{ "source_file": "0930", "source_platform": "Medium", "author": "Ibrahim Salami", "title_listed": "I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer", "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-second-etl-pipeline-this-time-i-started-thinking-like-a-data-engineer/", "publish_date": "2026-07-10", "corpus_stage": "", "notes": "", "id": "95bd5b7aee97", "collected_at": "2026-10-02T11:28:37", "title": "I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer | 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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This Time, I Started Thinking Like a Data Engineer\nBuilding a production-ready RSS pipeline with Python, Docker, PostgreSQL, and Kestra\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nJuly 10, 202615 min read\n![](../images/000.jpg)\nArchitectural Diagram Generated by AI\nA few months ago, I decided I wanted to transition from data analyst to data engineer.\nLike many people starting out, I was overwhelmed by the sheer number of things I thought I needed to learn. Data warehouses, orchestration tools, distributed processing, streaming systems, cloud platforms, infrastructure. The list seemed endless.\nInstead of trying to learn everything at once, I took a different approach.\nI created a [12-month self-study roadmap](https://towardsdatascience.com/from-data-analyst-to-data-engineer-my-12-month-self-study-roadmap/) built around one simple idea: learn by building.\nRather than jumping from one tutorial to another, I would build a series of small projects. Each project would introduce a few new concepts while reinforcing what I had learned before. The goal wasn't to build the most sophisticated applications possible. It was to develop the habit of thinking like an engineer by solving one problem at a time.\nThe first project in that journey was a [GitHub ETL pipeline](https://towardsdatascience.com/i-built-my-first-etl-pipeline-as-a-complete-beginner-heres-exactly-how/). It began as a simple Python script that fetched repository data and exported it to a CSV file. As I learned more, I gradually improved it. I replaced CSV files with SQLite, made the pipeline idempotent to prevent duplicate data, and eventually automated it with GitHub Actions.\nBy the time I finished, I realized something that hadn't been obvious when I started.\nBuilding the ETL logic was actually the easy part.\nThe harder questions appeared once I stopped thinking about a script that runs once and started thinking about a system that needs to run over and over again without me watching it.\n * How should it be scheduled?\n * What happens if it fails halfway through?\n * Where should retry logic live?\n * How do you package the application so it runs the same way everywhere?\n\n\nThose questions taught me far more about data engineering than parsing JSON or writing SQL ever did.\nThat first project left me with another challenge.\n[GitHub Actions worked well for automating a small ETL pipeline](https://towardsdatascience.com/i-tried-to-schedule-my-etl-pipeline-heres-what-i-didnt-expect/), but I wanted to understand what changes when you use a workflow orchestrator built specifically for data engineering. I wanted to learn how engineers separate orchestration from execution, how containerized workloads fit into that picture, and what a production-minded pipeline actually looks like, even on a small scale.\nSo for my second project, I decided to **build an automated RSS ingestion pipeline.**\nOn the surface, it's a fairly simple application. It fetches articles from an RSS feed, parses them into structured objects, and stores them in PostgreSQL.\nBut the goal was never to build an RSS reader.\nThe goal was to explore the engineering decisions that transform a Python script into a reliable data pipeline.\nIn this article, I'll walk through those decisions, the mistakes I made along the way, and the lessons I learned building my first pipeline with Kestra.\n## Why Build Another ETL Pipeline?\nAfter finishing my first ETL project, I considered moving on to something completely different.\nMaybe a data warehouse project. Maybe Apache Spark. Maybe an API with a more complex transformation layer.\nInstead, I built... another ETL pipeline.\nAt first, that probably sounds like a step backwards.\nAfter all, I'd already built an extraction pipeline, made it idempotent, and scheduled it with GitHub Actions. Why repeat the same exercise?\nBecause I wasn't trying to learn a new dataset. I was trying to learn a new way of thinking.\nOne lesson from my first project stuck with me.\nWriting the ETL logic wasn't the difficult part. The difficult part was everything surrounding it.\n * How should the pipeline be executed?\n * How should it recover from failures?\n * How do you package it so it runs consistently on any machine?\n * Where does scheduling belong?\n\n\nAnd perhaps the biggest question of all:\n> Where should the responsibilities of the application end, and where should the responsibilities of the orchestration layer begin?\nThose questions don't have much to do with RSS feeds or GitHub repositories. They're engineering questions, and I realized I could explore them with almost any data source.\nThat's why I chose an RSS feed.\nNot because RSS is particularly exciting, but because it's intentionally simple.\nThe extraction logic only takes a few lines of Python. That meant I could spend less time worrying about business logic and more time thinking about architecture.\nFor the same reason, I decided to move away from GitHub Actions for this project.\nGitHub Actions was a great introduction to scheduling. It showed me how to automate a workflow and gave me my first taste of running an ETL pipeline without manual intervention.\nBut GitHub Actions isn't designed specifically for orchestrating data workflows.\nI wanted to understand what changes when you use a tool that's built with data pipelines in mind.\nThat's what led me to Kestra.\nRather than asking, \"How do I run this Python script every hour?\", I found myself asking a different set of questions.\n * How should retries be configured?