├── assets └── private-fork.png ├── DATA_LICENSE ├── README.md ├── LICENSE └── odyssey.ipynb /assets/private-fork.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dotlas/dotlas_odyssey/main/assets/private-fork.png -------------------------------------------------------------------------------- /DATA_LICENSE: -------------------------------------------------------------------------------- 1 | Data Usage License 2 | Version 1.0, 2023-09-02 3 | 4 | Copyright (c) 2023, Dotlas Inc. 5 | All Rights Reserved 6 | 7 | I. Definitions 8 | 9 | 1. "Data" refers to all information, including but not limited to data provided by Dotlas through S3 URLs for the purpose of this assignment. 10 | 2. "Derivative Work" refers to any data, content, or work created by the licensee that is based upon, derived from, or includes any portion of the Data. 11 | 12 | II. Grant of License 13 | Subject to the terms and conditions of this License, Dotlas Inc. hereby grants to the licensee a non-exclusive, non-transferable, worldwide, royalty-free license to use, copy, modify, and distribute the Data and Derivative Works for research and open-source purposes only. 14 | 15 | III. Restrictions 16 | 17 | 1. Commercial Use: The licensee is not permitted to use the Data or any Derivative Work for commercial purposes, including but not limited to, the sale, resale, redistribution, licensing, or use in internal or proprietary models or systems, without the express written permission of Dotlas Inc.. 18 | 2. No Sublicensing: The licensee may not sublicense the Data or any Derivative Work. 19 | 20 | IV. Disclaimer of Warranty 21 | The Data is provided "as is" without warranty of any kind, either express or implied, including without limitation, any warranty of merchantability, fitness for a particular purpose, or non-infringement. 22 | 23 | V. Limitation of Liability 24 | In no event shall Dotlas Inc. be liable for any damages, including but not limited to, direct, indirect, special, incidental, or consequential damages, arising out of or in connection with the use or inability to use the Data, even if Dotlas Inc. has been advised of the possibility of such damages. 25 | 26 | VI. Termination 27 | This License will terminate automatically without notice from Dotlas Inc. if the licensee fails to comply with any provision of this License. Upon termination, the licensee must cease all use of the Data and destroy all copies of the Data in their possession. 28 | 29 | VII. Governing Law 30 | This License shall be governed by and construed in accordance with the laws of the State of Delaware, without regard to its conflict of laws principles. 31 | 32 | VIII. Entire Agreement 33 | This License constitutes the entire agreement between the parties with respect to the use of the Data and supersedes all prior or contemporaneous understandings regarding such subject matter. 34 | 35 | By using the Data, the licensee agrees to be bound by the terms and conditions of this License. 36 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Dotlas Odyssey ⛵️ 2 | 3 | A data engineering assignment 4 | 5 | - [Dotlas Odyssey ⛵️](#dotlas-odyssey-️) 6 | - [Overview 🗼](#overview-) 7 | - [Stage 1: **Amalgamation** 🪢](#stage-1-amalgamation-) 8 | - [Stage 2: **Augmentation** 🧱](#stage-2-augmentation-) 9 | - [Stage 3: **Acclamation** ☑️](#stage-3-acclamation-️) 10 | - [Deliverables](#deliverables) 11 | - [Getting started 🏎](#getting-started-) 12 | - [Create a Private fork 🍴](#create-a-private-fork-) 13 | - [Install Python `v3.9` or higher 🐍](#install-python-v39-or-higher-) 14 | - [Install dependencies 📦](#install-dependencies-) 15 | - [Get crackin' ⌛️](#get-crackin-️) 16 | - [Submission 🏝](#submission-) 17 | - [Grading Criteria 📝](#grading-criteria-) 18 | - [Problem Solving 🕵🏼‍♀️](#problem-solving-️) 19 | - [Data 📇](#data-) 20 | - [Pipeline ⛳️](#pipeline-️) 21 | - [Code 🧼](#code-) 22 | - [Contact Us 💬](#contact-us-) 23 | 24 | ## Overview 🗼 25 | 26 | In this assignment, you will embark on a data expedition involving the amalgamation of disparate data sources, web scraping for additional data fields, and the implementation of data quality tests to ensure production readiness. 