras](../03-data-warehouse/extras)
+* If you recieve an error stating "Permission denied while globbing file pattern." when attemting to run fact_trips.sql this [Video](https://www.youtube.com/watch?v=kL3ZVNL9Y4A&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb) may be helpful in resolving the issue
+
+## Setting up your environment 
+  
+
+### Setting up dbt for using BigQuery (Alternative A - preferred)
+
+1. Open a free developer dbt cloud account following[this link](https://www.getdbt.com/signup/)
+2. [Following these instructions to connect to your BigQuery instance]([https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-setting-up-bigquery-oauth](https://docs.getdbt.com/guides/bigquery?step=4)). More detailed instructions in [dbt_cloud_setup.md](dbt_cloud_setup.md)
+
+_Optional_: If you feel more comfortable developing locally you could use a local installation of dbt core. You can follow the [official dbt documentation]([https://docs.getdbt.com/dbt-cli/installation](https://docs.getdbt.com/docs/core/installation-overview)) or follow the [dbt core with BigQuery on Docker](docker_setup/README.md) guide to setup dbt locally on docker. You will need to install the latest version with the BigQuery adapter (dbt-bigquery). 
+
+### Setting up dbt for using Postgres locally (Alternative B)
+
+As an alternative to the cloud, that require to have a cloud database, you will be able to run the project installing dbt locally.
+You can follow the [official dbt documentation]([https://docs.getdbt.com/dbt-cli/installation](https://docs.getdbt.com/dbt-cli/installation)) or use a docker image from oficial [dbt repo](https://github.com/dbt-labs/dbt/). You will need to install the latest version with the postgres adapter (dbt-postgres).
+After local installation you will have to set up the connection to PG in the `profiles.yml`, you can find the templates [here](https://docs.getdbt.com/docs/core/connect-data-platform/postgres-setup)
+
+</details>
+
+## Content
+
+### Introduction to analytics engineering
+
+* What is analytics engineering?
+* ETL vs ELT 
+* Data modeling concepts (fact and dim tables)
+
+ :movie_camera: [Video](https://www.youtube.com/watch?v=uF76d5EmdtU&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=32)
+
+### What is dbt? 
+
+* Intro to dbt 
+
+ :movie_camera: [Video](https://www.youtube.com/watch?v=4eCouvVOJUw&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=33)
+
+## Starting a dbt project
+
+### Alternative A: Using BigQuery + dbt cloud
+* Starting a new project with dbt init (dbt cloud and core)
+* dbt cloud setup
+* project.yml
+
+ :movie_camera: [Video](https://www.youtube.com/watch?v=iMxh6s_wL4Q&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=34)
+ 
+### Alternative B: Using Postgres + dbt core (locally)
+* Starting a new project with dbt init (dbt cloud and core)
+* dbt core local setup
+* profiles.yml
+* project.yml
+
+:movie_camera: [Video](https://www.youtube.com/watch?v=1HmL63e-vRs&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=35)
+
+### dbt models
+
+* Anatomy of a dbt model: written code vs compiled Sources
+* Materialisations: table, view, incremental, ephemeral  
+* Seeds, sources and ref  
+* Jinja and Macros 
+* Packages 
+* Variables
+
+:movie_camera: [Video](https://www.youtube.com/watch?v=UVI30Vxzd6c&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=36)
+
+_Note: This video is shown entirely on dbt cloud IDE but the same steps can be followed locally on the IDE of your choice_
+
+### Testing and documenting dbt models
+* Tests  
+* Documentation 
+
+:movie_camera: [Video](https://www.youtube.com/watch?v=UishFmq1hLM&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=37)
+
+_Note: This video is shown entirely on dbt cloud IDE but the same steps can be followed locally on the IDE of your choice_
+
+## Deployment
+
+### Alternative A: Using BigQuery + dbt cloud
+* Deployment: development environment vs production 
+* dbt cloud: scheduler, sources and hosted documentation
+
+:movie_camera: [Video](https://www.youtube.com/watch?v=rjf6yZNGX8I&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=38)
+
+### Alternative B: Using Postgres + dbt core (locally)
+* Deployment: development environment vs production 
+* dbt cloud: scheduler, sources and hosted documentation
+
+:movie_camera: [Video](https://www.youtube.com/watch?v=Cs9Od1pcrzM&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=39)
+
+## Visualising the transformed data
+:movie_camera: [Google data studio Video](https://www.youtube.com/watch?v=39nLTs74A3E&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=42) 
+:movie_camera: [Metabase Video](https://www.youtube.com/watch?v=BnLkrA7a6gM&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb&index=43) 
+
+ 
+## Advanced concepts
+
+ * [Make a model Incremental](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models)
+ * [Use of tags](https://docs.getdbt.com/reference/resource-configs/tags)
+ * [Hooks](https://docs.getdbt.com/docs/building-a-dbt-project/hooks-operations)
+ * [Analysis](https://docs.getdbt.com/docs/building-a-dbt-project/analyses)
+ * [Snapshots](https://docs.getdbt.com/docs/building-a-dbt-project/snapshots)
+ * [Exposure](https://docs.getdbt.com/docs/building-a-dbt-project/exposures)
+ * [Metrics](https://docs.getdbt.com/docs/building-a-dbt-project/metrics)
+
+
+## Community notes
+
+Did you take notes? You can share them here.
+
+* [Notes by Alvaro Navas](https://github.com/ziritrion/dataeng-zoomcamp/blob/main/notes/4_analytics.md)
+* [Sandy's DE learning blog](https://learningdataengineering540969211.wordpress.com/2022/02/17/week-4-setting-up-dbt-cloud-with-bigquery/)
+* [Notes by Victor Padilha](https://github.com/padilha/de-zoomcamp/tree/master/week4)
+* [Marcos Torregrosa's blog (spanish)](https://www.n4gash.com/2023/data-engineering-zoomcamp-semana-4/)
+* [Notes by froukje](https://github.com/froukje/de-zoomcamp/blob/main/week_4_analytics_engineering/notes/notes_week_04.md)
+* [Notes by Alain Boisvert](https://github.com/boisalai/de-zoomcamp-2023/blob/main/week4.md)
+* [Setting up Prefect with dbt by Vera](https://medium.com/@verazabeida/zoomcamp-week-5-5b6a9d53a3a0)
+* [Blog by Xia He-Bleinagel](https://xiahe-bleinagel.com/2023/02/week-4-data-engineering-zoomcamp-notes-analytics-engineering-and-dbt/)
+* [Setting up DBT with BigQuery by Tofag](https://medium.com/@fagbuyit/setting-up-your-dbt-cloud-dej-9-d18e5b7c96ba)
+* [Blog post by Dewi Oktaviani](https://medium.com/@oktavianidewi/de-zoomcamp-2023-learning-week-4-analytics-engineering-with-dbt-53f781803d3e)
+* [Notes from Vincenzo Galante](https://binchentso.notion.site/Data-Talks-Club-Data-Engineering-Zoomcamp-8699af8e7ff94ec49e6f9bdec8eb69fd)
+* [Notes from Balaji](https://github.com/Balajirvp/DE-Zoomcamp/blob/main/Week%204/Data%20Engineering%20Zoomcamp%20Week%204.ipynb)
+ *Add your notes here (above this line)*
+
+## Useful links
+- [Slides used in the videos](https://docs.google.com/presentation/d/1xSll_jv0T8JF4rYZvLHfkJXYqUjPtThA/edit?usp=sharing&ouid=114544032874539580154&rtpof=true&sd=true)
+- [Visualizing data with Metabase course](https://www.metabase.com/learn/visualization/)
+- [dbt free courses](https://courses.getdbt.com/collections)

04-analytics-engineering/taxi_rides_ny/.gitignore

@@ -0,0 +1,5 @@
+# you shouldn't commit these into source control
+# these are the default directory names, adjust/add to fit your needs
+target/
+dbt_packages/
+logs/

04-analytics-engineering/taxi_rides_ny/README.md

@@ -35,4 +35,4 @@ _Alternative: use `$ dbt build` to execute with one command the 3 steps above to
 - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers
 - Join the [chat](http://slack.getdbt.com/) on Slack for live discussions and support
 - Find [dbt events](https://events.getdbt.com) near you
-- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices
+- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices

04-analytics-engineering/taxi_rides_ny/dbt_project.yml

@@ -7,13 +7,13 @@ version: '1.0.0'
 config-version: 2
 
 # This setting configures which "profile" dbt uses for this project.
-profile: 'pg-dbt-workshop'
+profile: 'default'
 
 # These configurations specify where dbt should look for different types of files.
-# The `source-paths` config, for example, states that models in this project can be
+# The `model-paths` config, for example, states that models in this project can be
 # found in the "models/" directory. You probably won't need to change these!
 model-paths: ["models"]
-analysis-paths: ["analysis"]
+analysis-paths: ["analyses"]
 test-paths: ["tests"]
 seed-paths: ["seeds"]
 macro-paths: ["macros"]
@@ -21,17 +21,20 @@ snapshot-paths: ["snapshots"]
 
 target-path: "target"  # directory which will store compiled SQL files
 clean-targets:         # directories to be removed by `dbt clean`
-    - "target"
-    - "dbt_packages"
-    - "dbt_modules"
+  - "target"
+  - "dbt_packages"
 
 
 # Configuring models
 # Full documentation: https://docs.getdbt.com/docs/configuring-models
 
-# In this example config, we tell dbt to build all models in the example/ directory
-# as tables. These settings can be overridden in the individual model files
+# In dbt, the default materialization for a model is a view. This means, when you run 
+# dbt run or dbt build, all of your models will be built as a view in your data platform. 
+# The configuration below will override this setting for models in the example folder to 
+# instead be materialized as tables. Any models you add to the root of the models folder will 
+# continue to be built as views. These settings can be overridden in the individual model files
 # using the `{{ config(...) }}` macro.
+
 models:
   taxi_rides_ny:
       # Applies to all files under models/.../
@@ -46,4 +49,4 @@ seeds:
     taxi_rides_ny:
         taxi_zone_lookup:
             +column_types:
-                locationid: numeric
+                locationid: numeric

04-analytics-engineering/taxi_rides_ny/macros/get_payment_type_description.sql

@@ -1,18 +1,17 @@
- {#
+{#
     This macro returns the description of the payment_type 
 #}
 
 {% macro get_payment_type_description(payment_type) -%}
 
-    case {{ payment_type }}
+    case {{ dbt.safe_cast("payment_type", api.Column.translate_type("integer")) }}  
         when 1 then 'Credit card'
         when 2 then 'Cash'
         when 3 then 'No charge'
         when 4 then 'Dispute'
         when 5 then 'Unknown'
         when 6 then 'Voided trip'
+        else 'EMPTY'
     end
 
-{%- endmacro %}
-
-              
+{%- endmacro %}

04-analytics-engineering/taxi_rides_ny/models/core/dim_zones.sql

@@ -1,9 +1,8 @@
 {{ config(materialized='table') }}
 
-
 select 
     locationid, 
     borough, 
     zone, 
-    replace(service_zone,'Boro','Green') as service_zone
+    replace(service_zone,'Boro','Green') as service_zone 
 from {{ ref('taxi_zone_lookup') }}

04-analytics-engineering/taxi_rides_ny/models/core/dm_monthly_zone_revenue.sql

@@ -6,8 +6,7 @@ with trips_data as (
     select 
     -- Reveneue grouping 
     pickup_zone as revenue_zone,
-    date_trunc('month', pickup_datetime) as revenue_month, 
-    --Note: For BQ use instead: date_trunc(pickup_datetime, month) as revenue_month, 
+    {{ dbt.date_trunc("month", "pickup_datetime") }} as revenue_month, 
 
     service_type, 
 
@@ -20,7 +19,6 @@ with trips_data as (
     sum(ehail_fee) as revenue_monthly_ehail_fee,
     sum(improvement_surcharge) as revenue_monthly_improvement_surcharge,
     sum(total_amount) as revenue_monthly_total_amount,
-    sum(congestion_surcharge) as revenue_monthly_congestion_surcharge,
 
     -- Additional calculations
     count(tripid) as total_monthly_trips,
@@ -28,4 +26,4 @@ with trips_data as (
     avg(trip_distance) as avg_montly_trip_distance
 
     from trips_data
-    group by 1,2,3
+    group by 1,2,3

04-analytics-engineering/taxi_rides_ny/models/core/fact_trips.sql

@@ -1,29 +1,29 @@
-{{ config(materialized='table') }}
+{{
+    config(
+        materialized='table'
+    )
+}}
 
-with green_data as (
+with green_tripdata as (
     select *, 
-        'Green' as service_type 
+        'Green' as service_type
     from {{ ref('stg_green_tripdata') }}
 ), 
-
-yellow_data as (
+yellow_tripdata as (
     select *, 
         'Yellow' as service_type
     from {{ ref('stg_yellow_tripdata') }}
 ), 
-
 trips_unioned as (
-    select * from green_data
-    union all
-    select * from yellow_data
+    select * from green_tripdata
+    union all 
+    select * from yellow_tripdata
 ), 
-
 dim_zones as (
     select * from {{ ref('dim_zones') }}
     where borough != 'Unknown'
 )
-select 
-    trips_unioned.tripid, 
+select trips_unioned.tripid, 
     trips_unioned.vendorid, 
     trips_unioned.service_type,
     trips_unioned.ratecodeid, 
@@ -48,10 +48,9 @@ select
     trips_unioned.improvement_surcharge, 
     trips_unioned.total_amount, 
     trips_unioned.payment_type, 
-    trips_unioned.payment_type_description, 
-    trips_unioned.congestion_surcharge
+    trips_unioned.payment_type_description
 from trips_unioned
 inner join dim_zones as pickup_zone
 on trips_unioned.pickup_locationid = pickup_zone.locationid
 inner join dim_zones as dropoff_zone
-on trips_unioned.dropoff_locationid = dropoff_zone.locationid
+on trips_unioned.dropoff_locationid = dropoff_zone.locationid

04-analytics-engineering/taxi_rides_ny/models/core/schema.yml

@@ -0,0 +1,129 @@
+version: 2
+
+models:
+  - name: dim_zones
+    description: >
+      List of unique zones idefied by locationid. 
+      Includes the service zone they correspond to (Green or yellow).
+
+  - name: dm_monthly_zone_revenue
+    description: >
+      Aggregated table of all taxi trips corresponding to both service zones (Green and yellow) per pickup zone, month and service.
+      The table contains monthly sums of the fare elements used to calculate the monthly revenue. 
+      The table contains also monthly indicators like number of trips, and average trip distance. 
+    columns:
+      - name: revenue_monthly_total_amount
+        description: Monthly sum of the the total_amount of the fare charged for the trip per pickup zone, month and service.
+        tests:
+            - not_null:
+                severity: error
+      
+  - name: fact_trips
+    description: >
+      Taxi trips corresponding to both service zones (Green and yellow).
+      The table contains records where both pickup and dropoff locations are valid and known zones. 
+      Each record corresponds to a trip uniquely identified by tripid. 
+    columns:
+      - name: tripid
+        data_type: string
+        description: "unique identifier conformed by the combination of vendorid and pickyp time"
+
+      - name: vendorid
+        data_type: int64
+        description: ""
+
+      - name: service_type
+        data_type: string
+        description: ""
+
+      - name: ratecodeid
+        data_type: int64
+        description: ""
+
+      - name: pickup_locationid
+        data_type: int64
+        description: ""
+
+      - name: pickup_borough
+        data_type: string
+        description: ""
+
+      - name: pickup_zone
+        data_type: string
+        description: ""
+
+      - name: dropoff_locationid
+        data_type: int64
+        description: ""
+
+      - name: dropoff_borough
+        data_type: string
+        description: ""
+
+      - name: dropoff_zone
+        data_type: string
+        description: ""
+
+      - name: pickup_datetime
+        data_type: timestamp
+        description: ""
+
+      - name: dropoff_datetime
+        data_type: timestamp
+        description: ""
+
+      - name: store_and_fwd_flag
+        data_type: string
+        description: ""
+
+      - name: passenger_count
+        data_type: int64
+        description: ""
+
+      - name: trip_distance
+        data_type: numeric
+        description: ""
+
+      - name: trip_type
+        data_type: int64
+        description: ""
+
+      - name: fare_amount
+        data_type: numeric
+        description: ""
+
+      - name: extra
+        data_type: numeric
+        description: ""
+
+      - name: mta_tax
+        data_type: numeric
+        description: ""
+
+      - name: tip_amount
+        data_type: numeric
+        description: ""
+
+      - name: tolls_amount
+        data_type: numeric
+        description: ""
+
+      - name: ehail_fee
+        data_type: numeric
+        description: ""
+
+      - name: improvement_surcharge
+        data_type: numeric
+        description: ""
+
+      - name: total_amount
+        data_type: numeric
+        description: ""
+
+      - name: payment_type
+        data_type: int64
+        description: ""
+
+      - name: payment_type_description
+        data_type: string
+        description: ""