\n * How are workflow executions tracked?\n * How should environment variables be passed into a container?\n * What does a failed execution look like?\n * How do you separate the application from the infrastructure that runs it?\n\n\nThose questions were exactly what I wanted to explore.\nBy choosing a simple ETL pipeline, I could focus on the engineering decisions instead of getting distracted by complicated business logic.\nLooking back, I think that was the right decision.\nThis project isn't interesting because it processes RSS feeds.\nIt's interesting because building it forced me to think about reliability, repeatability, and orchestration in a way my first project never did.\n## The First Architectural Decision: Docker Before Kestra\nWith the project defined, my first instinct was to jump straight into Kestra.\nAfter all, orchestration was one of the main reasons I chose this project. Why not start there?\nInstead, I did something that turned out to save me a lot of frustration later.\nI ignored Kestra completely.\nThat might sound counterintuitive, but I wanted to avoid introducing multiple moving parts before I knew the core application actually worked.\nSo I built the project in layers.\nFirst, I wrote the Python ETL.\nIts job was intentionally small. Fetch the RSS feed, parse each entry into an `Article` object, and save the results to PostgreSQL. Nothing more.\npython\n\n```\nfeed = feedparser.parse(RSS_URL)\n\narticles = parse_feed(feed)\n\nsave_articles(articles)\n```\n\nThat's really all the ETL did. I intentionally kept the application small because the focus of this project wasn't the transformation logic. It was everything that happened around it.\nOnce that worked reliably, I turned my attention to the database.\nI wanted repeated executions to be safe, so I made the inserts idempotent using PostgreSQL's `ON CONFLICT DO NOTHING`. That way, running the pipeline multiple times wouldn't create duplicate rows.\nsql\n\n```\nINSERT INTO articles (...)\nVALUES (...)\nON CONFLICT (id) DO NOTHING;\n```\n\nThis one line made the pipeline safe to execute repeatedly. Whether Kestra ran the workflow once or one hundred times, PostgreSQL became responsible for preventing duplicate records.\nOnly after the ETL and database worked together did I introduce Docker.\nThat decision changed how I thought about the project.\nInitially, Docker felt like another tool I needed to learn.\nBy the end of the project, I realized it had become something much more important.\nIt became the unit of execution.\nbash\n\n```\nFROM python:3.13-slim\n\nWORKDIR /app\n\nCOPY requirements.txt .\nRUN pip install --no-cache-dir -r requirements.txt\n\nCOPY . .\n\nCMD [\"python\", \"fetch_rss.py\"]\n```\n\nPackaging the ETL this way meant the application became self-contained. Instead of asking Kestra to understand my Python project, I could simply ask it to run a container that already knew how to execute the pipeline.\nInstead of thinking, \"Kestra will run my Python script,\" I started thinking, \"Kestra will run my Docker image.\"\nThat distinction might seem subtle, but it completely changes the relationship between your application and your orchestration layer.\nOnce the ETL was packaged into a container, it no longer mattered whether it ran on my laptop, inside Kestra, or on another machine entirely.\nThe runtime environment was always the same.\nThat consistency gave me something I didn't have before: confidence.\nBefore introducing Kestra, I could run the container manually and verify that it fetched the RSS feed, connected to PostgreSQL, and persisted the expected records.\nIf something failed, I knew the problem wasn't hidden behind another layer of orchestration.\nThat validation step turned out to be incredibly valuable later.\nDuring development, I ran into issues with networking, container configuration, and workflow execution. Because the Docker image had already been validated independently, I could immediately rule out the ETL itself and focus on the orchestration layer.\nThat made debugging dramatically easier.\nLooking back, I think this was one of the biggest lessons from the project.\nIt's tempting to connect every component together as quickly as possible and hope everything works.\nA better approach is to validate each layer before introducing the next one.\nIn this project, the order looked like this:\n * Validate the Python ETL.\n * Validate PostgreSQL persistence.\n * Validate the Docker image.\n * Finally, let Kestra orchestrate a container that I already trusted.\n\n\nEach layer built on the previous one.\nBy the time Kestra entered the picture, I wasn't trying to debug Python, PostgreSQL, Docker, and orchestration at the same time.\nI was only solving one problem.\nThat incremental approach made the entire project feel much more manageable, and it's a workflow I'll probably continue using on future data engineering projects.\n## The Assumption That Turned Out to Be Wrong\nWith a working Docker image, I was convinced the hard part was over.\n * I had a Python ETL that worked.\n * I had PostgreSQL running in Docker.\n * I had verified that the container could fetch RSS articles and save them to the database.\n\n\nNow all Kestra had to do was run it.\nOr so I thought.\nMy original assumption was simple.\nKestra would point to my Python files, execute the script, and everything would work exactly as it had from the command line.\nIt didn't.\nI quickly discovered that there was an important distinction I hadn't fully appreciated.\nKestra is an orchestrator.\nIt isn't responsible for building Python environments or managing application dependencies. Its responsibility is deciding when and how workloads should run.\nThat realization changed the direction of the project.\nInstead of treating Kestra as another place to execute Python code, I started treating it as the layer responsible for orchestrating a workload that already existed.\nThat workload was my Docker image.\nOnce I made that mental shift, the architecture became much cleaner.\n * The ETL became a self-contained application.\n * Docker became the deployment artifact.