27 | 28 | ### Stage 1: **Amalgamation** 🪢 29 | 30 | In the first stage, you will be provided with multiple smaller datasets, each containing a subset of the overall data needed for the expedition. Your task will be to combine these smaller datasets into a single, cohesive dataset that will serve as the foundation for the next stage of the assignment. 31 | 32 | - Load the provided datasets into a suitable data structure, such as a pandas DataFrame. 33 | - Perform exploratory data analysis (EDA) to understand the structure, format, and quality of the data. 34 | - Combine the smaller datasets into a single dataset, ensuring that the data is correctly aligned and that there are no duplicate or missing entries. 35 | - Perform initial data cleaning and transformation tasks, such as dealing with missing values, normalizing data, and transforming data types. 36 | 37 | ### Stage 2: **Augmentation** 🧱 38 | 39 | In the second stage, you will augment the dataset created in Stage 1 by extracting additional fields from the web. These additional fields will provide more context and depth to the dataset, making it more valuable for production use-cases. 40 | 41 | - Identify suitable web sources from which to extract the additional fields. 42 | - Use web scraping techniques, such as BeautifulSoup or Scrapy, to extract the additional fields from the identified web sources. 43 | - Merge the extracted data with the dataset created in Stage 1, ensuring that the data is correctly aligned and that there are no duplicate or missing entries. 44 | 45 | ### Stage 3: **Acclamation** ☑️ 46 | 47 | In the final stage, you will implement a series of data quality tests to ensure that the final dataset is of high quality and ready for production use. 48 | 49 | - Define a set of data quality tests that the final dataset must pass. These tests should cover aspects such as completeness, uniqueness, validity, accuracy, and consistency. 50 | - Implement the defined data quality tests using appropriate testing frameworks, such as pytest. 51 | - Run the implemented tests on the final dataset and document the results. 52 | 53 | ### Deliverables 54 | 55 | Upon completion of the assignment, you should have a single, cohesive dataset that has been augmented with additional fields extracted from the web and that has passed all defined data quality tests. 56 | 57 | ## Getting started 🏎 58 | 59 | ### Create a Private fork 🍴 60 | 61 | Let's create a private fork of this repository: 62 | 63 | - [Go to the Import a repository page on GitHub](https://github.com/new/import) 64 | - Fill in the information as shown below: 65 | 66 |

67 | How to create a private fork 68 |

How to create a private fork

69 |

70 | 71 | Once the repository is created on GitHub, clone it onto your local system! 72 | 73 | ### Install Python `v3.9` or higher 🐍 74 | 75 | - You can download it from the [Python website](https://www.python.org/downloads/release/python-3105/) 76 | - Or, you can install [Anaconda](https://www.anaconda.com/) and run the following command in your terminal: 77 | 78 | ```bash 79 | conda create --name dotlas python=3.9 -y 80 | conda activate dotlas 81 | ``` 82 | 83 | ### Install dependencies 📦 84 | 85 | ```bash 86 | python -m pip install -r requirements 87 | ``` 88 | 89 | > You may install any additional dependencies! 90 | 91 | ### Get crackin' ⌛️ 92 | 93 | Your odyssey's details are displayed in [odyssey.ipynb](./odyssey.ipynb). 94 | 95 | ## Submission 🏝 96 | 97 | Once you're ready for evaluation, [invite us](#contact) as [private collaborators to your private fork](https://docs.github.com/en/account-and-profile/setting-up-and-managing-your-personal-account-on-github/managing-access-to-your-personal-repositories/inviting-collaborators-to-a-personal-repository)! 98 | 99 | ## Grading Criteria 📝 100 | 101 | ### Problem Solving 🕵🏼‍♀️ 102 | 103 | - **Investigative**: Demonstrate curiosity by deeply exploring the data, uncovering hidden patterns, and asking insightful questions. 104 | - **Detail-Oriented**: Be meticulous in cleaning & transforming the data as well as coding the solution. 