How to Streamline ETL Processes

After a decade in data engineering, I’ve seen hundreds of hours wasted developing on top of messy, unmaintainable code. Here’s how to make your code easy to maintain in just 5 minutes: 🚀 1. Create a Validation Script Before refactoring, ensure your output remains consistent. ✅ Check row count differences ✅ Validate metric consistency across key dimensions ✅ Use tools like datacompy to automate checks 🔄 2. Split Large Code Blocks into Individual Parts Refactor complex logic into modular components. 💡 Break down CTEs/subqueries into individual parts 💡 In Python, use functions 💡 In dbt, create separate models 🔌 3. Separate I/O from Transformation Logic Decouple data reading/writing from transformations. 🔹 Easier testing & debugging 🔹 Re-running transformations becomes simpler 🛠️ 4. Make Each Function Independent Your transformation functions should have no side effects. 🔑 Inputs = DataFrames → Outputs = DataFrames 🔑 External writes (e.g., logging) should use objects 🧪 5. Write Extensive Tests Tests ensure your pipelines don’t break with new changes. ✅ Catch issues before they hit production ✅ Gain confidence in refactoring 🔗 6. Think in Chains of Functions ETL should be a chain of reusable transformation functions. 💡 Modular functions = easier debugging, maintenance, and scaling Following these principles will save you hours of frustration while keeping your code clean, scalable, and easy to modify. What’s your biggest challenge with maintaining ETL pipelines? Drop it in the comments! 👇 #data #dataengineering #datapipeline

💥Your data pipeline is only as strong as its weakest assumption Even the most elegant data pipelines can break if you're not careful. I’ve broken more pipelines than I’d like to admit - and learned them the hard way. After years of building and scaling pipelines - especially at high-throughput environments like TikTok and my previous companies - I’ve learned that small oversights can lead to massive downstream pain. I’ve seen beautiful code break in production because of avoidable mistakes, let's see how to avoid them: ❌ 1. No Data Validation: ➡️ Do not assume upstream systems always send clean data. ✅ Add schema checks, null checks, and value thresholds before processing and triggering your downstreams ❌ 2. Hardcoding Logic ➡️ Writing the same transformation for 10 different tables? ✅ Move to a metadata-driven or parametrized ETL framework. Believe me, you will save hours. ❌ 3. Over-Shuffling in Spark ➡️ groupby, join, or distinct without proper partitioning - it's a disaster. ✅ Use broadcast joins instead, and monitor Exchange nodes in the execution plan. ❌ 4. No Observability ➡️ A silent failure is worse than a visible crash. ✅ Always implement logging, alerts, and data quality checks (e.g: row counts, null rates etc) ❌ 5. Failure to Design for Re-runs ➡️ Rerunning your job shouldn’t duplicate or corrupt data. ✅  Ensure that your logic is repeat-safe using overwrite modes or deduplication keys #dataengineering #etl #datapipeline #bigdata #sparktips #databricks #moderndatastack #engineering #datareliability #tiktok #data #dataengineering

🔄 Are you looking to streamline your ETL processes? Extract, Transform, Load (ETL) pipelines are essential for moving data from various sources into your data warehouse. Let’s see how Google Cloud Platform (GCP) can simplify this process using Dataflow. 🌐 Building ETL Pipelines with Dataflow ETL pipelines are crucial for transforming raw data into valuable insights, and GCP’s Dataflow offers a serverless and highly scalable solution for building these pipelines. Here’s how to effectively leverage Dataflow for your ETL needs: Key Benefits of Using Dataflow: 1. Serverless Architecture: Automatic Scaling: With Dataflow, you don’t need to manage servers. The service automatically scales resources based on the volume of data you’re processing, ensuring optimal performance without manual intervention. Cost Efficiency: Pay only for the compute resources you use. This can significantly reduce costs compared to traditional ETL solutions where you have to provision servers upfront. 2. Unified Programming Model: Stream and Batch Processing: Dataflow supports both stream and batch processing, allowing you to build pipelines that can handle real-time data as well as scheduled batch jobs seamlessly. Apache Beam SDK: Use the Apache Beam SDK for Dataflow to write your ETL pipelines in a simple and flexible manner. This allows you to focus on the data transformations rather than the infrastructure. 3. Integration with GCP Services: BigQuery: Load transformed data directly into BigQuery for analytics and reporting. Dataflow works seamlessly with BigQuery, enabling quick insights from your data. Cloud Storage: Use Cloud Storage as a staging area for raw data and intermediate results. Dataflow can easily read from and write to Cloud Storage, facilitating smooth data movement. 4. Data Transformation: Built-in Transformations: Utilize built-in transformations to simplify data cleaning, filtering, and enrichment processes, helping you get high-quality data into your data warehouse quickly. Custom Transformations: If needed, implement custom transformations using Java or Python to tailor the pipeline to your specific requirements. Error Handling: Implement error handling strategies to manage failures gracefully and ensure that your ETL processes are resilient. 💡 Pro Tip: Start with small, simple pipelines to understand Dataflow’s capabilities. As you gain confidence, you can scale up to more complex ETL workflows. 🗣️ Question for You: What challenges have you faced while building ETL pipelines, and how has GCP helped you overcome them? Share your experiences in the comments below! 📢 Stay Connected: Follow my LinkedIn profile for more tips on data engineering and GCP best practices: https://zurl.co/WYBY #ETL #Dataflow #GCP #DataEngineering #CloudComputing

When you perform the "E" part of ETL, try to filter as much as possible at the source. Way too often, I see this typical pattern in an ETL Spark Pipeline: 1. Select * from some table 2. Have Spark filter, dedupe, and transform the dataset 3. Load to destination When you follow that pattern, you can sometimes have severe costs on network I/O and just an overall very slow pipeline. If you instead update step 1 to apply your filters and dedupe the data, your pipelines will generally run faster and you won't incur the extra overhead from source. Additionally, if you are working with a source system that has a pricing model based on number of bytes scanned (e.g. Google BigQuery and AWS Athena), there is a quantifiable cost-out you can achieve by implementing your filters at the source. Very rarely do I perform step 1 without filters. I think maybe once or twice in my career, I had to do so, simply because the source system had a governor/WLM imposed on it that was very poorly implemented and throttled the CPU usage to a high degree. Thus, when applying things such as window functions to depupe the data or multiple joins would cause the query to bomb with a "max CPU exceeded" error message. Curious what others think on this? #dataengineering

How I Cut ETL Processing Time by 40% Using SQL + AWS Glue : At Citi, I am working on automating ETL for 1TB+ of customer data using AWS Glue and SQL-based transformation scripts. By optimizing SQL queries and scheduling with Airflow, we reduced processing time by 40%, unlocking faster insights for the business. If you're dealing with massive data and lagging ETL—SQL tuning + Glue = game changer. #SQL #ETL #AWS #DataEngineering #Analytics