\n * Kestra became the orchestrator.\n\n\n![](../images/001.png)\nEach layer had a clear responsibility, and none of them needed to know how the others worked internally.\nInterestingly, reaching that point wasn't completely straightforward.\nMy first instinct was to find a way for Kestra to execute the Python project directly. That approach sounded simpler, but the more I experimented with it, the more I realized I was asking the orchestrator to take responsibility for something the application should already provide.\nOnce I embraced Docker as the execution artifact, the workflow became much simpler.\nSome approaches looked promising at first but introduced unnecessary complexity. Others worked, but didn't align with how Kestra encourages containerized workloads to be executed.\nEventually, I landed on a workflow that felt surprisingly simple.\nInstead of trying to teach Kestra how to run Python, I let Kestra do what it does best.\nIt launches a container.\nyaml\n\n```\ntasks:\n - id: run_etl\n type: io.kestra.plugin.scripts.shell.Commands\n\n containerImage: rss-pipeline-etl:latest\n\n taskRunner:\n type: io.kestra.plugin.scripts.runner.docker.Docker\n\n commands:\n - python /app/fetch_rss.py\n```\n\nLooking at the workflow now, what stands out isn't how much YAML it contains. It's how little Kestra actually needs to know about my application. Its only responsibility is to launch a container that already knows how to execute the ETL.\nInside that container, my application already knows exactly what to do.\nThat small architectural change solved more than just the immediate execution problem.\nIt also reinforced an idea that's becoming a recurring theme in my learning journey.\nGood engineering often isn't about adding another layer.\nIt's about giving each layer a single responsibility and allowing it to do that job well.\n * Python shouldn't worry about orchestration.\n * Kestra shouldn't worry about Python dependencies.\n * Docker shouldn't know anything about RSS feeds.\n\n\nEach component solves a different problem.\nOnce I stopped asking one tool to solve every problem, the entire system became much easier to reason about.\nLooking back, this was probably the biggest mindset shift in the whole project.\nI didn't just learn how to use Kestra.\nI learned what orchestration actually means.\n## Once a Pipeline Runs Automatically, Everything Changes\nUp until this point, I had been running the pipeline manually.\nIf something failed, I was sitting in front of my computer. I could read the error, make a change, and try again.\nThat safety net disappears the moment a pipeline starts running on its own.\nOne of the first things I configured in Kestra was a simple hourly schedule.\nyaml\n\n```\ntriggers:\n - id: hourly_schedule\n type: io.kestra.plugin.core.trigger.Schedule\n cron: \"0 * * * *\"\n```\n\nOn paper, it was just a cron expression.\nIn practice, it represented a much bigger shift.\nThe pipeline no longer depended on me remembering to run it.\nEvery hour, Kestra would start a new execution, launch the ETL container, and process the latest articles from the RSS feed.\nThat immediately raised another question.\nWhat happens if one of those executions fails?\nDuring development, I deliberately introduced failures to answer that question. I pointed the pipeline at an invalid database host and watched what happened.\nThe first execution failed, exactly as expected.\nMore importantly, it didn't stop there.\nBecause the workflow was configured with retries, Kestra automatically attempted the execution again after a short delay. \nyaml\n\n```\nretry:\n type: constant\n maxAttempts: 3\n interval: PT30S\n```\n\nThat was one of my favorite moments in the project.\nOnce I corrected the configuration, the workflow completed successfully without requiring any changes to the application itself.\nThat was one of my favorite moments in the project.\nNot because retries are particularly complicated, but because they highlighted another separation of responsibilities.\nThe ETL shouldn't decide whether it deserves another chance.\nThat's an orchestration concern.\nBy moving retry logic into Kestra, the Python application remained focused on a single responsibility: process the feed and persist the results.\nThe orchestration layer handled resilience.\nScheduling introduced another challenge that I'd already encountered in my first ETL project.\nRepeated executions mean repeated attempts to write data.\nIf the same RSS article appears in multiple hourly runs, the pipeline shouldn't insert it twice.\nFortunately, I had already solved a similar problem before.\nThe database layer was designed to be idempotent using PostgreSQL's `ON CONFLICT DO NOTHING`.\nsql\n\n```\nINSERT INTO articles (...)\nVALUES (...)\nON CONFLICT (id) DO NOTHING;\n```\n\nThat meant every execution could safely attempt to insert the same records without creating duplicates.\nThe combination of scheduling, retries, and idempotent writes made the pipeline much more forgiving.\nIf a run failed, Kestra could retry it.\nIf a successful retry encountered data that had already been written, PostgreSQL would simply ignore the duplicates.\nNeither layer needed to know what the other was doing.\nThey each handled their own responsibility.\nThe last improvement was visibility.\nEarly in development, my logs looked exactly like you'd expect from a project that was still being debugged.\nI printed entire RSS objects to the console just to make sure the parser was working.\nIt wasn't pretty, but it served its purpose.\nAs the pipeline became more stable, those debug statements became less useful.\nI replaced them with logs that described the execution instead of dumping raw data.\n### Before\npython\n\n```\nprint(feed.entries[0])\n```\n\n### After\npython\n\n```\nprint(\"=== RSS PIPELINE START ===\")\n\nfirst = feed.entries[0]\n\nprint(\"First entry preview:\")\nprint(f\"Title: {first.get('title')}\")\nprint(f\"Link: {first.get('link')}\")\n\nprint(f\"Feed title: {feed.feed.title}\")\nprint(f\"Fetched articles: {len(articles)}\")\nprint(f\"Saved {len(articles)} articles to the database.