04-analytics-engineering/taxi_rides_ny/models/staging/schema.yml

@@ -1,20 +1,16 @@
-
 version: 2
 
 sources:
-    - name: staging
-      #For bigquery:
-      #database: taxi-rides-ny-339813
-
-      # For postgres:
-      database: production
-
-      schema: trips_data_all
+  - name: staging
+    database: taxi-rides-ny-339813-412521
+     # For postgres:
+      #database: production
+    schema: trips_data_all
 
       # loaded_at_field: record_loaded_at
-      tables:
-        - name: green_tripdata
-        - name: yellow_tripdata
+    tables:
+      - name: green_tripdata
+      - name: yellow_tripdata
          # freshness:
            # error_after: {count: 6, period: hour}
 
@@ -75,7 +71,7 @@ models:
               memory before sending to the vendor, aka “store and forward,”
               because the vehicle did not have a connection to the server.
                 Y= store and forward trip
-                N= not a store and forward trip
+                N = not a store and forward trip
           - name: Dropoff_longitude 
             description: Longitude where the meter was disengaged.
           - name: Dropoff_latitude 
@@ -200,4 +196,4 @@ models:
           - name: Tolls_amount 
             description: Total amount of all tolls paid in trip.
           - name: Total_amount 
-            description: The total amount charged to passengers. Does not include cash tips.
+            description: The total amount charged to passengers. Does not include cash tips.

04-analytics-engineering/taxi_rides_ny/models/staging/stg_green_tripdata.sql

@@ -0,0 +1,52 @@
+{{
+    config(
+        materialized='view'
+    )
+}}
+
+with tripdata as 
+(
+  select *,
+    row_number() over(partition by vendorid, lpep_pickup_datetime) as rn
+  from {{ source('staging','green_tripdata') }}
+  where vendorid is not null 
+)
+select
+    -- identifiers
+    {{ dbt_utils.generate_surrogate_key(['vendorid', 'lpep_pickup_datetime']) }} as tripid,
+    {{ dbt.safe_cast("vendorid", api.Column.translate_type("integer")) }} as vendorid,
+    {{ dbt.safe_cast("ratecodeid", api.Column.translate_type("integer")) }} as ratecodeid,
+    {{ dbt.safe_cast("pulocationid", api.Column.translate_type("integer")) }} as pickup_locationid,
+    {{ dbt.safe_cast("dolocationid", api.Column.translate_type("integer")) }} as dropoff_locationid,
+    
+    -- timestamps
+    cast(lpep_pickup_datetime as timestamp) as pickup_datetime,
+    cast(lpep_dropoff_datetime as timestamp) as dropoff_datetime,
+    
+    -- trip info
+    store_and_fwd_flag,
+    {{ dbt.safe_cast("passenger_count", api.Column.translate_type("integer")) }} as passenger_count,
+    cast(trip_distance as numeric) as trip_distance,
+    {{ dbt.safe_cast("trip_type", api.Column.translate_type("integer")) }} as trip_type,
+
+    -- payment info
+    cast(fare_amount as numeric) as fare_amount,
+    cast(extra as numeric) as extra,
+    cast(mta_tax as numeric) as mta_tax,
+    cast(tip_amount as numeric) as tip_amount,
+    cast(tolls_amount as numeric) as tolls_amount,
+    cast(ehail_fee as numeric) as ehail_fee,
+    cast(improvement_surcharge as numeric) as improvement_surcharge,
+    cast(total_amount as numeric) as total_amount,
+    coalesce({{ dbt.safe_cast("payment_type", api.Column.translate_type("integer")) }},0) as payment_type,
+    {{ get_payment_type_description("payment_type") }} as payment_type_description
+from tripdata
+where rn = 1
+
+
+-- dbt build --select <model_name> --vars '{'is_test_run': 'false'}'
+{% if var('is_test_run', default=true) %}
+
+  limit 100
+
+{% endif %}

04-analytics-engineering/taxi_rides_ny/models/staging/stg_yellow_tripdata.sql

@@ -9,19 +9,19 @@ with tripdata as
 )
 select
    -- identifiers
-    {{ dbt_utils.surrogate_key(['vendorid', 'tpep_pickup_datetime']) }} as tripid,
-    cast(vendorid as integer) as vendorid,
-    cast(ratecodeid as integer) as ratecodeid,
-    cast(pulocationid as integer) as  pickup_locationid,
-    cast(dolocationid as integer) as dropoff_locationid,
-    
+    {{ dbt_utils.generate_surrogate_key(['vendorid', 'tpep_pickup_datetime']) }} as tripid,    
+    {{ dbt.safe_cast("vendorid", api.Column.translate_type("integer")) }} as vendorid,
+    {{ dbt.safe_cast("ratecodeid", api.Column.translate_type("integer")) }} as ratecodeid,
+    {{ dbt.safe_cast("pulocationid", api.Column.translate_type("integer")) }} as pickup_locationid,
+    {{ dbt.safe_cast("dolocationid", api.Column.translate_type("integer")) }} as dropoff_locationid,
+
     -- timestamps
     cast(tpep_pickup_datetime as timestamp) as pickup_datetime,
     cast(tpep_dropoff_datetime as timestamp) as dropoff_datetime,
     
     -- trip info
     store_and_fwd_flag,
-    cast(passenger_count as integer) as passenger_count,
+    {{ dbt.safe_cast("passenger_count", api.Column.translate_type("integer")) }} as passenger_count,
     cast(trip_distance as numeric) as trip_distance,
     -- yellow cabs are always street-hail
     1 as trip_type,
@@ -35,16 +35,14 @@ select
     cast(0 as numeric) as ehail_fee,
     cast(improvement_surcharge as numeric) as improvement_surcharge,
     cast(total_amount as numeric) as total_amount,
-    cast(payment_type as integer) as payment_type,
-    {{ get_payment_type_description('payment_type') }} as payment_type_description, 
-    cast(congestion_surcharge as numeric) as congestion_surcharge
+    coalesce({{ dbt.safe_cast("payment_type", api.Column.translate_type("integer")) }},0) as payment_type,
+    {{ get_payment_type_description('payment_type') }} as payment_type_description
 from tripdata
 where rn = 1
 
--- dbt build --m <model.sql> --var 'is_test_run: false'
+-- dbt build --select <model.sql> --vars '{'is_test_run: false}'
 {% if var('is_test_run', default=true) %}
 
   limit 100
 
-{% endif %}
-
+{% endif %}

04-analytics-engineering/taxi_rides_ny/package-lock.yml

@@ -0,0 +1,6 @@
+packages:
+- package: dbt-labs/dbt_utils
+  version: 1.1.1
+- package: dbt-labs/codegen
+  version: 0.12.1
+sha1_hash: d974113b0f072cce35300077208f38581075ab40

04-analytics-engineering/taxi_rides_ny/packages.yml

@@ -0,0 +1,5 @@
+packages:
+  - package: dbt-labs/dbt_utils
+    version: 1.1.1
+  - package: dbt-labs/codegen
+    version: 0.12.1

