Building an End-to-End Data Pipeline with DuckDB and Python

New Project: Building a Resilient ETL Pipeline for Public Service Data Hello network! I’m excited to share my latest Data Engineering project, where I focused on transforming raw governmental records into structured, analysis-ready information. Real-world open data often comes with quality challenges: inconsistent date formats, hidden white spaces in schemas, and missing values. To tackle this, I developed GovData-ETL, a robust pipeline that automates data ingestion and sanitization. Key Technical Highlights: - Data Cleaning & Wrangling: Leveraged Python (Pandas) for schema normalization and complex data type casting. - Defensive Programming: Implemented robust error handling for encoding issues and inconsistent column naming (preventing pipeline breakage). - SQL Architecture: Automated the loading process into SQLite, migrating flat CSV files into an efficient relational model. - Business Intelligence: Developed automated SQL queries to extract key metrics, such as service distribution by modality. This project allowed me to strengthen core engineering skills: ensuring data integrity, handling high-variability inputs, and designing efficient workflows—all essential pillars for a Data Engineer role. Check out the full code and documentation on my GitHub: 👉 https://lnkd.in/eeFmS4-D I’d love to hear your thoughts or feedback! #DataEngineering #Python #SQL #ETL #DataQuality #Backend #DataScience #DataAnalytics #OpenData #NL #NuevoLeon

Stop calling yourself a Data Engineer if you've never fixed a pipeline nobody knew was broken. I see it constantly. Engineers who want to design the "lakehouse architecture" but have never traced why 12,000 rows vanished between source and target. That's not engineering. That's slideware. LinkedIn makes data engineering look like standing up Spark clusters and drawing architecture diagrams. The reality is reading 400 lines of Airflow logs trying to figure out why a DAG silently failed three days ago. Here's the truth: 1. You will write more SQL than Python. And it won't be elegant. It'll be a 200-line query with six CTEs because someone designed every column as a VARCHAR. 2. Your most important skill is not Spark or Kafka. It's looking at a dataset and immediately knowing something is wrong. 3. Nobody will thank you for the disaster that didn't happen. The pipeline that ran perfectly for 18 months. The backfill that just worked. Your best work is invisible. 4. Tools don't matter as much as you think. I've seen teams with Databricks, dbt, and Airflow still deliver garbage data. And teams running cron jobs deliver data the business actually trusts. Stop chasing the next framework. Start understanding your data. What's the messiest pipeline you've ever had to untangle?

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝘃𝘀 𝗦𝗽𝗮𝗿𝗸 𝗦𝗤𝗟: 𝗜 𝗱𝗼𝗻’𝘁 𝗰𝗵𝗼𝗼𝘀𝗲 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲, 𝗜 𝗰𝗵𝗼𝗼𝘀𝗲 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘁𝗵𝗲 𝗷𝗼𝗯. It’s easy to turn this into a “which is better” debate. In practice, both are useful just for different reasons. And one thing is often misunderstood: Spark doesn’t execute “Python” or “SQL” the way people think. It executes a 𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗽𝗹𝗮𝗻 -> 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗲𝗱 𝗽𝗹𝗮𝗻 -> 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗽𝗹𝗮𝗻. So a lot of the time, the real difference isn’t performance, it’s 𝗵𝗼𝘄 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝘆𝗼𝘂 𝗲𝘅𝗽𝗿𝗲𝘀𝘀 𝗶𝗻𝘁𝗲𝗻𝘁 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝘁𝗵𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗶𝘀. 𝗪𝗵𝗲𝗻 𝗦𝗽𝗮𝗿𝗸 𝗦𝗤𝗟 𝘄𝗶𝗻𝘀   • The work is mostly select, join, filter, aggregate   • Logic needs to be readable by more people (analysts + engineers)   • I want quick iteration and clear intent   • Performance tuning is easier because the query shape is obvious 𝗪𝗵𝗲𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝘄𝗶𝗻𝘀   • I need custom logic that’s awkward in SQL   • Complex parsing, nested structures, arrays/maps, JSON heavy work   • Reusable functions and cleaner code structure (modules, unit tests)   • Integration steps around the transformation (validation, file handling, etc.) 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘁𝗿𝗮𝗱𝗲 𝗼𝗳𝗳   • SQL usually optimizes for clarity.   • PySpark usually optimizes for flexibility. 𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗜’𝘃𝗲 𝘀𝗲𝗲𝗻   • Use SQL for the core transformations (joins/aggregations)   • Use PySpark for the edges (validation, enrichment, complex business rules)   • Keep one “source of truth” so business logic doesn’t get duplicated Takeaway: Choosing PySpark vs Spark SQL isn’t a style choice. It’s a maintainability and delivery choice. Drop your go-to rule for choosing between them in the comments. #PySpark #SparkSQL #DataEngineering #Databricks #BigData #SQL #AnalyticsEngineering #DataPipelines