\")\n\nprint(\"=== RSS PIPELINE END ===\")\n```\n\nEach run now tells a simple story.\ntext\n\n```\n=== RSS PIPELINE START ===\n\nFirst entry preview:\nTitle: Christian Ledermann: Migrate From mypy To ty And pyrefly\nLink: https://dev.to/...\n\nFeed title: Planet Python\nFetched articles: 25\nSaved 25 articles to the database.\n\n=== RSS PIPELINE END ===\n```\n\nThose changes didn't make the application smarter. They made it easier to understand. And that's an important distinction.\nGood observability isn't about producing more logs. It's about producing the right logs.\nBy the end of the project, I realized something interesting. The Python code hadn't grown dramatically. Most of the work had happened around it.\nLooking back, most of the engineering effort wasn't spent making the ETL smarter. It was spent making it more reliable.\n * Scheduling ensured it ran without me.\n * Retries helped it recover from transient failures.\n * Idempotency protected the database from duplicate writes.\n * Logging made every execution easier to understand.\n\n\nIndividually, none of these changes were particularly complex. Together, they transformed a simple Python script into something that behaved much more like a production system.\n## The Final Architecture\nBy the end of the project, the architecture had settled into something that felt surprisingly simple.\n![](../images/body-c7474384db27c98cf637.png)\n \nLooking at the final architecture, it's tempting to think the project was always heading in this direction.\nIt wasn't.\nEach layer was added only after the previous one had been validated.\n * The ETL came first.\n * Then PostgreSQL.\n * Then Docker.\n * Finally, Kestra.\n\n\nThat order mattered.\nBecause every component had already been tested independently, I never found myself debugging Python, Docker, PostgreSQL, and Kestra at the same time. Each decision reduced the number of unknowns instead of increasing them.\nMore importantly, every component ended up with a clear responsibility.\n * Python knows how to fetch, parse, and persist RSS articles.\n * PostgreSQL knows how to store data safely and prevent duplicates.\n * Docker provides a consistent execution environment.\n * Kestra decides when the workload should run and what should happen if it fails.\n\n\nNone of those components are trying to do each other's jobs. \nIronically, that's what made the finished system feel much simpler than I expected.\n## What This Project Changed About the Way I Think\nWhen I started learning data engineering, I assumed the difficult part would be writing ETL code.\nThat's what most beginner tutorials focus on.\n * You learn how to call an API.\n * You transform the data.\n * You save it somewhere.\n\n\nRepeat.\nThose are valuable skills, but they're only one part of the picture. \nThis project taught me that the engineering begins after the script works.\nOnce a pipeline is expected to run every hour, survive transient failures, avoid duplicate data, and produce logs that explain what happened, the questions become much more interesting.\nYou're no longer thinking about individual functions. You're thinking about systems.\nOne of the biggest mindset shifts for me was understanding the difference between execution and orchestration.\nAt first, those ideas felt almost interchangeable. Now they feel completely separate.\nThe Python application should focus on business logic. The orchestrator should focus on when, where, and how that application runs.\nKeeping those responsibilities separate made the entire project easier to reason about.\nIt also changed how I think about Docker.\nBefore this project, I saw Docker primarily as a way to package applications.\nNow I see it as a deployment artifact.\nOnce the ETL had been packaged into a container and validated independently, I could stop worrying about whether it would behave differently inside Kestra.\nThat confidence turned out to be one of the biggest advantages of containerizing the application.\nPerhaps the most important lesson, though, had nothing to do with Kestra or Docker. It was the value of building incrementally. \nEvery major decision followed the same pattern.\n * Build the smallest thing that works.\n * Validate it.\n * Only then introduce the next layer.\n\n\nThat approach made the project feel much less overwhelming than trying to connect everything together from the beginning. \nLooking back, I think that's a lesson I'll carry into every future project, regardless of the technology.\n## Looking Ahead\nThis RSS pipeline is only the second project in my data engineering learning journey. Compared to my first ETL pipeline, the code itself isn't dramatically more complex. What changed was the way I approached the problem.\nInstead of asking, \"How do I write this script?\", I found myself asking questions like:\n * Where should this responsibility live?\n * What happens if it fails?\n * Can I run it repeatedly without worrying about duplicate data\n * Can I trust it to run when I'm not watching?\n\n\nThose questions pushed me to think less like someone writing Python code and more like someone designing a system. And I suspect that's the real value of building projects.\nEvery project teaches a new tool. But the best projects slowly change the way you think. This one certainly did.\nI'm sure the next project will challenge a completely different set of assumptions. Honestly, I'm looking forward to finding out what they are.\n_This is part of my ongoing series documenting my transition from systems analyst to data engineer. If you’ve been following along, thank you._\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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This Time, I Started Thinking Like a Data Engineer\nBuilding a production-ready RSS pipeline with Python, Docker, PostgreSQL, and Kestra\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nJuly 10, 202615 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/07/etl-article-image-rss.jpg)\nArchitectural Diagram Generated by AI\nA few months ago, I decided I wanted to transition from data analyst to data engineer.