04-analytics-engineering/taxi_rides_ny/seeds/seeds_properties.yml

@@ -6,5 +6,4 @@ seeds:
       Taxi Zones roughly based on NYC Department of City Planning's Neighborhood
       Tabulation Areas (NTAs) and are meant to approximate neighborhoods, so you can see which
       neighborhood a passenger was picked up in, and which neighborhood they were dropped off in. 
-      Includes associated service_zone (EWR, Boro Zone, Yellow Zone)
-            
+      Includes associated service_zone (EWR, Boro Zone, Yellow Zone)

04-analytics-engineering/taxi_rides_ny/seeds/taxi_zone_lookup.csv

@@ -1,266 +1,266 @@
-"locationid","borough","zone","service_zone"
-1,"EWR","Newark Airport","EWR"
-2,"Queens","Jamaica Bay","Boro Zone"
-3,"Bronx","Allerton/Pelham Gardens","Boro Zone"
-4,"Manhattan","Alphabet City","Yellow Zone"
-5,"Staten Island","Arden Heights","Boro Zone"
-6,"Staten Island","Arrochar/Fort Wadsworth","Boro Zone"
-7,"Queens","Astoria","Boro Zone"
-8,"Queens","Astoria Park","Boro Zone"
-9,"Queens","Auburndale","Boro Zone"
-10,"Queens","Baisley Park","Boro Zone"
-11,"Brooklyn","Bath Beach","Boro Zone"
-12,"Manhattan","Battery Park","Yellow Zone"
-13,"Manhattan","Battery Park City","Yellow Zone"
-14,"Brooklyn","Bay Ridge","Boro Zone"
-15,"Queens","Bay Terrace/Fort Totten","Boro Zone"
-16,"Queens","Bayside","Boro Zone"
-17,"Brooklyn","Bedford","Boro Zone"
-18,"Bronx","Bedford Park","Boro Zone"
-19,"Queens","Bellerose","Boro Zone"
-20,"Bronx","Belmont","Boro Zone"
-21,"Brooklyn","Bensonhurst East","Boro Zone"
-22,"Brooklyn","Bensonhurst West","Boro Zone"
-23,"Staten Island","Bloomfield/Emerson Hill","Boro Zone"
-24,"Manhattan","Bloomingdale","Yellow Zone"
-25,"Brooklyn","Boerum Hill","Boro Zone"
-26,"Brooklyn","Borough Park","Boro Zone"
-27,"Queens","Breezy Point/Fort Tilden/Riis Beach","Boro Zone"
-28,"Queens","Briarwood/Jamaica Hills","Boro Zone"
-29,"Brooklyn","Brighton Beach","Boro Zone"
-30,"Queens","Broad Channel","Boro Zone"
-31,"Bronx","Bronx Park","Boro Zone"
-32,"Bronx","Bronxdale","Boro Zone"
-33,"Brooklyn","Brooklyn Heights","Boro Zone"
-34,"Brooklyn","Brooklyn Navy Yard","Boro Zone"
-35,"Brooklyn","Brownsville","Boro Zone"
-36,"Brooklyn","Bushwick North","Boro Zone"
-37,"Brooklyn","Bushwick South","Boro Zone"
-38,"Queens","Cambria Heights","Boro Zone"
-39,"Brooklyn","Canarsie","Boro Zone"
-40,"Brooklyn","Carroll Gardens","Boro Zone"
-41,"Manhattan","Central Harlem","Boro Zone"
-42,"Manhattan","Central Harlem North","Boro Zone"
-43,"Manhattan","Central Park","Yellow Zone"
-44,"Staten Island","Charleston/Tottenville","Boro Zone"
-45,"Manhattan","Chinatown","Yellow Zone"
-46,"Bronx","City Island","Boro Zone"
-47,"Bronx","Claremont/Bathgate","Boro Zone"
-48,"Manhattan","Clinton East","Yellow Zone"
-49,"Brooklyn","Clinton Hill","Boro Zone"
-50,"Manhattan","Clinton West","Yellow Zone"
-51,"Bronx","Co-Op City","Boro Zone"
-52,"Brooklyn","Cobble Hill","Boro Zone"
-53,"Queens","College Point","Boro Zone"
-54,"Brooklyn","Columbia Street","Boro Zone"
-55,"Brooklyn","Coney Island","Boro Zone"
-56,"Queens","Corona","Boro Zone"
-57,"Queens","Corona","Boro Zone"
-58,"Bronx","Country Club","Boro Zone"
-59,"Bronx","Crotona Park","Boro Zone"
-60,"Bronx","Crotona Park East","Boro Zone"
-61,"Brooklyn","Crown Heights North","Boro Zone"
-62,"Brooklyn","Crown Heights South","Boro Zone"
-63,"Brooklyn","Cypress Hills","Boro Zone"
-64,"Queens","Douglaston","Boro Zone"
-65,"Brooklyn","Downtown Brooklyn/MetroTech","Boro Zone"
-66,"Brooklyn","DUMBO/Vinegar Hill","Boro Zone"
-67,"Brooklyn","Dyker Heights","Boro Zone"
-68,"Manhattan","East Chelsea","Yellow Zone"
-69,"Bronx","East Concourse/Concourse Village","Boro Zone"
-70,"Queens","East Elmhurst","Boro Zone"
-71,"Brooklyn","East Flatbush/Farragut","Boro Zone"
-72,"Brooklyn","East Flatbush/Remsen Village","Boro Zone"
-73,"Queens","East Flushing","Boro Zone"
-74,"Manhattan","East Harlem North","Boro Zone"
-75,"Manhattan","East Harlem South","Boro Zone"
-76,"Brooklyn","East New York","Boro Zone"
-77,"Brooklyn","East New York/Pennsylvania Avenue","Boro Zone"
-78,"Bronx","East Tremont","Boro Zone"
-79,"Manhattan","East Village","Yellow Zone"
-80,"Brooklyn","East Williamsburg","Boro Zone"
-81,"Bronx","Eastchester","Boro Zone"
-82,"Queens","Elmhurst","Boro Zone"
-83,"Queens","Elmhurst/Maspeth","Boro Zone"
-84,"Staten Island","Eltingville/Annadale/Prince's Bay","Boro Zone"
-85,"Brooklyn","Erasmus","Boro Zone"
-86,"Queens","Far Rockaway","Boro Zone"
-87,"Manhattan","Financial District North","Yellow Zone"
-88,"Manhattan","Financial District South","Yellow Zone"
-89,"Brooklyn","Flatbush/Ditmas Park","Boro Zone"
-90,"Manhattan","Flatiron","Yellow Zone"
-91,"Brooklyn","Flatlands","Boro Zone"
-92,"Queens","Flushing","Boro Zone"
-93,"Queens","Flushing Meadows-Corona Park","Boro Zone"
-94,"Bronx","Fordham South","Boro Zone"
-95,"Queens","Forest Hills","Boro Zone"
-96,"Queens","Forest Park/Highland Park","Boro Zone"
-97,"Brooklyn","Fort Greene","Boro Zone"
-98,"Queens","Fresh Meadows","Boro Zone"
-99,"Staten Island","Freshkills Park","Boro Zone"
-100,"Manhattan","Garment District","Yellow Zone"
-101,"Queens","Glen Oaks","Boro Zone"
-102,"Queens","Glendale","Boro Zone"
-103,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
-104,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
-105,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
-106,"Brooklyn","Gowanus","Boro Zone"
-107,"Manhattan","Gramercy","Yellow Zone"
-108,"Brooklyn","Gravesend","Boro Zone"
-109,"Staten Island","Great Kills","Boro Zone"
-110,"Staten Island","Great Kills Park","Boro Zone"
-111,"Brooklyn","Green-Wood Cemetery","Boro Zone"
-112,"Brooklyn","Greenpoint","Boro Zone"
-113,"Manhattan","Greenwich Village North","Yellow Zone"
-114,"Manhattan","Greenwich Village South","Yellow Zone"
-115,"Staten Island","Grymes Hill/Clifton","Boro Zone"
-116,"Manhattan","Hamilton Heights","Boro Zone"
-117,"Queens","Hammels/Arverne","Boro Zone"
-118,"Staten Island","Heartland Village/Todt Hill","Boro Zone"
-119,"Bronx","Highbridge","Boro Zone"
-120,"Manhattan","Highbridge Park","Boro Zone"
-121,"Queens","Hillcrest/Pomonok","Boro Zone"
-122,"Queens","Hollis","Boro Zone"
-123,"Brooklyn","Homecrest","Boro Zone"
-124,"Queens","Howard Beach","Boro Zone"
-125,"Manhattan","Hudson Sq","Yellow Zone"
