{ "source_file": "0930", "source_platform": "Medium", "author": "Ibrahim Salami", "title_listed": "I Built My First ETL Pipeline as a Complete Beginner. Here's How.", "keywords": "", "domain": "数据处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/i-built-my-first-etl-pipeline-as-a-complete-beginner-heres-exactly-how/", "publish_date": "2026-05-25", "corpus_stage": "", "notes": "", "id": "74557aed852f", "collected_at": "2026-10-02T11:36:30", "title": "I Built My First ETL Pipeline as a Complete Beginner. Here’s How. | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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Here’s How.\nA beginner's honest walkthrough of Extract, Transform, Load using the GitHub API\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nMay 25, 20266 min read\n![](../images/000.jpg)\nGenerated with Gemini\n_This is part two of my data engineering journey series. In part one, I shared my_[ _12-month roadmap for transitioning from data analyst to data engineer_](https://towardsdatascience.com/from-data-analyst-to-data-engineer-my-12-month-self-study-roadmap/) _. This is where the actual building begins._ \n \nWhen I published my first article documenting my data engineering journey, something unexpected happened. People resonated with it. I had strangers reaching out saying they were excited to follow along. That felt good.\nBut it also came with pressure.\nSuddenly this wasn't just a personal goal I could quietly abandon if things got hard. People were watching. People were in the same boat. And that accountability, honestly, is part of why you're reading this right now.\nSo I had to move. And like anyone starting a new skill, the first thing I did was look for resources. There are countless tutorials on the internet for data engineering. YouTube videos, courses, written guides. More than you could ever finish.\nBut I couldn't bring myself to just consume theory. I needed to build something. Something real, with real data, that actually worked at the end.\nSo I closed the tutorials and opened a Google Colab notebook instead. I found the GitHub API documentation and decided I was going to build my first ETL pipeline from scratch. No hand-holding. Just me, some Python, and a goal.\nThis article is that experience documented in full. The code, the confusion, the small wins, and what I actually learned by doing it.\nLearn this step by step with the interactive [Data Engineer](https://roadmap.sh/data-engineer) roadmap.\n## First, what is ETL?\nBefore I get into what I built, let me quickly explain what ETL actually means because I had to look this up myself not too long ago.\nETL stands for Extract, Transform, Load. It's one of the most fundamental concepts in data engineering.\n * **Extract** means going somewhere to get data. An API, a database, a website, a file. You're pulling raw information from a source.\n * **Transform** means cleaning and shaping that data. Removing bad rows, adding new columns, restructuring it so it's actually useful.\n * **Load** means saving the cleaned data somewhere. A database, a data warehouse, a simple CSV file.\n\n\nThat's it. Those three steps, done in sequence, are what a data pipeline is. Everything else in data engineering, Airflow, Spark, Databricks, is just more sophisticated ways of doing those same three things at scale.\nI'm at the beginning of my roadmap, so I kept it simple. Pure Python, no orchestration tools yet. But the shape of the problem is the same.\n## What I built\nI extracted data from the GitHub API, specifically the most starred Python repositories created in the last 30 days. I then cleaned it, added a new column, and saved the output as a CSV file. \nSimple. Real. Entirely mine.\nHere's how it went.\n## Step 1: Extract\nThe first thing I had to do was figure out how to talk to the GitHub API. An API is basically a door that a company or platform opens so that developers can request data from it programmatically, without having to manually copy and paste anything.\nGitHub has a free, public API. No account or paid plan needed for basic searches.\nHere's the code I wrote to extract the data:\npython\n\n```\nimport requests\n\nurl = \"https://api.github.com/search/repositories\"\n\nparams = {\n \"q\": \"language:python created:>2025-04-22\",\n \"sort\": \"stars\",\n \"order\": \"desc\",\n \"per_page\": 30\n}\n\nresponse = requests.get(url, params=params)\ndata = response.json()\n\nprint(response.status_code)\nprint(data.keys())\n```\n\nI'll be honest. This block confused me at first. The `requests` library was new to me. The `params` dictionary with that `q` syntax felt alien. I didn't immediately know what `.json()` was doing or why I needed it.\nLet me break it down simply.\n * `requests.get()` is how you knock on GitHub's door and ask for something. The `url` is the address of what you're asking for. The\n * `params` dictionary is the specific question you're asking. In this case: \"give me Python repos, sorted by stars, created after April 22, show me 30 results.\"\n * `.json()` converts GitHub's response from raw text into a Python dictionary that you can actually work with.