\nLike many people starting out, I was overwhelmed by the sheer number of things I thought I needed to learn. Data warehouses, orchestration tools, distributed processing, streaming systems, cloud platforms, infrastructure. The list seemed endless.\nInstead of trying to learn everything at once, I took a different approach.\nI created a [12-month self-study roadmap](https://towardsdatascience.com/from-data-analyst-to-data-engineer-my-12-month-self-study-roadmap/) built around one simple idea: learn by building.\nRather than jumping from one tutorial to another, I would build a series of small projects. Each project would introduce a few new concepts while reinforcing what I had learned before. The goal wasn't to build the most sophisticated applications possible. It was to develop the habit of thinking like an engineer by solving one problem at a time.\nThe first project in that journey was a [GitHub ETL pipeline](https://towardsdatascience.com/i-built-my-first-etl-pipeline-as-a-complete-beginner-heres-exactly-how/). It began as a simple Python script that fetched repository data and exported it to a CSV file. As I learned more, I gradually improved it. I replaced CSV files with SQLite, made the pipeline idempotent to prevent duplicate data, and eventually automated it with GitHub Actions.\nBy the time I finished, I realized something that hadn't been obvious when I started.\nBuilding the ETL logic was actually the easy part.\nThe harder questions appeared once I stopped thinking about a script that runs once and started thinking about a system that needs to run over and over again without me watching it.\n * How should it be scheduled?\n * What happens if it fails halfway through?\n * Where should retry logic live?\n * How do you package the application so it runs the same way everywhere?\n\n\nThose questions taught me far more about data engineering than parsing JSON or writing SQL ever did.\nThat first project left me with another challenge.\n[GitHub Actions worked well for automating a small ETL pipeline](https://towardsdatascience.com/i-tried-to-schedule-my-etl-pipeline-heres-what-i-didnt-expect/), but I wanted to understand what changes when you use a workflow orchestrator built specifically for data engineering. I wanted to learn how engineers separate orchestration from execution, how containerized workloads fit into that picture, and what a production-minded pipeline actually looks like, even on a small scale.\nSo for my second project, I decided to **build an automated RSS ingestion pipeline.**\nOn the surface, it's a fairly simple application. It fetches articles from an RSS feed, parses them into structured objects, and stores them in PostgreSQL.\nBut the goal was never to build an RSS reader.\nThe goal was to explore the engineering decisions that transform a Python script into a reliable data pipeline.\nIn this article, I'll walk through those decisions, the mistakes I made along the way, and the lessons I learned building my first pipeline with Kestra.\n## Why Build Another ETL Pipeline?\nAfter finishing my first ETL project, I considered moving on to something completely different.\nMaybe a data warehouse project. Maybe Apache Spark. Maybe an API with a more complex transformation layer.\nInstead, I built... another ETL pipeline.\nAt first, that probably sounds like a step backwards.\nAfter all, I'd already built an extraction pipeline, made it idempotent, and scheduled it with GitHub Actions. Why repeat the same exercise?\nBecause I wasn't trying to learn a new dataset. I was trying to learn a new way of thinking.\nOne lesson from my first project stuck with me.\nWriting the ETL logic wasn't the difficult part. The difficult part was everything surrounding it.\n * How should the pipeline be executed?\n * How should it recover from failures?\n * How do you package it so it runs consistently on any machine?\n * Where does scheduling belong?\n\n\nAnd perhaps the biggest question of all:\n> Where should the responsibilities of the application end, and where should the responsibilities of the orchestration layer begin?\nThose questions don't have much to do with RSS feeds or GitHub repositories. They're engineering questions, and I realized I could explore them with almost any data source.\nThat's why I chose an RSS feed.\nNot because RSS is particularly exciting, but because it's intentionally simple.\nThe extraction logic only takes a few lines of Python. That meant I could spend less time worrying about business logic and more time thinking about architecture.\nFor the same reason, I decided to move away from GitHub Actions for this project.\nGitHub Actions was a great introduction to scheduling. It showed me how to automate a workflow and gave me my first taste of running an ETL pipeline without manual intervention.\nBut GitHub Actions isn't designed specifically for orchestrating data workflows.\nI wanted to understand what changes when you use a tool that's built with data pipelines in mind.\nThat's what led me to Kestra.\nRather than asking, \"How do I run this Python script every hour?\", I found myself asking a different set of questions.\n * How should retries be configured?\n * How are workflow executions tracked?\n * How should environment variables be passed into a container?\n * What does a failed execution look like?\n * How do you separate the application from the infrastructure that runs it?\n\n\nThose questions were exactly what I wanted to explore.\nBy choosing a simple ETL pipeline, I could focus on the engineering decisions instead of getting distracted by complicated business logic.\nLooking back, I think that was the right decision.\nThis project isn't interesting because it processes RSS feeds.\nIt's interesting because building it forced me to think about reliability, repeatability, and orchestration in a way my first project never did.\n## The First Architectural Decision: Docker Before Kestra\nWith the project defined, my first instinct was to jump straight into Kestra.\nAfter all, orchestration was one of the main reasons I chose this project. Why not start there?