105 | - **Critical Thinking**: Question assumptions, consider biases, and weigh the implications of different problem-solving approaches. 106 | - **Creativity**: Develop novel approaches for data transformation, feature engineering, and pipeline design. 107 | - **Optimization**: Optimize code and the pipeline for performance, efficiency, and scalability. This includes speeding up the code, reducing memory usage, and adapting the pipeline for larger datasets. 108 | 109 | ### Data 📇 110 | 111 | - **Quality**: Check for duplicates, errors, missing values, and type mismatches. Assess each field's quality to ensure the dataset accurately reflects the real world. 112 | - **Completeness**: Make sure there are no missing values and that all rows and columns contain meaningful data. 113 | - **Feature Engineering**: Creatively generate new, relevant features from the existing data. This may involve combining features, creating derived features, or extracting new information. 114 | - **Preprocessing**: Properly normalize or standardize the features to prepare the dataset for machine learning models. 115 | 116 | ### Pipeline ⛳️ 117 | 118 | - **Edge-Case Handling**: Ensure your pipeline can handle a new but similar dataset with the same schema and doesn't fail on edge cases. 119 | - **Modularity**: Use short code cells and wrap commonly used code into reusable functions. 120 | - **No Artifacts**: The pipeline should run end-to-end, transforming the input data into the required output if the notebook is re-run. There shouldn't be any ad-hoc functions or code blocks. 121 | - **Generic Solutions**: Solve problems and code solutions for general cases, not specific ones. 122 | - **Use of Libraries**: Appropriate use of external libraries and Python packages. Do not reinvent the wheel unnecessarily, but also do not use a library for a simple task that can be accomplished easily with built-in Python functions. 123 | 124 | ### Code 🧼 125 | 126 | - **Formatting**: Ensure the code is neatly formatted according to Python's style guides. 127 | - **Pythonic Principles**: Use Python’s features and idioms correctly. For example, use list comprehensions and generators where appropriate. 128 | - **Error Handling**: Handle errors and exceptions gracefully to prevent crashes or incorrect results. 129 | - **Avoid Hardcoding**: Don't hardcode values or use inflexible code styles. This is related to error handling. 130 | - **Documentation**: Document the code clearly and concisely using docstrings. Avoid in-line comments. Use Markdown cells in the notebook for context at key points. Don't create a Markdown cell for every code cell. 131 | - **Vectorize**: Avoid tedious row-wise iterations of large tables and instead use vectorized functions. 132 | 133 | ## Contact Us 💬 134 | 135 | Feel free to reach out to us, should you have any questions. 136 | 137 | | Name | LinkedIn | GitHub | 138 | | :-------------- | :------------------------------------- | :-------------------------------- | 139 | | Eshwaran Venkat | | | 140 | | Kelvin DeCosta | | | 141 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Attribution 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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For 392 | the avoidance of doubt, this paragraph does not form part of the public 393 | licenses. 394 | 395 | Creative Commons may be contacted at creativecommons.org. 396 | -------------------------------------------------------------------------------- /odyssey.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "application/vnd.databricks.v1+cell": { 7 | "cellMetadata": {}, 8 | "inputWidgets": {}, 9 | "nuid": "a70fb4f4-a35c-46a2-8388-ff1fbc466139", 10 | "showTitle": false, 11 | "title": "" 12 | } 13 | }, 14 | "source": [ 15 | "# Dotlas Odyssey 🏞 [40 points]\n", 16 | "\n", 17 | "> Data Engineering Assignment\n", 18 | "\n", 19 | "> `v1.0` Updated: Sep 2 2023\n", 20 | "\n", 21 | "Greetings Traveller,\n", 22 | "\n", 23 | "You have embarked on a mighty Odyssey, a journey filled with challenges, mysteries, and opportunities for growth. Your mission, should you choose to accept it, will involve navigating uncharted territories, solving complex problems, and contributing to a mission that will redefine our future. We believe that with your skills, passion, and determination, you can contribute significantly to this mission.