The Modern Data Stack is amazing... and a bit of a mess. Every week, I come across a new “must-have” data tool, a shiny connector here, a new orchestration layer there. Don’t get me wrong, I love the modern data stack. dbt, Snowflake, Airbyte, Dagster, all of them have made our lives as data engineers much better. But at some point, you start realizing something: 👉 Simplicity scales better than hype. We spend weeks evaluating tools that promise “no-code” transformations or “self-healing” pipelines, but then we still end up debugging JSON configs at 2 AM. Here’s what I’ve learned after a few projects in 2025: Focus on data contracts early, broken schemas cause 80% of downstream pain. Metadata and lineage aren’t optional anymore, they save hours of blame-game debugging. Don’t build a Ferrari when a reliable Toyota (simple ELT to a warehouse) does the job. A smaller, well-understood stack always beats a “trendy” one. The modern data stack isn’t bad, it’s just growing up. And maybe the next evolution isn’t “more tools”… but more discipline. Curious to hear from others. What’s one “fancy” data tool you stopped using because it overcomplicated things? #DataEngineering #ModernDataStack #ELT #DataPipelines #AnalyticsEngineering

Big news in the data engineering world today! Everyone’s talking consolidation. Matillion's evolving. I'm not a data engineer, and I just built an end to end Snowflake pipeline for retailers to understand their customers. My prompt was "Build a customer intelligence and product affinity pipeline." In Fivetran: Configure 3 connectors, set up schedules, pay per row. Then, in DBT: Hand code 5+ SQL files, debug, define dependencies in YAML, run to execute. With Maia, it's minutes. Combining tools doesn’t solve the delivery bottleneck. It just consolidates it. We're redefining data engineering for what’s coming next.

Our Data Science Agent turns Databricks Assistant into an autonomous partner for data science and analytics. Fully integrated with Databricks Notebooks and the SQL Editor, our Data Science Agent accelerates analysis by exploring data, running and fixing code, training models, and summarizing insights — all from a single prompt. See how to get started: https://lnkd.in/gFBh7VSh

🌟 New Blog Just Published! 🌟 📌 Docker for Data Science: Why It’s a Game-Changer 🚀 ✍️ Author: Hiren Dave 📖 In the rapidly evolving landscape of data science , the gap between a prototype that dazzles on a laptop and a production system that runs reliably on a cluster is widening. Docker bridges that gap...... 🕒 Published: 2025-10-18 📂 Category: Tech 🔗 Read more: https://lnkd.in/dd_Qkt2j 🚀✨ #dockerdatascience #reproducibledatasc #datasciencecontain

🚀 Master Spark Like a Pro — From DAG to Optimization! Ever wondered how Spark actually transforms your code into parallel execution magic? Let’s break it down 👇 ✨ Driver is the brain — it plans, schedules, and manages. ⚙️ Executors are the muscles — they execute tasks in parallel. 🧩 Each transformation — whether narrow or wide — decides how efficiently your data moves across partitions. But here’s the real power tip 💡 ✅ Avoid shuffles whenever possible. ✅ Optimize partitions with repartition() or coalesce() wisely. ✅ Keep an eye on skewness — one bad partition can crash your performance! 🔍 Remember: A good Spark engineer doesn’t just write transformations — they engineer performance. I recently explored a detailed Spark guide covering everything — from Schema inference to Adaptive Query Execution (AQE) and Data Skew Optimization — and it’s a goldmine for anyone serious about Data Engineering. #DataEngineering #ApacheSpark #BigData #ETL #PerformanceTuning #PySpark #LearningJourney