\nInstead, I did something that turned out to save me a lot of frustration later.\nI ignored Kestra completely.\nThat might sound counterintuitive, but I wanted to avoid introducing multiple moving parts before I knew the core application actually worked.\nSo I built the project in layers.\nFirst, I wrote the Python ETL.\nIts job was intentionally small. Fetch the RSS feed, parse each entry into an `Article` object, and save the results to PostgreSQL. Nothing more.\npython\n\n```\nfeed = feedparser.parse(RSS_URL)\n\narticles = parse_feed(feed)\n\nsave_articles(articles)\n```\n\nThat's really all the ETL did. I intentionally kept the application small because the focus of this project wasn't the transformation logic. It was everything that happened around it.\nOnce that worked reliably, I turned my attention to the database.\nI wanted repeated executions to be safe, so I made the inserts idempotent using PostgreSQL's `ON CONFLICT DO NOTHING`. That way, running the pipeline multiple times wouldn't create duplicate rows.\nsql\n\n```\nINSERT INTO articles (...)\nVALUES (...)\nON CONFLICT (id) DO NOTHING;\n```\n\nThis one line made the pipeline safe to execute repeatedly. Whether Kestra ran the workflow once or one hundred times, PostgreSQL became responsible for preventing duplicate records.\nOnly after the ETL and database worked together did I introduce Docker.\nThat decision changed how I thought about the project.\nInitially, Docker felt like another tool I needed to learn.\nBy the end of the project, I realized it had become something much more important.\nIt became the unit of execution.\nbash\n\n```\nFROM python:3.13-slim\n\nWORKDIR /app\n\nCOPY requirements.txt .\nRUN pip install --no-cache-dir -r requirements.txt\n\nCOPY . .\n\nCMD [\"python\", \"fetch_rss.py\"]\n```\n\nPackaging the ETL this way meant the application became self-contained. Instead of asking Kestra to understand my Python project, I could simply ask it to run a container that already knew how to execute the pipeline.\nInstead of thinking, \"Kestra will run my Python script,\" I started thinking, \"Kestra will run my Docker image.\"\nThat distinction might seem subtle, but it completely changes the relationship between your application and your orchestration layer.\nOnce the ETL was packaged into a container, it no longer mattered whether it ran on my laptop, inside Kestra, or on another machine entirely.\nThe runtime environment was always the same.\nThat consistency gave me something I didn't have before: confidence.\nBefore introducing Kestra, I could run the container manually and verify that it fetched the RSS feed, connected to PostgreSQL, and persisted the expected records.\nIf something failed, I knew the problem wasn't hidden behind another layer of orchestration.\nThat validation step turned out to be incredibly valuable later.\nDuring development, I ran into issues with networking, container configuration, and workflow execution. Because the Docker image had already been validated independently, I could immediately rule out the ETL itself and focus on the orchestration layer.\nThat made debugging dramatically easier.\nLooking back, I think this was one of the biggest lessons from the project.\nIt's tempting to connect every component together as quickly as possible and hope everything works.\nA better approach is to validate each layer before introducing the next one.\nIn this project, the order looked like this:\n * Validate the Python ETL.\n * Validate PostgreSQL persistence.\n * Validate the Docker image.\n * Finally, let Kestra orchestrate a container that I already trusted.\n\n\nEach layer built on the previous one.\nBy the time Kestra entered the picture, I wasn't trying to debug Python, PostgreSQL, Docker, and orchestration at the same time.\nI was only solving one problem.\nThat incremental approach made the entire project feel much more manageable, and it's a workflow I'll probably continue using on future data engineering projects.\n## The Assumption That Turned Out to Be Wrong\nWith a working Docker image, I was convinced the hard part was over.\n * I had a Python ETL that worked.\n * I had PostgreSQL running in Docker.\n * I had verified that the container could fetch RSS articles and save them to the database.\n\n\nNow all Kestra had to do was run it.\nOr so I thought.\nMy original assumption was simple.\nKestra would point to my Python files, execute the script, and everything would work exactly as it had from the command line.\nIt didn't.\nI quickly discovered that there was an important distinction I hadn't fully appreciated.\nKestra is an orchestrator.\nIt isn't responsible for building Python environments or managing application dependencies. Its responsibility is deciding when and how workloads should run.\nThat realization changed the direction of the project.\nInstead of treating Kestra as another place to execute Python code, I started treating it as the layer responsible for orchestrating a workload that already existed.\nThat workload was my Docker image.\nOnce I made that mental shift, the architecture became much cleaner.\n * The ETL became a self-contained application.\n * Docker became the deployment artifact.\n * Kestra became the orchestrator.\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/07/arch-diagram1.png)\nEach layer had a clear responsibility, and none of them needed to know how the others worked internally.\nInterestingly, reaching that point wasn't completely straightforward.\nMy first instinct was to find a way for Kestra to execute the Python project directly. That approach sounded simpler, but the more I experimented with it, the more I realized I was asking the orchestrator to take responsibility for something the application should already provide.\nOnce I embraced Docker as the execution artifact, the workflow became much simpler.\nSome approaches looked promising at first but introduced unnecessary complexity. Others worked, but didn't align with how Kestra encourages containerized workloads to be executed.\nEventually, I landed on a workflow that felt surprisingly simple.