\n", 24 | "\n", 25 | "In this notebook, you will find a series of assignments designed to test your skills, stretch your capabilities, and ultimately, prepare you for the journey ahead. Each task is a stepping stone towards your ultimate goal, and completing them will not only bring you closer to becoming a part of the Dotlas family but also to making a meaningful impact on the world.\n", 26 | "\n", 27 | "Remember, the journey of a thousand miles begins with a single step. So, gear up, stay positive, and embrace the adventure that awaits you.\n", 28 | "\n", 29 | "---\n", 30 | "\"dotlas\"\n", 31 | "\n", 32 | "> You are welcome to make your solution for the Dotlas Odyssey public as open-source and add it to your portfolio of projects, but only after all candidates have completed the assignment, or the position has been filled. Sharing your solution publicly while the assignment process is ongoing will result in disqualification from the selection process." 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": { 38 | "application/vnd.databricks.v1+cell": { 39 | "cellMetadata": {}, 40 | "inputWidgets": {}, 41 | "nuid": "185e6bb9-9782-4ba4-8f7c-1d69385f7c6b", 42 | "showTitle": false, 43 | "title": "" 44 | } 45 | }, 46 | "source": [ 47 | "## Tools & Technologies 🪛\n", 48 | "\n", 49 | "![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)\n", 50 | "![Anaconda](https://img.shields.io/badge/Anaconda-%2344A833.svg?style=for-the-badge&logo=anaconda&logoColor=white)\n", 51 | "![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)\n", 52 | "![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white)\n", 53 | "![Spark](https://img.shields.io/badge/Apache%20Spark-E25A1C.svg?style=for-the-badge&logo=Apache-Spark&logoColor=white)\n", 54 | "![Matplotlib](https://img.shields.io/badge/Matplotlib-%23ffffff.svg?style=for-the-badge&logo=Matplotlib&logoColor=black)\n", 55 | "![Numpy](https://img.shields.io/badge/NumPy-013243.svg?style=for-the-badge&logo=NumPy&logoColor=white)\n", 56 | "![Plotly](https://img.shields.io/badge/Plotly-%233F4F75.svg?style=for-the-badge&logo=plotly&logoColor=white)\n", 57 | "![AWS](https://img.shields.io/badge/AWS-%23FF9900.svg?style=for-the-badge&logo=amazon-aws&logoColor=white)\n", 58 | "\n", 59 | "- This exercise will be carried out using the [Python](https://www.python.org/) programming language and will rely hevily on the [Pandas](https://pandas.pydata.org/) library for data manipulation.\n", 60 | "- You are also free to use [Polars](https://www.pola.rs/), [Dask](https://www.dask.org/) or [Spark](https://spark.apache.org/docs/latest/api/python/index.html) if you do not want to use Pandas.\n", 61 | "- You may use any of [Matplotlib](https://matplotlib.org/), [Seaborn](https://seaborn.pydata.org/) or [Plotly](https://plotly.com/python/) packages for data visualization.\n", 62 | "- We will be using [Jupyter notebooks](https://jupyter.org/) to run Python code in order to view and interact better with our data and visualizations.\n", 63 | "- You are free to use [Google Colab](https://colab.research.google.com/) which provides an easy-to-use Jupyter interface.\n", 64 | "- When not in Colab, it is recommended to run this Jupyter Notebook within an [Anaconda](https://continuum.io/) environment\n", 65 | "- You can use any other Python packages that you deem fit for this project.