I decoupled the data layer of my latest project into a production-grade pipeline. Here is the architecture. In building MyJobPhase, I realized that the value isn't just in the frontend — it's in the reliability and scalability of the underlying data. I spent the last week architecting a standalone infrastructure to handle high-volume ingestion and transformation. The Engineering Stack: 🔶 Bronze Layer (Raw): Ingests and stores raw, immutable JSON from 11+ sources (Greenhouse, Lever, Ashby, etc.). This ensures a Single Source of Truth and allows for full state replay in case of downstream failures. 🥈 Silver Layer (Cleaned): Standardizes disparate schemas and deduplicates records. Data is stored in Parquet format with Snappy compression, reducing storage footprint by ~70% and enabling high-performance columnar reads. 🥇 Gold Layer (Analytics): Aggregates data into business-ready tables — Company Rankings, Daily Job Metrics, Source Performance — optimized for BI consumption and downstream analytics. 💀 Dead Letter Queue (DLQ): To ensure data integrity, I implemented a fail-forward system where malformed or out-of-scope records are isolated into an audit log rather than silently dropped. 🔁 Orchestration (Airflow): A 6-task DAG manages the full workflow with built-in retry logic, dependency mapping, and automated resource cleanup. The Numbers: The pipeline currently processes 1,900+ worldwide remote job entries daily with sub-6-hour latency. 11 active sources. 34 records isolated to DLQ. 3 Gold tables recomputed every run. Full pipeline code & architecture docs: 👉 https://lnkd.in/eNn982Ec #DataEngineering #ModernDataStack #Airflow #Python #Parquet #SoftwareEngineering

Still using cron jobs to run your data pipelines? Honest question, how do you handle retries, task dependencies, or debugging a failure that happened at 3 AM? That's exactly where Apache Airflow comes in. Our latest article on Data Engineering Byte breaks down Airflow in the simplest way possible, no jargon overload, no assumptions. Here's what you'll walk away with: → Why cron falls short (dependencies, retries, branching — it can't do any of it well) → What a DAG actually is (and why it's called "acyclic") → Your first DAG in under 20 lines of Python: with DAG(   dag_id="simple_example",   start_date=datetime(2026, 1, 1),   schedule="@daily",   catchup=False ) as dag:   t1 >> t2 → What catchup=True vs False really means → How tasks talk to each other using XComs (think: passing sticky notes) → Full Docker setup to run Airflow 3 locally in minutes One thing that trips up beginners: Airflow does NOT store data. It only orchestrates. Your DAG tells tasks what to run, in what order, and when — that's it. Whether you're a data engineer, analyst stepping into pipelines, or just Airflow-curious — this 5-minute read will get you from zero to running your first DAG. ✍️ Written by Shrividya Hegde (Shri): AI Data Engineer, Apache Airflow Champion, and Women in Data Chapter Lead. 🔗 Link in comments 👇 Subscribe to Data Engineering Byte for more hands-on, no-fluff data engineering tutorials every week. #ApacheAirflow #DataEngineering #ETL #Python #DataPipelines #Airflow #DataEngineeringByte

Excited to join the Data Engineering Byte team as a Content Expert! 🎉 I'll be sharing practical insights on data engineering topics through Substack , starting with a beginner-friendly deep dive into Apache Airflow. If you've been curious about what Airflow is and why it matters, this first article covers the core fundamentals to get you started. More articles on the way. Thanks for the opportunity , can't wait to give back to this community!