\nInstead of trying to teach Kestra how to run Python, I let Kestra do what it does best.\nIt launches a container.\nyaml\n\n```\ntasks:\n - id: run_etl\n type: io.kestra.plugin.scripts.shell.Commands\n\n containerImage: rss-pipeline-etl:latest\n\n taskRunner:\n type: io.kestra.plugin.scripts.runner.docker.Docker\n\n commands:\n - python /app/fetch_rss.py\n```\n\nLooking at the workflow now, what stands out isn't how much YAML it contains. It's how little Kestra actually needs to know about my application. Its only responsibility is to launch a container that already knows how to execute the ETL.\nInside that container, my application already knows exactly what to do.\nThat small architectural change solved more than just the immediate execution problem.\nIt also reinforced an idea that's becoming a recurring theme in my learning journey.\nGood engineering often isn't about adding another layer.\nIt's about giving each layer a single responsibility and allowing it to do that job well.\n * Python shouldn't worry about orchestration.\n * Kestra shouldn't worry about Python dependencies.\n * Docker shouldn't know anything about RSS feeds.\n\n\nEach component solves a different problem.\nOnce I stopped asking one tool to solve every problem, the entire system became much easier to reason about.\nLooking back, this was probably the biggest mindset shift in the whole project.\nI didn't just learn how to use Kestra.\nI learned what orchestration actually means.\n## Once a Pipeline Runs Automatically, Everything Changes\nUp until this point, I had been running the pipeline manually.\nIf something failed, I was sitting in front of my computer. I could read the error, make a change, and try again.\nThat safety net disappears the moment a pipeline starts running on its own.\nOne of the first things I configured in Kestra was a simple hourly schedule.\nyaml\n\n```\ntriggers:\n - id: hourly_schedule\n type: io.kestra.plugin.core.trigger.Schedule\n cron: \"0 * * * *\"\n```\n\nOn paper, it was just a cron expression.\nIn practice, it represented a much bigger shift.\nThe pipeline no longer depended on me remembering to run it.\nEvery hour, Kestra would start a new execution, launch the ETL container, and process the latest articles from the RSS feed.\nThat immediately raised another question.\nWhat happens if one of those executions fails?\nDuring development, I deliberately introduced failures to answer that question. I pointed the pipeline at an invalid database host and watched what happened.\nThe first execution failed, exactly as expected.\nMore importantly, it didn't stop there.\nBecause the workflow was configured with retries, Kestra automatically attempted the execution again after a short delay. \nyaml\n\n```\nretry:\n type: constant\n maxAttempts: 3\n interval: PT30S\n```\n\nThat was one of my favorite moments in the project.\nOnce I corrected the configuration, the workflow completed successfully without requiring any changes to the application itself.\nThat was one of my favorite moments in the project.\nNot because retries are particularly complicated, but because they highlighted another separation of responsibilities.\nThe ETL shouldn't decide whether it deserves another chance.\nThat's an orchestration concern.\nBy moving retry logic into Kestra, the Python application remained focused on a single responsibility: process the feed and persist the results.\nThe orchestration layer handled resilience.\nScheduling introduced another challenge that I'd already encountered in my first ETL project.\nRepeated executions mean repeated attempts to write data.\nIf the same RSS article appears in multiple hourly runs, the pipeline shouldn't insert it twice.\nFortunately, I had already solved a similar problem before.\nThe database layer was designed to be idempotent using PostgreSQL's `ON CONFLICT DO NOTHING`.\nsql\n\n```\nINSERT INTO articles (...)\nVALUES (...)\nON CONFLICT (id) DO NOTHING;\n```\n\nThat meant every execution could safely attempt to insert the same records without creating duplicates.\nThe combination of scheduling, retries, and idempotent writes made the pipeline much more forgiving.\nIf a run failed, Kestra could retry it.\nIf a successful retry encountered data that had already been written, PostgreSQL would simply ignore the duplicates.\nNeither layer needed to know what the other was doing.\nThey each handled their own responsibility.\nThe last improvement was visibility.\nEarly in development, my logs looked exactly like you'd expect from a project that was still being debugged.\nI printed entire RSS objects to the console just to make sure the parser was working.\nIt wasn't pretty, but it served its purpose.\nAs the pipeline became more stable, those debug statements became less useful.\nI replaced them with logs that described the execution instead of dumping raw data.\n### Before\npython\n\n```\nprint(feed.entries[0])\n```\n\n### After\npython\n\n```\nprint(\"=== RSS PIPELINE START ===\")\n\nfirst = feed.entries[0]\n\nprint(\"First entry preview:\")\nprint(f\"Title: {first.get('title')}\")\nprint(f\"Link: {first.get('link')}\")\n\nprint(f\"Feed title: {feed.feed.title}\")\nprint(f\"Fetched articles: {len(articles)}\")\nprint(f\"Saved {len(articles)} articles to the database.\")\n\nprint(\"=== RSS PIPELINE END ===\")\n```\n\nEach run now tells a simple story.\ntext\n\n```\n=== RSS PIPELINE START ===\n\nFirst entry preview:\nTitle: Christian Ledermann: Migrate From mypy To ty And pyrefly\nLink: https://dev.to/...\n\nFeed title: Planet Python\nFetched articles: 25\nSaved 25 articles to the database.\n\n=== RSS PIPELINE END ===\n```\n\nThose changes didn't make the application smarter. They made it easier to understand. And that's an important distinction.\nGood observability isn't about producing more logs. It's about producing the right logs.\nBy the end of the project, I realized something interesting. The Python code hadn't grown dramatically. Most of the work had happened around it.\nLooking back, most of the engineering effort wasn't spent making the ETL smarter. It was spent making it more reliable.\n * Scheduling ensured it ran without me.