\n", 66 | "\n", 67 | "> ⚠ **Ensure that your Python version is 3.9 or higher**\n", 68 | "\n", 69 | "![](https://upload.wikimedia.org/wikipedia/commons/1/1b/Blue_Python_3.9_Shield_Badge.svg)" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 0, 75 | "metadata": { 76 | "application/vnd.databricks.v1+cell": { 77 | "cellMetadata": {}, 78 | "inputWidgets": {}, 79 | "nuid": "3a1cd966-d7cc-45d9-b6be-a73e14d97f28", 80 | "showTitle": false, 81 | "title": "" 82 | } 83 | }, 84 | "outputs": [], 85 | "source": [ 86 | "# YOUR CODE HERE\n", 87 | "# Import necessary libraries" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": { 93 | "application/vnd.databricks.v1+cell": { 94 | "cellMetadata": {}, 95 | "inputWidgets": {}, 96 | "nuid": "f93a1493-4f91-44eb-8fea-a58dd9862ed5", 97 | "showTitle": false, 98 | "title": "" 99 | } 100 | }, 101 | "source": [ 102 | "## Section 1: Extract 🚰 [5]\n", 103 | "\n", 104 | "\"extract\"\n", 105 | "\n", 106 | "You have to extract 4 datasets. Each dataset contains partial information about **restaurants in the San Francisco Bay Area, California, US**. The datasets are as follows:\n", 107 | "1. **Delta**: A pipe delimited csv file that contains restaurants that offer food-delivery\n", 108 | "2. **Oscar**: A raw binary file (pickled) that contains restaurants that take reservations, and other high-end places\n", 109 | "3. **Tango**: A json file that contains all kinds of restaurants\n", 110 | "2. **Yankee**: A parquet file that contains crowdsourced restaurant data\n", 111 | "\n", 112 | "You also have one additional dataset called **Lookup Record** that contains the pre-defined mapping of restaurants between various rows of Delta through Yankee. Each row in the Lookup table can be considered as a single restaurant and can be assigned to one or more sources based on whether or not they exist for that restaurant. The Lookup records may have imperfect matches between sources, but you can ignore this quality issue and instead consider it as a source of truth for the amalgamation stage.\n", 113 | "\n", 114 | "\n", 115 | "### License 📜\n", 116 | "\n", 117 | "The data in this assignment is provided by Dotlas and is available for non-commercial use, such as research previews, and open-source projects. For any commercial use of the data, you must request access and provide all necessary context to [Dotlas](https://www.dotlas.com). Unauthorized commercial use of the data is prohibited. For more details, refer to the `DATA_LICENSE` file in the GitHub repository." 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 0, 123 | "metadata": { 124 | "application/vnd.databricks.v1+cell": { 125 | "cellMetadata": { 126 | "byteLimit": 2048000, 127 | "rowLimit": 10000 128 | }, 129 | "inputWidgets": {}, 130 | "nuid": "05fd1db4-98d8-4bd6-991e-8a9b5ad7e3db", 131 | "showTitle": false, 132 | "title": "" 133 | } 134 | }, 135 | "outputs": [], 136 | "source": [ 137 | "delta_url: str = (\n", 138 | " \"https://dotlas-marketing.s3.amazonaws.com/interviews/california_delta_dataset.csv\"\n", 139 | ")\n", 140 | "oscar_url: str = (\n", 141 | " \"https://dotlas-marketing.s3.amazonaws.com/interviews/california_oscar_dataset.pkl\"\n", 142 | ")\n", 143 | "tango_url: str = (\n", 144 | " \"https://dotlas-marketing.s3.amazonaws.com/interviews/california_tango_dataset.json\"\n", 145 | ")\n", 146 | "yankee_url: str = (\n", 147 | " \"https://dotlas-marketing.s3.amazonaws.com/interviews/california_yankee_dataset.parquet\"\n", 148 | ")\n", 149 | "\n", 150 | "lookup_record: str = (\n", 151 | " \"https://dotlas-marketing.s3.amazonaws.com/interviews/california_master_record.csv\"\n", 152 | ")" 153 | ] 154 | }, 155 | { 156 | "cell_type": "markdown", 157 | "metadata": { 158 | "application/vnd.databricks.v1+cell": { 159 | "cellMetadata": {}, 160 | "inputWidgets": {}, 161 | "nuid": "27433a9e-42d7-431e-91ef-f986f85acb4f", 162 | "showTitle": false, 163 | "title": "" 164 | } 165 | }, 166 | "source": [ 167 | "### 1.1 Read Data 🔀 [2]\n", 168 | "\n", 169 | "Load each source data file into separate DataFrames. Do not download the files locally to the file system but instead read them directly in-memory." 170 | ] 171 | }, 172 | { 173 | "cell_type": "markdown", 174 | "metadata": { 175 | "application/vnd.databricks.v1+cell": { 176 | "cellMetadata": {}, 177 | "inputWidgets": {}, 178 | "nuid": "2ff09fae-1323-439a-8c9b-817dac8cbdc3", 179 | "showTitle": false, 180 | "title": "" 181 | } 182 | }, 183 | "source": [ 184 | "### 1.2 Exploratory Data Analysis 📊 [3]\n", 185 | "\n", 186 | "Perform a simple exploratory data analysis of each dataset to understand its structure, null values, nature of the data, data types, duplicate analysis, data quality and more." 