-126,"Bronx","Hunts Point","Boro Zone"
-127,"Manhattan","Inwood","Boro Zone"
-128,"Manhattan","Inwood Hill Park","Boro Zone"
-129,"Queens","Jackson Heights","Boro Zone"
-130,"Queens","Jamaica","Boro Zone"
-131,"Queens","Jamaica Estates","Boro Zone"
-132,"Queens","JFK Airport","Airports"
-133,"Brooklyn","Kensington","Boro Zone"
-134,"Queens","Kew Gardens","Boro Zone"
-135,"Queens","Kew Gardens Hills","Boro Zone"
-136,"Bronx","Kingsbridge Heights","Boro Zone"
-137,"Manhattan","Kips Bay","Yellow Zone"
-138,"Queens","LaGuardia Airport","Airports"
-139,"Queens","Laurelton","Boro Zone"
-140,"Manhattan","Lenox Hill East","Yellow Zone"
-141,"Manhattan","Lenox Hill West","Yellow Zone"
-142,"Manhattan","Lincoln Square East","Yellow Zone"
-143,"Manhattan","Lincoln Square West","Yellow Zone"
-144,"Manhattan","Little Italy/NoLiTa","Yellow Zone"
-145,"Queens","Long Island City/Hunters Point","Boro Zone"
-146,"Queens","Long Island City/Queens Plaza","Boro Zone"
-147,"Bronx","Longwood","Boro Zone"
-148,"Manhattan","Lower East Side","Yellow Zone"
-149,"Brooklyn","Madison","Boro Zone"
-150,"Brooklyn","Manhattan Beach","Boro Zone"
-151,"Manhattan","Manhattan Valley","Yellow Zone"
-152,"Manhattan","Manhattanville","Boro Zone"
-153,"Manhattan","Marble Hill","Boro Zone"
-154,"Brooklyn","Marine Park/Floyd Bennett Field","Boro Zone"
-155,"Brooklyn","Marine Park/Mill Basin","Boro Zone"
-156,"Staten Island","Mariners Harbor","Boro Zone"
-157,"Queens","Maspeth","Boro Zone"
-158,"Manhattan","Meatpacking/West Village West","Yellow Zone"
-159,"Bronx","Melrose South","Boro Zone"
-160,"Queens","Middle Village","Boro Zone"
-161,"Manhattan","Midtown Center","Yellow Zone"
-162,"Manhattan","Midtown East","Yellow Zone"
-163,"Manhattan","Midtown North","Yellow Zone"
-164,"Manhattan","Midtown South","Yellow Zone"
-165,"Brooklyn","Midwood","Boro Zone"
-166,"Manhattan","Morningside Heights","Boro Zone"
-167,"Bronx","Morrisania/Melrose","Boro Zone"
-168,"Bronx","Mott Haven/Port Morris","Boro Zone"
-169,"Bronx","Mount Hope","Boro Zone"
-170,"Manhattan","Murray Hill","Yellow Zone"
-171,"Queens","Murray Hill-Queens","Boro Zone"
-172,"Staten Island","New Dorp/Midland Beach","Boro Zone"
-173,"Queens","North Corona","Boro Zone"
-174,"Bronx","Norwood","Boro Zone"
-175,"Queens","Oakland Gardens","Boro Zone"
-176,"Staten Island","Oakwood","Boro Zone"
-177,"Brooklyn","Ocean Hill","Boro Zone"
-178,"Brooklyn","Ocean Parkway South","Boro Zone"
-179,"Queens","Old Astoria","Boro Zone"
-180,"Queens","Ozone Park","Boro Zone"
-181,"Brooklyn","Park Slope","Boro Zone"
-182,"Bronx","Parkchester","Boro Zone"
-183,"Bronx","Pelham Bay","Boro Zone"
-184,"Bronx","Pelham Bay Park","Boro Zone"
-185,"Bronx","Pelham Parkway","Boro Zone"
-186,"Manhattan","Penn Station/Madison Sq West","Yellow Zone"
-187,"Staten Island","Port Richmond","Boro Zone"
-188,"Brooklyn","Prospect-Lefferts Gardens","Boro Zone"
-189,"Brooklyn","Prospect Heights","Boro Zone"
-190,"Brooklyn","Prospect Park","Boro Zone"
-191,"Queens","Queens Village","Boro Zone"
-192,"Queens","Queensboro Hill","Boro Zone"
-193,"Queens","Queensbridge/Ravenswood","Boro Zone"
-194,"Manhattan","Randalls Island","Yellow Zone"
-195,"Brooklyn","Red Hook","Boro Zone"
-196,"Queens","Rego Park","Boro Zone"
-197,"Queens","Richmond Hill","Boro Zone"
-198,"Queens","Ridgewood","Boro Zone"
-199,"Bronx","Rikers Island","Boro Zone"
-200,"Bronx","Riverdale/North Riverdale/Fieldston","Boro Zone"
-201,"Queens","Rockaway Park","Boro Zone"
-202,"Manhattan","Roosevelt Island","Boro Zone"
-203,"Queens","Rosedale","Boro Zone"
-204,"Staten Island","Rossville/Woodrow","Boro Zone"
-205,"Queens","Saint Albans","Boro Zone"
-206,"Staten Island","Saint George/New Brighton","Boro Zone"
-207,"Queens","Saint Michaels Cemetery/Woodside","Boro Zone"
-208,"Bronx","Schuylerville/Edgewater Park","Boro Zone"
-209,"Manhattan","Seaport","Yellow Zone"
-210,"Brooklyn","Sheepshead Bay","Boro Zone"
-211,"Manhattan","SoHo","Yellow Zone"
-212,"Bronx","Soundview/Bruckner","Boro Zone"
-213,"Bronx","Soundview/Castle Hill","Boro Zone"
-214,"Staten Island","South Beach/Dongan Hills","Boro Zone"
-215,"Queens","South Jamaica","Boro Zone"
-216,"Queens","South Ozone Park","Boro Zone"
-217,"Brooklyn","South Williamsburg","Boro Zone"
-218,"Queens","Springfield Gardens North","Boro Zone"
-219,"Queens","Springfield Gardens South","Boro Zone"
-220,"Bronx","Spuyten Duyvil/Kingsbridge","Boro Zone"
-221,"Staten Island","Stapleton","Boro Zone"
-222,"Brooklyn","Starrett City","Boro Zone"
-223,"Queens","Steinway","Boro Zone"
-224,"Manhattan","Stuy Town/Peter Cooper Village","Yellow Zone"
-225,"Brooklyn","Stuyvesant Heights","Boro Zone"
-226,"Queens","Sunnyside","Boro Zone"
-227,"Brooklyn","Sunset Park East","Boro Zone"
-228,"Brooklyn","Sunset Park West","Boro Zone"
-229,"Manhattan","Sutton Place/Turtle Bay North","Yellow Zone"
-230,"Manhattan","Times Sq/Theatre District","Yellow Zone"
-231,"Manhattan","TriBeCa/Civic Center","Yellow Zone"
-232,"Manhattan","Two Bridges/Seward Park","Yellow Zone"
-233,"Manhattan","UN/Turtle Bay South","Yellow Zone"
-234,"Manhattan","Union Sq","Yellow Zone"
-235,"Bronx","University Heights/Morris Heights","Boro Zone"
-236,"Manhattan","Upper East Side North","Yellow Zone"
-237,"Manhattan","Upper East Side South","Yellow Zone"
-238,"Manhattan","Upper West Side North","Yellow Zone"
-239,"Manhattan","Upper West Side South","Yellow Zone"
-240,"Bronx","Van Cortlandt Park","Boro Zone"
-241,"Bronx","Van Cortlandt Village","Boro Zone"
-242,"Bronx","Van Nest/Morris Park","Boro Zone"
-243,"Manhattan","Washington Heights North","Boro Zone"
-244,"Manhattan","Washington Heights South","Boro Zone"
-245,"Staten Island","West Brighton","Boro Zone"
-246,"Manhattan","West Chelsea/Hudson Yards","Yellow Zone"
-247,"Bronx","West Concourse","Boro Zone"
-248,"Bronx","West Farms/Bronx River","Boro Zone"
-249,"Manhattan","West Village","Yellow Zone"
-250,"Bronx","Westchester Village/Unionport","Boro Zone"
-251,"Staten Island","Westerleigh","Boro Zone"
-252,"Queens","Whitestone","Boro Zone"
-253,"Queens","Willets Point","Boro Zone"
-254,"Bronx","Williamsbridge/Olinville","Boro Zone"
-255,"Brooklyn","Williamsburg (North Side)","Boro Zone"
-256,"Brooklyn","Williamsburg (South Side)","Boro Zone"
-257,"Brooklyn","Windsor Terrace","Boro Zone"
-258,"Queens","Woodhaven","Boro Zone"