Quick Spark Cache Tip for Faster Iterations 🔄 Senior Data Engineers, iterating on ML models? Cache your DataFrame after heavy transforms to avoid recomputing – it’s a game-changer for dev cycles. Simple win: df_transformed = df.filter(...).withColumn(...) # Your ETL magic df_transformed.cache() # Persist in memory df_transformed.count() # Triggers caching Uncache when done: df_transformed.unpersist(). In Databricks, watch the UI for eviction stats. Saves hours! ⏱️ Caching saved your day? Share! 👇 #DataEngineering #ApacheSpark #Caching #BigData #Databricks #SeniorEngineer #ETL #Performance #TechTips #Analytics

Ok, this is by far the coolest thing Databricks has ever released. Think Cursor—but purpose-built for data scientists—with governed data context and enterprise control via Unity Catalog. The Data Science Agent in Databricks Assistant plans, writes, runs, and fixes code from a single prompt, orchestrating EDA, modeling, and iterative error repair directly in notebooks and the SQL Editor. For Startup data teams, this is everything you want from an agentic IDE—plus your data’s governance and context—compressing hours into minutes for EDA, forecasting, churn modeling, etc. Super exciting! https://lnkd.in/estZepYY

Day 30: Metadata-Driven Pipelines – Working Smarter, Not Harder 🚀 In data engineering, the challenge isn’t just moving data — it’s building pipelines that adapt. Metadata-driven pipelines help us achieve exactly that: They read metadata (like table names, schemas, and source locations) instead of hardcoding everything. This allows scalable, reusable, and flexible pipelines. Adding a new table or source? Often, no code change is needed — just update the metadata. Think of it as giving your pipeline a map and compass instead of writing directions for every single journey. 🗺️ Metadata-driven design = less manual work, fewer errors, faster delivery. #DataEngineering #Databricks #ETL #MetadataDriven #Automation #Spark

⚙️ How I Built My First Data Pipeline (and Messed Up Along the Way) When I built my first real data pipeline, I thought it would be easy: Read data → clean it → store it. Yeah… not really. 😅 I used PySpark, S3, and Airflow, ran it locally first, everything looked perfect… until I moved it to EMR and Airflow. Here’s what I learned the hard way: 💥 One Giant Output File: Spark took forever to write huge data. Lesson: Partition by date or region. ⚠️ No Trigger Check: DAG ran daily assuming new data existed. One day, the API didn’t refresh. Lesson: Always check before running — trigger files in S3 save headaches. 🧠 Overcomplicating Airflow: Multiple branches, conditions, retries… nightmare to debug. Lesson: Keep it simple first. ✅ Key Takeaways: Build small steps, add sanity checks & alerts, and test with sample data first. 💡 What about you? Have you ever messed up a pipeline or project and learned something valuable? Drop your story in the comments — I’d love to hear! 🔗 Read the full story on Substack: [https://lnkd.in/gMqYQKTK] #dataengineer #TechJourney #GrowWithPraneeth #Data

Why 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗔𝘂𝘁𝗼 𝗟𝗼𝗮𝗱𝗲𝗿 Is My Go-To for Scalable Ingestion If you're managing data pipelines with millions of files, stop manually tracking what’s been processed—seriously. Databricks Auto Loader is a total game-changer. It automatically detects and ingests new files (#CSV, #JSON, #Parquet, Avro—you name it) with minimal configuration. No more brittle scripts or manual file tracking. 🛠️Features That Make My Life Easier:- - ✅ File Type Filtering  Even if you don’t control the source folder, Auto Loader lets you ingest only the formats you care about. Say goodbye to noisy data. - ✅ Glob Pattern Directory Filtering  Dynamically read from multiple subfolders—no hardcoded paths, no headaches. - ✅ cloudFiles.cleanSource` Options  Keep your landing zone tidy with flexible cleanup modes:    - OFF: Leave files untouched    - DELETE: Remove after retention    - MOVE: Archive to another path 💬 Have you used Auto Loader in production? What’s your favorite feature or use case? Let’s trade notes! #Databricks #AutoLoader #BigData #DataEngineering #CloudComputing #Spark #ETL #DataPipeline #DataEngineer #DataFactory #Big4