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": 0, 192 | "metadata": { 193 | "application/vnd.databricks.v1+cell": { 194 | "cellMetadata": { 195 | "byteLimit": 2048000, 196 | "rowLimit": 10000 197 | }, 198 | "inputWidgets": {}, 199 | "nuid": "c666aa55-88c5-4535-bc59-db736546ba66", 200 | "showTitle": false, 201 | "title": "" 202 | } 203 | }, 204 | "outputs": [], 205 | "source": [ 206 | "# Your code here" 207 | ] 208 | }, 209 | { 210 | "cell_type": "markdown", 211 | "metadata": { 212 | "application/vnd.databricks.v1+cell": { 213 | "cellMetadata": {}, 214 | "inputWidgets": {}, 215 | "nuid": "1bb3df62-e5e1-42ba-92b2-ae527c1d19fe", 216 | "showTitle": false, 217 | "title": "" 218 | } 219 | }, 220 | "source": [ 221 | "## Section 2: Join and Knit 🧵 [15] \n", 222 | "\n", 223 | "\"merge\"\n", 224 | "

\n", 225 | "\n", 226 | "1. **Objective**: \n", 227 | " - Merge the datasets - Delta, Oscar, Tango, and Yankee into one final restaurant dataset.\n", 228 | " - Use the *Lookup Records* table for reference on joining the source tables.\n", 229 | "\n", 230 | "2. **Key Details**:\n", 231 | " - Datasets contain unique details (e.g., Dining Style, Meals Offered).\n", 232 | " - Some details (e.g., Name, Location) are shared across datasets.\n", 233 | " - Your goal: Merge to create `Table Z`.\n", 234 | "\n", 235 | "3. **How to Merge**:\n", 236 | " - Join tables Delta through Yankee using **all columns**.\n", 237 | " - Address overlapping columns to avoid duplication.\n", 238 | " - Merge columns with similar data but different names (like 'telephone_number' and 'phone_number').\n", 239 | " - If a column appears in multiple tables with varying values, select the most appropriate one (e.g., 'price_range' and 'price_class').\n", 240 | "\n", 241 | "### Example\n", 242 | "\n", 243 | "Consider the hypothetical tables A, B, C, and D. We aim to merge these into Table Z. \n", 244 | "\n", 245 | "Note: The example highlights specific columns from A to D, but you must consider all columns from datasets Delta to Yankee.\n", 246 | "\n", 247 | "**Table A**\n", 248 | "\n", 249 | "| id | restaurant_name | area | categories | rating | website | price_range | executive_chef |\n", 250 | "| --- | --- | --- | --- | --- | --- | --- | --- |\n", 251 | "| 1 | The Good Food | Downtown | Italian,Seafood | 4.5 | goodfood.com | $11-30 | John Doe |\n", 252 | "| 2 | Taste of Asia | Midtown | Asian,Thai | 4.0 | tasteofasia.com | $10-25 | Jane Doe |\n", 253 | "\n", 254 | "**Table B**\n", 255 | "\n", 256 | "| id | name | location | type | rating | website | price_class | meals_offered |\n", 257 | "| --- | --- | --- | --- | --- | --- | --- | --- |\n", 258 | "| 1 | The Good Food | Downtown | Italian | 4.5 | goodfood.com | 2 | Lunch,Dinner |\n", 259 | "| 3 | Spicy Hut | Uptown | Indian,Spicy | 4.2 | spicyhut.com | 3 | Lunch,Dinner,Brunch |\n", 260 | "\n", 261 | "**Table C**\n", 262 | "\n", 263 | "| id | restaurant | location | categories | telephone_number | price_range |\n", 264 | "| --- | --- | --- | --- | --- | --- |\n", 265 | "| 2 | Taste of Asia | Midtown | Asian,Thai | 1234567890 | $10-25 |\n", 266 | "| 4 | Fresh & Healthy | Suburbs | Vegetarian,Vegan | 9876543210 | $15-30 |\n", 267 | "\n", 268 | "**Table D**\n", 269 | "\n", 270 | "| id | restaurant_name | area | type | phone_number | website | executive_chef |\n", 271 | "| --- | --- | --- | --- | --- | --- | --- |\n", 272 | "| 3 | Spicy Hut | Uptown | Indian | 1234567890 | spicyhut.com | Raj Patel |\n", 273 | "| 4 | Fresh & Healthy | Suburbs | Vegetarian | 9876543210 | freshandhealthy.com | Emily Brown |\n", 274 | "\n", 275 | "We want to merge tables A, B, C, and D into a single table Z. However, we need to handle the overlapping and exclusive columns carefully to avoid duplication and to ensure a single source of truth. We will use coalescing to handle overlapping columns with different names (e.g., 'telephone_number' and 'phone_number') and combine values when necessary (e.g., 'categories' and 'type'). We will also choose the most appropriate value for columns that appear in multiple tables but have different values (e.g., 'price_range' and 'price_class').