-259,"Bronx","Woodlawn/Wakefield","Boro Zone"
-260,"Queens","Woodside","Boro Zone"
-261,"Manhattan","World Trade Center","Yellow Zone"
-262,"Manhattan","Yorkville East","Yellow Zone"
-263,"Manhattan","Yorkville West","Yellow Zone"
-264,"Unknown","NV","N/A"
-265,"Unknown","NA","N/A"
+"locationid","borough","zone","service_zone"
+1,"EWR","Newark Airport","EWR"
+2,"Queens","Jamaica Bay","Boro Zone"
+3,"Bronx","Allerton/Pelham Gardens","Boro Zone"
+4,"Manhattan","Alphabet City","Yellow Zone"
+5,"Staten Island","Arden Heights","Boro Zone"
+6,"Staten Island","Arrochar/Fort Wadsworth","Boro Zone"
+7,"Queens","Astoria","Boro Zone"
+8,"Queens","Astoria Park","Boro Zone"
+9,"Queens","Auburndale","Boro Zone"
+10,"Queens","Baisley Park","Boro Zone"
+11,"Brooklyn","Bath Beach","Boro Zone"
+12,"Manhattan","Battery Park","Yellow Zone"
+13,"Manhattan","Battery Park City","Yellow Zone"
+14,"Brooklyn","Bay Ridge","Boro Zone"
+15,"Queens","Bay Terrace/Fort Totten","Boro Zone"
+16,"Queens","Bayside","Boro Zone"
+17,"Brooklyn","Bedford","Boro Zone"
+18,"Bronx","Bedford Park","Boro Zone"
+19,"Queens","Bellerose","Boro Zone"
+20,"Bronx","Belmont","Boro Zone"
+21,"Brooklyn","Bensonhurst East","Boro Zone"
+22,"Brooklyn","Bensonhurst West","Boro Zone"
+23,"Staten Island","Bloomfield/Emerson Hill","Boro Zone"
+24,"Manhattan","Bloomingdale","Yellow Zone"
+25,"Brooklyn","Boerum Hill","Boro Zone"
+26,"Brooklyn","Borough Park","Boro Zone"
+27,"Queens","Breezy Point/Fort Tilden/Riis Beach","Boro Zone"
+28,"Queens","Briarwood/Jamaica Hills","Boro Zone"
+29,"Brooklyn","Brighton Beach","Boro Zone"
+30,"Queens","Broad Channel","Boro Zone"
+31,"Bronx","Bronx Park","Boro Zone"
+32,"Bronx","Bronxdale","Boro Zone"
+33,"Brooklyn","Brooklyn Heights","Boro Zone"
+34,"Brooklyn","Brooklyn Navy Yard","Boro Zone"
+35,"Brooklyn","Brownsville","Boro Zone"
+36,"Brooklyn","Bushwick North","Boro Zone"
+37,"Brooklyn","Bushwick South","Boro Zone"
+38,"Queens","Cambria Heights","Boro Zone"
+39,"Brooklyn","Canarsie","Boro Zone"
+40,"Brooklyn","Carroll Gardens","Boro Zone"
+41,"Manhattan","Central Harlem","Boro Zone"
+42,"Manhattan","Central Harlem North","Boro Zone"
+43,"Manhattan","Central Park","Yellow Zone"
+44,"Staten Island","Charleston/Tottenville","Boro Zone"
+45,"Manhattan","Chinatown","Yellow Zone"
+46,"Bronx","City Island","Boro Zone"
+47,"Bronx","Claremont/Bathgate","Boro Zone"
+48,"Manhattan","Clinton East","Yellow Zone"
+49,"Brooklyn","Clinton Hill","Boro Zone"
+50,"Manhattan","Clinton West","Yellow Zone"
+51,"Bronx","Co-Op City","Boro Zone"
+52,"Brooklyn","Cobble Hill","Boro Zone"
+53,"Queens","College Point","Boro Zone"
+54,"Brooklyn","Columbia Street","Boro Zone"
+55,"Brooklyn","Coney Island","Boro Zone"
+56,"Queens","Corona","Boro Zone"
+57,"Queens","Corona","Boro Zone"
+58,"Bronx","Country Club","Boro Zone"
+59,"Bronx","Crotona Park","Boro Zone"
+60,"Bronx","Crotona Park East","Boro Zone"
+61,"Brooklyn","Crown Heights North","Boro Zone"
+62,"Brooklyn","Crown Heights South","Boro Zone"
+63,"Brooklyn","Cypress Hills","Boro Zone"
+64,"Queens","Douglaston","Boro Zone"
+65,"Brooklyn","Downtown Brooklyn/MetroTech","Boro Zone"
+66,"Brooklyn","DUMBO/Vinegar Hill","Boro Zone"
+67,"Brooklyn","Dyker Heights","Boro Zone"
+68,"Manhattan","East Chelsea","Yellow Zone"
+69,"Bronx","East Concourse/Concourse Village","Boro Zone"
+70,"Queens","East Elmhurst","Boro Zone"
+71,"Brooklyn","East Flatbush/Farragut","Boro Zone"
+72,"Brooklyn","East Flatbush/Remsen Village","Boro Zone"
+73,"Queens","East Flushing","Boro Zone"
+74,"Manhattan","East Harlem North","Boro Zone"
+75,"Manhattan","East Harlem South","Boro Zone"
+76,"Brooklyn","East New York","Boro Zone"
+77,"Brooklyn","East New York/Pennsylvania Avenue","Boro Zone"
+78,"Bronx","East Tremont","Boro Zone"
+79,"Manhattan","East Village","Yellow Zone"
+80,"Brooklyn","East Williamsburg","Boro Zone"
+81,"Bronx","Eastchester","Boro Zone"
+82,"Queens","Elmhurst","Boro Zone"
+83,"Queens","Elmhurst/Maspeth","Boro Zone"
+84,"Staten Island","Eltingville/Annadale/Prince's Bay","Boro Zone"
+85,"Brooklyn","Erasmus","Boro Zone"
+86,"Queens","Far Rockaway","Boro Zone"
+87,"Manhattan","Financial District North","Yellow Zone"
+88,"Manhattan","Financial District South","Yellow Zone"
+89,"Brooklyn","Flatbush/Ditmas Park","Boro Zone"
+90,"Manhattan","Flatiron","Yellow Zone"
+91,"Brooklyn","Flatlands","Boro Zone"
+92,"Queens","Flushing","Boro Zone"
+93,"Queens","Flushing Meadows-Corona Park","Boro Zone"
+94,"Bronx","Fordham South","Boro Zone"
+95,"Queens","Forest Hills","Boro Zone"
+96,"Queens","Forest Park/Highland Park","Boro Zone"
+97,"Brooklyn","Fort Greene","Boro Zone"
+98,"Queens","Fresh Meadows","Boro Zone"
+99,"Staten Island","Freshkills Park","Boro Zone"
+100,"Manhattan","Garment District","Yellow Zone"
+101,"Queens","Glen Oaks","Boro Zone"
+102,"Queens","Glendale","Boro Zone"
+103,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
+104,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
+105,"Manhattan","Governor's Island/Ellis Island/Liberty Island","Yellow Zone"
+106,"Brooklyn","Gowanus","Boro Zone"
+107,"Manhattan","Gramercy","Yellow Zone"
+108,"Brooklyn","Gravesend","Boro Zone"
+109,"Staten Island","Great Kills","Boro Zone"
+110,"Staten Island","Great Kills Park","Boro Zone"
+111,"Brooklyn","Green-Wood Cemetery","Boro Zone"
+112,"Brooklyn","Greenpoint","Boro Zone"
+113,"Manhattan","Greenwich Village North","Yellow Zone"
+114,"Manhattan","Greenwich Village South","Yellow Zone"
+115,"Staten Island","Grymes Hill/Clifton","Boro Zone"
+116,"Manhattan","Hamilton Heights","Boro Zone"
+117,"Queens","Hammels/Arverne","Boro Zone"
+118,"Staten Island","Heartland Village/Todt Hill","Boro Zone"
+119,"Bronx","Highbridge","Boro Zone"
+120,"Manhattan","Highbridge Park","Boro Zone"
+121,"Queens","Hillcrest/Pomonok","Boro Zone"
+122,"Queens","Hollis","Boro Zone"
+123,"Brooklyn","Homecrest","Boro Zone"
+124,"Queens","Howard Beach","Boro Zone"
+125,"Manhattan","Hudson Sq","Yellow Zone"
+126,"Bronx","Hunts Point","Boro Zone"
+127,"Manhattan","Inwood","Boro Zone"
+128,"Manhattan","Inwood Hill Park","Boro Zone"
+129,"Queens","Jackson Heights","Boro Zone"
+130,"Queens","Jamaica","Boro Zone"
+131,"Queens","Jamaica Estates","Boro Zone"
+132,"Queens","JFK Airport","Airports"