\n\n\nWhen I ran it, I got this:\npascal\n\n```\n200 \ndict_keys(['total_count', 'incomplete_results', 'items'])\n```\n\nThe `200` means success. That's the internet's way of saying \"your request worked.\" If you see 403 or 404, something went wrong. \nThe dictionary has three keys. `total_count` tells you how many repos matched the search. `incomplete_results` tells you if GitHub had to cut anything short. And `items` is where the actual data lives.\nI then ran a second block to peek inside:\npython\n\n```\nprint(\"Total matches on GitHub:\", data['total_count'])\nprint(\"Repos returned:\", len(data['items']))\n\nfirst_repo = data['items'][0]\nprint(\"\\nFirst repo name:\", first_repo['name'])\nprint(\"Stars:\", first_repo['stargazers_count'])\nprint(\"Language:\", first_repo['language'])\nprint(\"URL:\", first_repo['html_url'])\n```\n\nOutput:\nmarkup\n\n```\nTotal matches on GitHub: 9228201Repos returned: 30First repo name: skillsStars: 139136Language: PythonURL: https://github.com/anthropics/skills\n```\n\nThe first result was an Anthropic repo with 139k stars. Real data. Live. Pulled by code I wrote.\nThat's Extract done.\n## Step 2: Transform\nNow I had 30 repos sitting in a Python list, each one a nested dictionary with dozens of fields. Most of which I didn't need. The Transform step is where you take that raw, messy data and shape it into something clean and purposeful.\nFirst I pulled out only the fields I cared about and loaded them into a Pandas dataframe:\npython\n\n```\nimport pandas as pd\n\nrepos = []\n\nfor repo in data['items']:\n repos.append({\n \"name\": repo['name'],\n \"owner\": repo['owner']['login'],\n \"stars\": repo['stargazers_count'],\n \"forks\": repo['forks_count'],\n \"language\": repo['language'],\n \"description\": repo['description'],\n \"url\": repo['html_url'],\n \"created_at\": repo['created_at']\n })\n\ndf = pd.DataFrame(repos)\ndf.head()\n```\n\nSeeing that dataframe appear was a proper \"wow\" moment. I went from a wall of JSON to a clean, readable table with labelled columns in a few lines.\nThen I did three transformations:\npython\n\n```\n# Drop rows where description is missing\ndf_clean = df.dropna(subset=['description'])\n\n# Add a viral flag for repos with over 50k stars\ndf_clean = df_clean.copy()\ndf_clean['viral'] = df_clean['stars'].apply(lambda x: 'Yes' if x > 50000 else 'No')\n\n# Sort by stars descending\ndf_clean = df_clean.sort_values('stars', ascending=False).reset_index(drop=True)\n\nprint(\"Before cleaning:\", len(df))\nprint(\"After cleaning:\", len(df_clean))\n```\n\nOutput:\nmarkup\n\n```\nBefore cleaning: 30 After cleaning: 29\n```\n\nOne repo had no description and got dropped. The viral column showed up cleanly. The data was now sorted and structured. \nThat's Transform done.\n## Step 3: Load\nThe final step. Take the clean data and save it somewhere. I kept this simple and loaded it into a CSV file:\npython\n\n```\ndf_clean.to_csv('github_trending_repos.csv', index=False)\n\nprint(\"Pipeline complete. File saved.\")\nprint(f\"{len(df_clean)} repos loaded into github_trending_repos.csv\")\n```\n\nOutput:\nmarkup\n\n```\nPipeline complete. File saved.29 repos loaded into github_trending_repos.csv\n```\n\nI downloaded the file and opened it. A clean spreadsheet with 29 rows and 9 columns. Real GitHub data, shaped and saved by a pipeline I built from scratch.\nThat's Load done.\n## What this actually felt like\nBefore this, whenever I wanted data to work with, I'd go looking for a public dataset someone had already cleaned and uploaded. Kaggle, Google Dataset Search, wherever. I was always a consumer of data that someone else had prepared.\nThis changed something for me.\nThe moment I realised I could just point Python at an API I was curious about and extract live data myself, the possibilities felt completely different. I'm not limited to datasets that already exist. I can build the pipeline that creates the dataset.\nThat's a different kind of power. And it's one of the things that drew me toward data engineering in the first place.\n## What's next\nThis pipeline is simple by design. I'm at the start of my roadmap and I'm not going to pretend I'm using Airflow or Spark yet. But the foundation is real. Extract, Transform, Load. It works. I built it. I understand it.\nThe next step is to make it more robust. Schedule it to run daily. Store the output in a SQLite database instead of a flat CSV. Start tracking how repos trend over time.\nAnd eventually, orchestrate the whole thing with Airflow. But that's a future article.\nFor now, the most important thing I proved to myself is that building teaches you things that watching never will. I spent weeks in tutorial land and barely moved. I spent one afternoon actually building, and I understand ETL better than any video made it feel.\nStop watching. Start building. \n \n_This is part two of my ongoing data engineering series. Follow along as I document every step of the journey, including the parts that don't go smoothly._ _Feel free to check out my more in-depth ETL take on my YouTube channel below_.