\n", 276 | "\n", 277 | "**Table Z**\n", 278 | "\n", 279 | "| id | name | location | categories | rating | website | price_range | executive_chef | meals_offered | telephone_number |\n", 280 | "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", 281 | "| 1 | The Good Food | Downtown | Italian,Seafood | 4.5 | goodfood.com | $11-30 | John Doe | Lunch,Dinner | |\n", 282 | "| 2 | Taste of Asia | Midtown | Asian,Thai | 4.0 | tasteofasia.com | $10-25 | Jane Doe | | 1234567890 |\n", 283 | "| 3 | Spicy Hut | Uptown | Indian,Spicy | 4.2 | spicyhut.com | $25-40 | Raj Patel | Lunch,Dinner,Brunch | 1234567890 |\n", 284 | "| 4 | Fresh & Healthy | Suburbs | Vegetarian,Vegan | | freshandhealthy.com | $15-30 | Emily Brown | | 9876543210 |\n", 285 | "\n", 286 | "\n", 287 | "> 📝 It's important to note that while this example is straightforward, you may encounter some ambiguity when working with the actual datasets Delta through Yankee. **The main objective is to construct a dataset that provides a comprehensive and detailed context for each restaurant**. There will be instances where you'll need to make subjective decisions, such as prioritizing one dataset over another or creating a new field to harmonize different representations of the same data across datasets (e.g., your own classification of price range). These decisions are up to you, and we encourage you to develop your own approach and rationale for resolving these issues. Just make sure to explain your choices and reasoning in a markdown cell.\n", 288 | "\n", 289 | "---\n", 290 | "\n", 291 | "### Tips 💡 \n", 292 | "- Remember to clean up columns in the 4 source datasets where necessary that would make the join more seamless, instead of retrofitting ad-hoc fixes after the fact.\n", 293 | "- If you have two or more columns with same or similar names or two or more different columns that are representing the same kind of data - then you have not reached the final output\n", 294 | "- All values in a single column should have the same data type. A type can be simple like `integer`, `float`, `string`, or complex like `list[str]` or `list[int]`. Try to avoid columns that result in having `json` or `dict` values.\n", 295 | "- The underlying four datasets and lookup records may have duplicates, so watch out for bugs in the join" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": 0, 301 | "metadata": { 302 | "application/vnd.databricks.v1+cell": { 303 | "cellMetadata": {}, 304 | "inputWidgets": {}, 305 | "nuid": "7fe0ed02-a238-4884-9ac4-69cac62805c3", 306 | "showTitle": false, 307 | "title": "" 308 | } 309 | }, 310 | "outputs": [], 311 | "source": [ 312 | "# YOUR CODE HERE" 313 | ] 314 | }, 315 | { 316 | "cell_type": "markdown", 317 | "metadata": { 318 | "application/vnd.databricks.v1+cell": { 319 | "cellMetadata": {}, 320 | "inputWidgets": {}, 321 | "nuid": "c6311a13-fd83-4ec1-86ea-a241ce1a0419", 322 | "showTitle": false, 323 | "title": "" 324 | } 325 | }, 326 | "source": [ 327 | "## Section 3: Web Harvesting 🕸 [15]\n", 328 | "\n", 329 | "\"merge\"\n", 330 | "\n", 331 | "In this section, your task is to enhance the current dataset by navigating to the restaurant websites specified in the data, retrieving any incomplete information, and collecting additional features that could be essential for the analysis. This will involve accessing the URLs of restaurant websites listed in the dataset, and potentially exploring linked pages from the initial page to gather required information. The depth of pages you explore is at your discretion, as long as the added information substantially benefits the final dataset.\n", 332 | "\n", 333 | "Tasks include:\n", 334 | "\n", 335 | "- **Filling Missing Data**: Identify and fill in missing values in the dataset, such as restaurant descriptions, operating hours, and contact information.\n", 336 | "- **Extracting New Features**: Extract additional valuable information from the restaurant websites, such as:\n", 337 | " - **Social Media Handles**: Links to Facebook, Twitter, Instagram, LinkedIn pages.\n", 338 | " - **Awards and Recognitions**: Any awards or recognitions received by the restaurant.