+133,"Brooklyn","Kensington","Boro Zone"
+134,"Queens","Kew Gardens","Boro Zone"
+135,"Queens","Kew Gardens Hills","Boro Zone"
+136,"Bronx","Kingsbridge Heights","Boro Zone"
+137,"Manhattan","Kips Bay","Yellow Zone"
+138,"Queens","LaGuardia Airport","Airports"
+139,"Queens","Laurelton","Boro Zone"
+140,"Manhattan","Lenox Hill East","Yellow Zone"
+141,"Manhattan","Lenox Hill West","Yellow Zone"
+142,"Manhattan","Lincoln Square East","Yellow Zone"
+143,"Manhattan","Lincoln Square West","Yellow Zone"
+144,"Manhattan","Little Italy/NoLiTa","Yellow Zone"
+145,"Queens","Long Island City/Hunters Point","Boro Zone"
+146,"Queens","Long Island City/Queens Plaza","Boro Zone"
+147,"Bronx","Longwood","Boro Zone"
+148,"Manhattan","Lower East Side","Yellow Zone"
+149,"Brooklyn","Madison","Boro Zone"
+150,"Brooklyn","Manhattan Beach","Boro Zone"
+151,"Manhattan","Manhattan Valley","Yellow Zone"
+152,"Manhattan","Manhattanville","Boro Zone"
+153,"Manhattan","Marble Hill","Boro Zone"
+154,"Brooklyn","Marine Park/Floyd Bennett Field","Boro Zone"
+155,"Brooklyn","Marine Park/Mill Basin","Boro Zone"
+156,"Staten Island","Mariners Harbor","Boro Zone"
+157,"Queens","Maspeth","Boro Zone"
+158,"Manhattan","Meatpacking/West Village West","Yellow Zone"
+159,"Bronx","Melrose South","Boro Zone"
+160,"Queens","Middle Village","Boro Zone"
+161,"Manhattan","Midtown Center","Yellow Zone"
+162,"Manhattan","Midtown East","Yellow Zone"
+163,"Manhattan","Midtown North","Yellow Zone"
+164,"Manhattan","Midtown South","Yellow Zone"
+165,"Brooklyn","Midwood","Boro Zone"
+166,"Manhattan","Morningside Heights","Boro Zone"
+167,"Bronx","Morrisania/Melrose","Boro Zone"
+168,"Bronx","Mott Haven/Port Morris","Boro Zone"
+169,"Bronx","Mount Hope","Boro Zone"
+170,"Manhattan","Murray Hill","Yellow Zone"
+171,"Queens","Murray Hill-Queens","Boro Zone"
+172,"Staten Island","New Dorp/Midland Beach","Boro Zone"
+173,"Queens","North Corona","Boro Zone"
+174,"Bronx","Norwood","Boro Zone"
+175,"Queens","Oakland Gardens","Boro Zone"
+176,"Staten Island","Oakwood","Boro Zone"
+177,"Brooklyn","Ocean Hill","Boro Zone"
+178,"Brooklyn","Ocean Parkway South","Boro Zone"
+179,"Queens","Old Astoria","Boro Zone"
+180,"Queens","Ozone Park","Boro Zone"
+181,"Brooklyn","Park Slope","Boro Zone"
+182,"Bronx","Parkchester","Boro Zone"
+183,"Bronx","Pelham Bay","Boro Zone"
+184,"Bronx","Pelham Bay Park","Boro Zone"
+185,"Bronx","Pelham Parkway","Boro Zone"
+186,"Manhattan","Penn Station/Madison Sq West","Yellow Zone"
+187,"Staten Island","Port Richmond","Boro Zone"
+188,"Brooklyn","Prospect-Lefferts Gardens","Boro Zone"
+189,"Brooklyn","Prospect Heights","Boro Zone"
+190,"Brooklyn","Prospect Park","Boro Zone"
+191,"Queens","Queens Village","Boro Zone"
+192,"Queens","Queensboro Hill","Boro Zone"
+193,"Queens","Queensbridge/Ravenswood","Boro Zone"
+194,"Manhattan","Randalls Island","Yellow Zone"
+195,"Brooklyn","Red Hook","Boro Zone"
+196,"Queens","Rego Park","Boro Zone"
+197,"Queens","Richmond Hill","Boro Zone"
+198,"Queens","Ridgewood","Boro Zone"
+199,"Bronx","Rikers Island","Boro Zone"
+200,"Bronx","Riverdale/North Riverdale/Fieldston","Boro Zone"
+201,"Queens","Rockaway Park","Boro Zone"
+202,"Manhattan","Roosevelt Island","Boro Zone"
+203,"Queens","Rosedale","Boro Zone"
+204,"Staten Island","Rossville/Woodrow","Boro Zone"
+205,"Queens","Saint Albans","Boro Zone"
+206,"Staten Island","Saint George/New Brighton","Boro Zone"
+207,"Queens","Saint Michaels Cemetery/Woodside","Boro Zone"
+208,"Bronx","Schuylerville/Edgewater Park","Boro Zone"
+209,"Manhattan","Seaport","Yellow Zone"
+210,"Brooklyn","Sheepshead Bay","Boro Zone"
+211,"Manhattan","SoHo","Yellow Zone"
+212,"Bronx","Soundview/Bruckner","Boro Zone"
+213,"Bronx","Soundview/Castle Hill","Boro Zone"
+214,"Staten Island","South Beach/Dongan Hills","Boro Zone"
+215,"Queens","South Jamaica","Boro Zone"
+216,"Queens","South Ozone Park","Boro Zone"
+217,"Brooklyn","South Williamsburg","Boro Zone"
+218,"Queens","Springfield Gardens North","Boro Zone"
+219,"Queens","Springfield Gardens South","Boro Zone"
+220,"Bronx","Spuyten Duyvil/Kingsbridge","Boro Zone"
+221,"Staten Island","Stapleton","Boro Zone"
+222,"Brooklyn","Starrett City","Boro Zone"
+223,"Queens","Steinway","Boro Zone"
+224,"Manhattan","Stuy Town/Peter Cooper Village","Yellow Zone"
+225,"Brooklyn","Stuyvesant Heights","Boro Zone"
+226,"Queens","Sunnyside","Boro Zone"
+227,"Brooklyn","Sunset Park East","Boro Zone"
+228,"Brooklyn","Sunset Park West","Boro Zone"
+229,"Manhattan","Sutton Place/Turtle Bay North","Yellow Zone"
+230,"Manhattan","Times Sq/Theatre District","Yellow Zone"
+231,"Manhattan","TriBeCa/Civic Center","Yellow Zone"
+232,"Manhattan","Two Bridges/Seward Park","Yellow Zone"
+233,"Manhattan","UN/Turtle Bay South","Yellow Zone"
+234,"Manhattan","Union Sq","Yellow Zone"
+235,"Bronx","University Heights/Morris Heights","Boro Zone"
+236,"Manhattan","Upper East Side North","Yellow Zone"
+237,"Manhattan","Upper East Side South","Yellow Zone"
+238,"Manhattan","Upper West Side North","Yellow Zone"
+239,"Manhattan","Upper West Side South","Yellow Zone"
+240,"Bronx","Van Cortlandt Park","Boro Zone"
+241,"Bronx","Van Cortlandt Village","Boro Zone"
+242,"Bronx","Van Nest/Morris Park","Boro Zone"
+243,"Manhattan","Washington Heights North","Boro Zone"
+244,"Manhattan","Washington Heights South","Boro Zone"
+245,"Staten Island","West Brighton","Boro Zone"
+246,"Manhattan","West Chelsea/Hudson Yards","Yellow Zone"
+247,"Bronx","West Concourse","Boro Zone"
+248,"Bronx","West Farms/Bronx River","Boro Zone"
+249,"Manhattan","West Village","Yellow Zone"
+250,"Bronx","Westchester Village/Unionport","Boro Zone"
+251,"Staten Island","Westerleigh","Boro Zone"
+252,"Queens","Whitestone","Boro Zone"
+253,"Queens","Willets Point","Boro Zone"
+254,"Bronx","Williamsbridge/Olinville","Boro Zone"
+255,"Brooklyn","Williamsburg (North Side)","Boro Zone"
+256,"Brooklyn","Williamsburg (South Side)","Boro Zone"
+257,"Brooklyn","Windsor Terrace","Boro Zone"
+258,"Queens","Woodhaven","Boro Zone"
+259,"Bronx","Woodlawn/Wakefield","Boro Zone"
+260,"Queens","Woodside","Boro Zone"
+261,"Manhattan","World Trade Center","Yellow Zone"
+262,"Manhattan","Yorkville East","Yellow Zone"
+263,"Manhattan","Yorkville West","Yellow Zone"
+264,"Unknown","NV","N/A"
+265,"Unknown","NA","N/A"