\n_Connect with me on_[ _LinkedIn_](https://www.linkedin.com/in/ibrahim-salami-059863228/) _,_[_YouTube_](https://www.youtube.com/@ibbysalam) _, and_[ _Twitter_](https://x.com/IbbySalam) _._\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Engineering](https://towardsdatascience.com/category/data-science/data-engineering/)\n# I Built My First ETL Pipeline as a Complete Beginner. Here’s How.\nA beginner's honest walkthrough of Extract, Transform, Load using the GitHub API\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nMay 25, 20266 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/etl-building.jpg)\nGenerated with Gemini\n_This is part two of my data engineering journey series. In part one, I shared my_[ _12-month roadmap for transitioning from data analyst to data engineer_](https://towardsdatascience.com/from-data-analyst-to-data-engineer-my-12-month-self-study-roadmap/) _. This is where the actual building begins._ \n \nWhen I published my first article documenting my data engineering journey, something unexpected happened. People resonated with it. I had strangers reaching out saying they were excited to follow along. That felt good.\nBut it also came with pressure.\nSuddenly this wasn't just a personal goal I could quietly abandon if things got hard. People were watching. People were in the same boat. And that accountability, honestly, is part of why you're reading this right now.\nSo I had to move. And like anyone starting a new skill, the first thing I did was look for resources. There are countless tutorials on the internet for data engineering. YouTube videos, courses, written guides. More than you could ever finish.\nBut I couldn't bring myself to just consume theory. I needed to build something. Something real, with real data, that actually worked at the end.\nSo I closed the tutorials and opened a Google Colab notebook instead. I found the GitHub API documentation and decided I was going to build my first ETL pipeline from scratch. No hand-holding. Just me, some Python, and a goal.\nThis article is that experience documented in full. The code, the confusion, the small wins, and what I actually learned by doing it.\nLearn this step by step with the interactive [Data Engineer](https://roadmap.sh/data-engineer) roadmap.\n## First, what is ETL?\nBefore I get into what I built, let me quickly explain what ETL actually means because I had to look this up myself not too long ago.\nETL stands for Extract, Transform, Load. It's one of the most fundamental concepts in data engineering.\n * **Extract** means going somewhere to get data. An API, a database, a website, a file. You're pulling raw information from a source.\n * **Transform** means cleaning and shaping that data. Removing bad rows, adding new columns, restructuring it so it's actually useful.\n * **Load** means saving the cleaned data somewhere. A database, a data warehouse, a simple CSV file.\n\n\nThat's it. Those three steps, done in sequence, are what a data pipeline is. Everything else in data engineering, Airflow, Spark, Databricks, is just more sophisticated ways of doing those same three things at scale.\nI'm at the beginning of my roadmap, so I kept it simple. Pure Python, no orchestration tools yet. But the shape of the problem is the same.\n## What I built\nI extracted data from the GitHub API, specifically the most starred Python repositories created in the last 30 days. I then cleaned it, added a new column, and saved the output as a CSV file. \nSimple. Real. Entirely mine.\nHere's how it went.\n## Step 1: Extract\nThe first thing I had to do was figure out how to talk to the GitHub API. An API is basically a door that a company or platform opens so that developers can request data from it programmatically, without having to manually copy and paste anything.\nGitHub has a free, public API. No account or paid plan needed for basic searches.\nHere's the code I wrote to extract the data:\npython\n\n```\nimport requests\n\nurl = \"https://api.github.com/search/repositories\"\n\nparams = {\n \"q\": \"language:python created:>2025-04-22\",\n \"sort\": \"stars\",\n \"order\": \"desc\",\n \"per_page\": 30\n}\n\nresponse = requests.get(url, params=params)\ndata = response.json()\n\nprint(response.status_code)\nprint(data.keys())\n```\n\nI'll be honest. This block confused me at first. The `requests` library was new to me. The `params` dictionary with that `q` syntax felt alien. I didn't immediately know what `.json()` was doing or why I needed it.\nLet me break it down simply.\n * `requests.get()` is how you knock on GitHub's door and ask for something. The `url` is the address of what you're asking for. The\n * `params` dictionary is the specific question you're asking. In this case: \"give me Python repos, sorted by stars, created after April 22, show me 30 results.\"\n * `.json()` converts GitHub's response from raw text into a Python dictionary that you can actually work with.\n\n\nWhen I ran it, I got this:\npascal\n\n```\n200 \ndict_keys(['total_count', 'incomplete_results', 'items'])\n```\n\nThe `200` means success. That's the internet's way of saying \"your request worked.