\n", 339 | " - **Special Events or Offers**: Information on any special events, promotions, or discounts.\n", 340 | " - Anything else that you feel may be relevant and valuable\n", 341 | "\n", 342 | "Be respectful of the website's `robots.txt` file and terms of service, and ensure your web scraping script is efficient, accurate, and does not overload the servers. You will be graded on the efficiency and accuracy of your script, as well as the completeness and usefulness of the extracted data. Ensure to update the dataset without altering its existing structure and content.\n", 343 | "\n", 344 | "> You may need to additionally join and clean up the resultant dataset" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": 0, 350 | "metadata": { 351 | "application/vnd.databricks.v1+cell": { 352 | "cellMetadata": {}, 353 | "inputWidgets": {}, 354 | "nuid": "f431ae1a-d68b-4f2a-9f47-6615079a9f6e", 355 | "showTitle": false, 356 | "title": "" 357 | } 358 | }, 359 | "outputs": [], 360 | "source": [ 361 | "# YOUR CODE HERE" 362 | ] 363 | }, 364 | { 365 | "cell_type": "markdown", 366 | "metadata": { 367 | "application/vnd.databricks.v1+cell": { 368 | "cellMetadata": {}, 369 | "inputWidgets": {}, 370 | "nuid": "823e734d-70e3-4270-88df-2cf86471dac7", 371 | "showTitle": false, 372 | "title": "" 373 | } 374 | }, 375 | "source": [ 376 | "## Section 4: Data Quality Checks ✅ [5]\n", 377 | "\n", 378 | "\"merge\"\n", 379 | "\n", 380 | "In this section, you are expected to devise and execute a series of data quality tests on the dataframe. Your objective is to identify and address potential edge cases that could affect the downstream use of the dataset. You may use [Great Expectations](https://greatexpectations.io/) or any other tool of your preference to run the tests and profile the data.\n", 381 | "\n", 382 | "\n", 383 | "- **Test Design**: Outline the data quality tests you plan to run. Explain why you have chosen these tests and what potential issues they could uncover.\n", 384 | "- **Test Execution**: Execute the designed tests on the dataframe. Document the results of each test, including any discrepancies or anomalies identified.\n", 385 | "- **Data Profiling**: Perform a thorough profiling of the data to understand its characteristics and quality. This may include understanding the distribution of different features, identifying outliers, or assessing the completeness and uniqueness of the data.\n", 386 | "- **Edge Case Identification**: Discuss any edge cases you have identified during the testing and profiling process. Explain how these edge cases could affect the downstream use of the dataset and propose potential solutions to address them.\n", 387 | "\n", 388 | "> 📝 Your ability to design comprehensive tests, identify and address edge cases, and thoroughly profile the data will be crucial in this section. Be sure to document your process and findings clearly and concisely" 389 | ] 390 | }, 391 | { 392 | "cell_type": "code", 393 | "execution_count": 0, 394 | "metadata": { 395 | "application/vnd.databricks.v1+cell": { 396 | "cellMetadata": {}, 397 | "inputWidgets": {}, 398 | "nuid": "e7820a32-6392-4d72-ad17-fcc4f3ac54de", 399 | "showTitle": false, 400 | "title": "" 401 | } 402 | }, 403 | "outputs": [], 404 | "source": [ 405 | "# YOUR CODE HERE" 406 | ] 407 | }, 408 | { 409 | "cell_type": "markdown", 410 | "metadata": { 411 | "application/vnd.databricks.v1+cell": { 412 | "cellMetadata": {}, 413 | "inputWidgets": {}, 414 | "nuid": "80efd06e-1048-40f1-a01c-3c09f3b5ea73", 415 | "showTitle": false, 416 | "title": "" 417 | } 418 | }, 419 | "source": [ 420 | "Save your results in a parquet file and upload them to your private fork of the GitHub Repository." 421 | ] 422 | } 423 | ], 424 | "metadata": { 425 | "application/vnd.databricks.v1+notebook": { 426 | "dashboards": [], 427 | "language": "python", 428 | "notebookMetadata": { 429 | "pythonIndentUnit": 4 430 | }, 431 | "notebookName": "odyssey", 432 | "widgets": {} 433 | }, 434 | "language_info": { 435 | "name": "python" 436 | } 437 | }, 438 | "nbformat": 4, 439 | "nbformat_minor": 0 440 | } 441 | --------------------------------------------------------------------------------