05-batch/README.md

@@ -1,12 +1,12 @@
-## Week 5: Batch Processing
+# Week 5: Batch Processing
 
-### 5.1 Introduction
+## 5.1 Introduction
 
 * :movie_camera: 5.1.1 [Introduction to Batch Processing](https://youtu.be/dcHe5Fl3MF8?list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 * :movie_camera: 5.1.2 [Introduction to Spark](https://youtu.be/FhaqbEOuQ8U?list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
 
-### 5.2 Installation
+## 5.2 Installation
 
 Follow [these intructions](setup/) to install Spark:
 
@@ -19,7 +19,7 @@ And follow [this](setup/pyspark.md) to run PySpark in Jupyter
 * :movie_camera: 5.2.1 [(Optional) Installing Spark (Linux)](https://youtu.be/hqUbB9c8sKg?list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
 
-### 5.3 Spark SQL and DataFrames
+## 5.3 Spark SQL and DataFrames
 
 * :movie_camera: 5.3.1 [First Look at Spark/PySpark](https://youtu.be/r_Sf6fCB40c?list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb) 
 * :movie_camera: 5.3.2 [Spark Dataframes](https://youtu.be/ti3aC1m3rE8?list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
@@ -32,19 +32,19 @@ Script to prepare the Dataset [download_data.sh](code/download_data.sh)
 * :movie_camera: 5.3.4 [SQL with Spark](https://www.youtube.com/watch?v=uAlp2VuZZPY&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
 
-### 5.4 Spark Internals
+## 5.4 Spark Internals
 
 * :movie_camera: 5.4.1 [Anatomy of a Spark Cluster](https://youtu.be/68CipcZt7ZA&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 * :movie_camera: 5.4.2 [GroupBy in Spark](https://youtu.be/9qrDsY_2COo&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 * :movie_camera: 5.4.3 [Joins in Spark](https://youtu.be/lu7TrqAWuH4&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
-### 5.5 (Optional) Resilient Distributed Datasets
+## 5.5 (Optional) Resilient Distributed Datasets
 
 * :movie_camera: 5.5.1 [Operations on Spark RDDs](https://youtu.be/Bdu-xIrF3OM&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 * :movie_camera: 5.5.2 [Spark RDD mapPartition](https://youtu.be/k3uB2K99roI&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
 
-### 5.6 Running Spark in the Cloud
+## 5.6 Running Spark in the Cloud
 
 * :movie_camera: 5.6.1 [Connecting to Google Cloud Storage ](https://youtu.be/Yyz293hBVcQ&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 * :movie_camera: 5.6.2 [Creating a Local Spark Cluster](https://youtu.be/HXBwSlXo5IA&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
@@ -52,13 +52,13 @@ Script to prepare the Dataset [download_data.sh](code/download_data.sh)
 * :movie_camera: 5.6.4 [Connecting Spark to Big Query](https://youtu.be/HIm2BOj8C0Q&list=PL3MmuxUbc_hJed7dXYoJw8DoCuVHhGEQb)
 
 
-### Homework
+# Homework
 
 
-* [Homework](../cohorts/2023/week_5_batch_processing/homework.md)
+* [2024 Homework](../cohorts/2024)
 
 
-## Community notes
+# Community notes
 
 Did you take notes? You can share them here.
 
@@ -68,4 +68,5 @@ Did you take notes? You can share them here.
 * [Alternative : Using docker-compose to launch spark by rafik](https://gist.github.com/rafik-rahoui/f98df941c4ccced9c46e9ccbdef63a03) 
 * [Marcos Torregrosa's blog (spanish)](https://www.n4gash.com/2023/data-engineering-zoomcamp-semana-5-batch-spark)
 * [Notes by Victor Padilha](https://github.com/padilha/de-zoomcamp/tree/master/week5)
+* [Notes by Oscar Garcia](https://github.com/ozkary/Data-Engineering-Bootcamp/tree/main/Step5-Batch-Processing)
 * Add your notes here (above this line)

05-batch/setup/macos.md

@@ -10,7 +10,7 @@ for other MacOS versions as well
 Ensure Brew and Java installed in your system:
 
 ```bash
-xcode-select –install
+xcode-select --install
 /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"
 brew install java
 ```

06-streaming/README.md

@@ -1,6 +1,6 @@
 # Week 6: Stream Processing
 
-## Code structure
+# Code structure
 * [Java examples](java)
 * [Python examples](python)
 * [KSQLD examples](ksqldb)
@@ -74,13 +74,7 @@ Please follow the steps described under [pyspark-streaming](python/streams-examp
 
 ## Homework
 
-[Form](https://forms.gle/rK7268U92mHJBpmW7)
-
-The homework is mostly theoretical. In the last question you have to provide working code link, please keep in mind that this
-question is not scored.
-
-Deadline: 13 March 2023, 22:00 CET
-
+* [2024 Homework](../cohorts/2024/)
 
 ## Community notes
 
@@ -88,5 +82,6 @@ Did you take notes? You can share them here.
 
 * [Notes by Alvaro Navas](https://github.com/ziritrion/dataeng-zoomcamp/blob/main/notes/6_streaming.md )
 * [Marcos Torregrosa's blog (spanish)](https://www.n4gash.com/2023/data-engineering-zoomcamp-semana-6-stream-processing/)
+* [Notes by Oscar Garcia](https://github.com/ozkary/Data-Engineering-Bootcamp/tree/main/Step6-Streaming)
 * Add your notes here (above this line)