\" If you see 403 or 404, something went wrong. \nThe dictionary has three keys. `total_count` tells you how many repos matched the search. `incomplete_results` tells you if GitHub had to cut anything short. And `items` is where the actual data lives.\nI then ran a second block to peek inside:\npython\n\n```\nprint(\"Total matches on GitHub:\", data['total_count'])\nprint(\"Repos returned:\", len(data['items']))\n\nfirst_repo = data['items'][0]\nprint(\"\\nFirst repo name:\", first_repo['name'])\nprint(\"Stars:\", first_repo['stargazers_count'])\nprint(\"Language:\", first_repo['language'])\nprint(\"URL:\", first_repo['html_url'])\n```\n\nOutput:\nmarkup\n\n```\nTotal matches on GitHub: 9228201Repos returned: 30First repo name: skillsStars: 139136Language: PythonURL: https://github.com/anthropics/skills\n```\n\nThe first result was an Anthropic repo with 139k stars. Real data. Live. Pulled by code I wrote.\nThat's Extract done.\n## Step 2: Transform\nNow I had 30 repos sitting in a Python list, each one a nested dictionary with dozens of fields. Most of which I didn't need. The Transform step is where you take that raw, messy data and shape it into something clean and purposeful.\nFirst I pulled out only the fields I cared about and loaded them into a Pandas dataframe:\npython\n\n```\nimport pandas as pd\n\nrepos = []\n\nfor repo in data['items']:\n repos.append({\n \"name\": repo['name'],\n \"owner\": repo['owner']['login'],\n \"stars\": repo['stargazers_count'],\n \"forks\": repo['forks_count'],\n \"language\": repo['language'],\n \"description\": repo['description'],\n \"url\": repo['html_url'],\n \"created_at\": repo['created_at']\n })\n\ndf = pd.DataFrame(repos)\ndf.head()\n```\n\nSeeing that dataframe appear was a proper \"wow\" moment. I went from a wall of JSON to a clean, readable table with labelled columns in a few lines.\nThen I did three transformations:\npython\n\n```\n# Drop rows where description is missing\ndf_clean = df.dropna(subset=['description'])\n\n# Add a viral flag for repos with over 50k stars\ndf_clean = df_clean.copy()\ndf_clean['viral'] = df_clean['stars'].apply(lambda x: 'Yes' if x > 50000 else 'No')\n\n# Sort by stars descending\ndf_clean = df_clean.sort_values('stars', ascending=False).reset_index(drop=True)\n\nprint(\"Before cleaning:\", len(df))\nprint(\"After cleaning:\", len(df_clean))\n```\n\nOutput:\nmarkup\n\n```\nBefore cleaning: 30 After cleaning: 29\n```\n\nOne repo had no description and got dropped. The viral column showed up cleanly. The data was now sorted and structured. \nThat's Transform done.\n## Step 3: Load\nThe final step. Take the clean data and save it somewhere. I kept this simple and loaded it into a CSV file:\npython\n\n```\ndf_clean.to_csv('github_trending_repos.csv', index=False)\n\nprint(\"Pipeline complete. File saved.\")\nprint(f\"{len(df_clean)} repos loaded into github_trending_repos.csv\")\n```\n\nOutput:\nmarkup\n\n```\nPipeline complete. File saved.29 repos loaded into github_trending_repos.csv\n```\n\nI downloaded the file and opened it. A clean spreadsheet with 29 rows and 9 columns. Real GitHub data, shaped and saved by a pipeline I built from scratch.\nThat's Load done.\n## What this actually felt like\nBefore this, whenever I wanted data to work with, I'd go looking for a public dataset someone had already cleaned and uploaded. Kaggle, Google Dataset Search, wherever. I was always a consumer of data that someone else had prepared.\nThis changed something for me.\nThe moment I realised I could just point Python at an API I was curious about and extract live data myself, the possibilities felt completely different. I'm not limited to datasets that already exist. I can build the pipeline that creates the dataset.\nThat's a different kind of power. And it's one of the things that drew me toward data engineering in the first place.\n## What's next\nThis pipeline is simple by design. I'm at the start of my roadmap and I'm not going to pretend I'm using Airflow or Spark yet. But the foundation is real. Extract, Transform, Load. It works. I built it. I understand it.\nThe next step is to make it more robust. Schedule it to run daily. Store the output in a SQLite database instead of a flat CSV. Start tracking how repos trend over time.\nAnd eventually, orchestrate the whole thing with Airflow. But that's a future article.\nFor now, the most important thing I proved to myself is that building teaches you things that watching never will. I spent weeks in tutorial land and barely moved. I spent one afternoon actually building, and I understand ETL better than any video made it feel.\nStop watching. Start building. \n \n_This is part two of my ongoing data engineering series. Follow along as I document every step of the journey, including the parts that don't go smoothly._ _Feel free to check out my more in-depth ETL take on my YouTube channel below_.\n_Connect with me on_[ _LinkedIn_](https://www.linkedin.com/in/ibrahim-salami-059863228/) _,_[_YouTube_](https://www.youtube.com/@ibbysalam) _, and_[ _Twitter_](https://x.com/IbbySalam) _._\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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