Most data engineers don’t fail because they lack coding skills. They fail because they ignore system design. You can write perfect SQL. You can build clean PySpark jobs. You can even optimize pipelines. But when the system scales… everything breaks. Pipelines slow down. APIs hit rate limits. Costs shoot up. Data becomes inconsistent. And suddenly — your “working solution” becomes a production nightmare. That’s where system design comes in. It’s not about drawing diagrams. It’s about thinking: How will this behave with 10x data? What happens when an API fails midway? Can this pipeline recover automatically? Are we building for one client or 100 clients? Where will this system break first? In the data world, system design means: ✔ Designing resilient pipelines ✔ Handling failures gracefully ✔ Managing scale without exploding costs ✔ Building reusable connectors instead of one-off scripts ✔ Thinking beyond “it works” → “it works reliably at scale” The shift is simple but powerful: From → Writing pipelines To → Designing data systems That’s the difference between a good data engineer and a high-impact one. Still underrated. But absolutely non-negotiable. #DataEngineering #SystemDesign #BigData #ETL #DataPipelines #Scalability #TechCareers

Data ingestion should be the least of your worries. But don't just take our word it; hear what Benjamin Rogojan (Seattle Data Guy) had to say in his recent conversation with Estuary customer Tim Frazer 🚀 (Trust & Will). As a data engineer, your time is valuable and better spent on the actual data transformation and downstream business application. Instead of spending engineering cycles writing the same old Python scripts, let a vendor handle your ingestion layer and get back to what really matters.

Day 61 The Spark job that taught me more than any tutorial didn't throw an error. It ran perfectly. Completed in 4 minutes. Showed green on the dashboard. The numbers were just wrong. That's the most dangerous kind of failure in data engineering - the one that looks like success. I spent two days debugging. Turns out I had a silent null coalesce in a join that was dropping 30% of records without a single warning. Here's what I learned from that mess: ▪️ Wrong output with no error is scarier than a broken pipeline with logs. ▪️ Validate outputs, not just pipeline runs. Row counts, null rates, distribution checks. Every time. ▪️ Write tests before you write transformations. Not after. Not "later." ▪️ If a pipeline has never failed in production, you probably don't have good enough monitoring. ▪️ We celebrate pipeline uptime. We should celebrate catching silent bugs before they reach dashboards. ▪️ What's the sneakiest data bug you've ever shipped to production? Drop it below. #DataEngineering #DataQuality #Spark

Most data engineers don’t fail at pipelines… They fail at dirty data. And the worst part? It doesn’t throw errors. It silently corrupts your dashboards. I’ve seen this happen way too often: • NULLs breaking transformations • Duplicates inflating metrics • Wrong data types giving completely wrong insights • Outliers making dashboards look “great” (when they’re not) That’s why I created this Data Cleaning Cheat Sheet (SQL vs Python) 👇 Not theory. Just the exact things you’ll use in real projects. Because in real-world Data Engineering: 👉 Writing queries is easy 👉 Cleaning data is where the real work happens If you’re preparing for interviews or working on pipelines… This is something you’ll keep coming back to. Save this before your next pipeline breaks. 📌𝗙𝗼𝗿 𝗠𝗲𝗻𝘁𝗼𝗿𝘀𝗵𝗶𝗽/ 𝟭:𝟭 𝗖𝗮𝗹𝗹 𝗯𝗼𝗼𝗸 𝗵𝗲𝗿𝗲 -- https://lnkd.in/gjHqeHMq 📌 𝐋𝐨𝐨𝐤𝐢𝐧𝐠 𝐟𝐨𝐫 𝐑𝐞𝐬𝐮𝐦𝐞 𝐡𝐚𝐯𝐢𝐧𝐠 𝟗𝟎+ 𝐀𝐓𝐒 𝐬𝐜𝐨𝐫𝐞? 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿-𝗔𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗥𝗲𝘀𝘂𝗺𝗲 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲 -https://lnkd.in/gxrUrxXg 📌 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗮𝗿𝗲𝗲𝗿? 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗼𝗽𝗲𝗻 𝗳𝗼𝗿 𝗼𝘂𝗿 𝟮𝗻𝗱 𝗯𝗮𝘁𝗰𝗵 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗼𝗵𝗼𝗿𝘁 , 𝗘𝗻𝗿𝗼𝗹𝗹 𝗵𝗲𝗿𝗲- https://rzp.io/rzp/May2026