\n * Retries helped it recover from transient failures.\n * Idempotency protected the database from duplicate writes.\n * Logging made every execution easier to understand.\n\n\nIndividually, none of these changes were particularly complex. Together, they transformed a simple Python script into something that behaved much more like a production system.\n## The Final Architecture\nBy the end of the project, the architecture had settled into something that felt surprisingly simple.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/07/etl2.png)\n \nLooking at the final architecture, it's tempting to think the project was always heading in this direction.\nIt wasn't.\nEach layer was added only after the previous one had been validated.\n * The ETL came first.\n * Then PostgreSQL.\n * Then Docker.\n * Finally, Kestra.\n\n\nThat order mattered.\nBecause every component had already been tested independently, I never found myself debugging Python, Docker, PostgreSQL, and Kestra at the same time. Each decision reduced the number of unknowns instead of increasing them.\nMore importantly, every component ended up with a clear responsibility.\n * Python knows how to fetch, parse, and persist RSS articles.\n * PostgreSQL knows how to store data safely and prevent duplicates.\n * Docker provides a consistent execution environment.\n * Kestra decides when the workload should run and what should happen if it fails.\n\n\nNone of those components are trying to do each other's jobs. \nIronically, that's what made the finished system feel much simpler than I expected.\n## What This Project Changed About the Way I Think\nWhen I started learning data engineering, I assumed the difficult part would be writing ETL code.\nThat's what most beginner tutorials focus on.\n * You learn how to call an API.\n * You transform the data.\n * You save it somewhere.\n\n\nRepeat.\nThose are valuable skills, but they're only one part of the picture. \nThis project taught me that the engineering begins after the script works.\nOnce a pipeline is expected to run every hour, survive transient failures, avoid duplicate data, and produce logs that explain what happened, the questions become much more interesting.\nYou're no longer thinking about individual functions. You're thinking about systems.\nOne of the biggest mindset shifts for me was understanding the difference between execution and orchestration.\nAt first, those ideas felt almost interchangeable. Now they feel completely separate.\nThe Python application should focus on business logic. The orchestrator should focus on when, where, and how that application runs.\nKeeping those responsibilities separate made the entire project easier to reason about.\nIt also changed how I think about Docker.\nBefore this project, I saw Docker primarily as a way to package applications.\nNow I see it as a deployment artifact.\nOnce the ETL had been packaged into a container and validated independently, I could stop worrying about whether it would behave differently inside Kestra.\nThat confidence turned out to be one of the biggest advantages of containerizing the application.\nPerhaps the most important lesson, though, had nothing to do with Kestra or Docker. It was the value of building incrementally. \nEvery major decision followed the same pattern.\n * Build the smallest thing that works.\n * Validate it.\n * Only then introduce the next layer.\n\n\nThat approach made the project feel much less overwhelming than trying to connect everything together from the beginning. \nLooking back, I think that's a lesson I'll carry into every future project, regardless of the technology.\n## Looking Ahead\nThis RSS pipeline is only the second project in my data engineering learning journey. Compared to my first ETL pipeline, the code itself isn't dramatically more complex. What changed was the way I approached the problem.\nInstead of asking, \"How do I write this script?\", I found myself asking questions like:\n * Where should this responsibility live?\n * What happens if it fails?\n * Can I run it repeatedly without worrying about duplicate data\n * Can I trust it to run when I'm not watching?\n\n\nThose questions pushed me to think less like someone writing Python code and more like someone designing a system. And I suspect that's the real value of building projects.\nEvery project teaches a new tool. But the best projects slowly change the way you think. This one certainly did.\nI'm sure the next project will challenge a completely different set of assumptions. Honestly, I'm looking forward to finding out what they are.\n_This is part of my ongoing series documenting my transition from systems analyst to data engineer. If you’ve been following along, thank you._\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. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657709013_9ddcph.jpeg)\nWritten By\n## [Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\n[See all from Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\n[Data Engineer](https://towardsdatascience.com/tag/data-engineer/)[Docker](https://towardsdatascience.com/tag/docker/)[Etl](https://towardsdatascience.com/tag/etl/)[Etl Pipeline](https://towardsdatascience.com/tag/etl-pipeline/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fi-built-my-second-etl-pipeline-this-time-i-started-thinking-like-a-data-engineer%2F&title=I%20Built%20My%20Second%20ETL%20Pipeline.%20This%20Time%2C%20I%20Started%20Thinking%20Like%20a%20Data%20Engineer)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fi-built-my-second-etl-pipeline-this-time-i-started-thinking-like-a-data-engineer%2F&title=I%20Built%20My%20Second%20ETL%20Pipeline.%20This%20Time%2C%20I%20Started%20Thinking%20Like%20a%20Data%20Engineer)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fi-built-my-second-etl-pipeline-this-time-i-started-thinking-like-a-data-engineer%2F&text=I%20Built%20My%20Second%20ETL%20Pipeline.%20This%20Time%2C%20I%20Started%20Thinking%20Like%20a%20Data%20Engineer)\n\n\nTowards Data Science is a community publication. 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