Databricks cost issues start in user land, not the platform

Table Triggers in Databricks: In our Databricks workflows, we have always scheduled jobs on timers, even when no new data arrived. Wasteful and not very data aware. We recently tested Table Triggers in Databricks and they are pretty efficient for event driven pipelines. Scenario: The ingestion team is responsible for bringing data into the Unit Catalog and building the staging layer, while the Lakehouse team manages the bronze, silver and gold layers. As these responsibilities are divided between teams, it is not feasible to orchestrate the entire process, from ingestion to the gold layer, within a single workflow. Time based scheduling is not always efficient, as the runtime of ingestion jobs can vary and is not always predictable. Any change in the ingestion schedule requires the Lakehouse team to update their schedules as well. Table Triggers therefore present a more reliable and flexible solution for this scenario. What we did: 1. In our Databricks job configuration, under Schedules & Triggers, we selected Table update as the trigger type. 2. Selected tables on which transformation jobs are dependent on. 3. We configured “Minimum time between triggers” to avoid running the job too frequently. 4. Then we set “Wait after last change” to have a short breather, making sure all table updates finish before the transformation job runs Our key takeaways: 1. Jobs fire only when there is actual change 2. Lower compute costs + leaner orchestration 3. Closer coupling between data changes and downstream updates, perfect for incremental pipelines (bronze > silver > gold) Things to keep in mind: 1. You can have up to 1000 table update triggers per workspace. 2. You can select up to 10 managed or Delta tables per trigger. #Databricks #DeltaLake #DataEngineering #EventDriven #Lakehouse

Day 2 of my #30DaysOfDatabricks Challenge — Databricks Architecture Deep Dive Yesterday, we explored what Databricks is and why it matters in modern data engineering. Today, let’s look under the hood and understand how Databricks actually works. The Databricks Architecture is designed to simplify data operations by combining data lakes and data warehouses into a single Lakehouse platform — built on top of Apache Spark. Here’s how the core layers connect: 🧱 1. Storage Layer: Databricks uses cloud storage (ADLS, S3, or GCS) to store all raw and processed data in open formats like Parquet and Delta Lake. ⚙️ 2. Compute Layer: This layer runs on Apache Spark clusters that scale automatically based on your workload. It executes transformations, queries, and machine learning workloads efficiently. 🧩 3. Control Layer: This is where Databricks adds intelligence — managing clusters, jobs, users, permissions, and notebooks. It ensures reliability, governance, and seamless collaboration. 💡 Together, these layers make Databricks: Highly scalable Unified for multiple data roles (Engineer, Analyst, Scientist) Secure and collaborative Tomorrow’s topic — Day 3: Setting Up Databricks and Exploring the Workspace UI, where we’ll go hands-on. If you’re learning Databricks too, save this post for reference and follow along on the journey. #30DaysOfDatabricks #DatabricksArchitecture #DataEngineering #LakehouseArchitecture #DeltaLake #ApacheSpark #BigData #AzureDatabricks #DataPipeline #PySpark #DataPlatform #CloudDataEngineering #DataAnalytics #LearnDatabricks #MisterBanerjee #TechLearning #LinkedInLearningChallenge

Getting Started with Databricks: Why It’s a Game-Changer for Data Engineering As data engineers, we often deal with multiple tools — data lakes for raw storage, data warehouses for analytics, and complex pipelines to keep them in sync. This usually leads to fragmented systems and a lot of operational overhead. That’s where Databricks comes in — a unified data platform built on the Lakehouse architecture. It combines the flexibility of a data lake with the performance and structure of a data warehouse — all in one place. 💡 Here’s what makes Databricks powerful: 🔄 Unified Platform: Ingest, process, and analyze data in a single environment. ⚡ Delta Lake: Brings ACID transactions and schema enforcement to your data lake. 🤝 Collaboration: Notebooks let data engineers, data scientists, and analysts work together seamlessly. 🧠 Scalability: Built on Apache Spark — easily handle massive datasets without worrying about infrastructure. In short, Databricks empowers teams to move from data chaos to data clarity. Have you tried working with Databricks yet? What’s your favourite feature so far? #Learning #Databricks #DataEngineering #DeltaLake #BigData #Lakehouse #PySpark

“The Day Databricks Finally Clicked 🧠✨” When I first opened Databricks, I’ll be honest… 👉 It looked powerful. 👉 It looked fast. 👉 But it also looked intimidating 😅 Clusters, notebooks, Delta Lake, lakehouse — Everything sounded cool, but I didn’t really get it. That changed during one overnight project 👇 We had to process hundreds of millions of rows from multiple sources. The SLA was tight. Transformations were complex. And our existing setup was already struggling. It was either learn Databricks properly — or miss the deadline. And that night, everything started to make sense 👇 🔸 1️⃣ Clusters — The Engine Room Once we set up job clusters with autoscaling, suddenly heavy jobs were flying through. No more resource bottlenecks. 🔸 2️⃣ Notebooks — My New Workspace Combining SQL and PySpark inside version-controlled notebooks was a game changer. Debugging finally felt human again. 🔸 3️⃣ Delta Lake — The Quiet Hero ACID transactions + schema enforcement = reliability. Our “messy lake” turned into a clean data foundation overnight. 🔸 4️⃣ Jobs — The Automation Layer Scheduling + retries + ADF orchestration made the pipeline run like clockwork. 🔸 5️⃣ Lakehouse Architecture — The Big Shift Bronze → Silver → Gold layers. No data duplication. No unnecessary movement. Just clean, structured performance. That night, for the first time: ✅ Pipelines ran faster ✅ Data quality issues dropped ✅ No 3AM dashboard complaints 😎 💡 My takeaway: 👉 The day you truly understand the core concepts, Databricks stops being “just another tool.” It becomes a superpower for data engineers 🦸♂️ 👉 Tomorrow: ETL/ELT Pipelines in Databricks — Real-World Patterns & Lessons Learned ⚡ #Azure #Databricks #AzureDatabricks #DataEngineering #MicrosoftAzure #BigData #CloudComputing #DeltaLake #Lakehouse #DataPipelines #DigitalTransformation #ETL

🚀 Day 1 of my #30DaysOfDatabricks Challenge begins! As data engineers, we constantly deal with data ingestion, transformation, and analytics across multiple systems. Managing these workflows efficiently and at scale is where Databricks truly stands out. 💡 Databricks is a unified data analytics platform that brings together: 🧱 Data Engineering 🧠 Data Science 🤖 Machine Learning 🤝 Collaborative Analytics All in one workspace — powered by Apache Spark. It enables teams to handle large-scale data, automate pipelines, and extract meaningful insights — while ensuring reliability and scalability through the Lakehouse architecture (a blend of Data Lake and Data Warehouse). 📅 In this 30-day challenge, I’ll be exploring Databricks step by step — from workspace setup to building a complete ETL pipeline using Delta Lake, PySpark, and Azure integration. 🎯 Today’s Focus: ✅ Understanding what Databricks is ✅ Exploring its benefits and architecture ✅ Getting familiar with its key components 📘 Stay tuned for Day 2: Databricks Architecture Deep Dive — where we’ll understand how Databricks orchestrates data processing at scale. If you’re also learning Databricks, feel free to join me on this journey. Let’s grow together as data professionals. #30DaysOfDatabricks #DataEngineering #Databricks #PySpark #AzureDatabricks #DataEngineerJourney #BigData #DataAnalytics #LakehouseArchitecture #DataTransformation #DeltaLake #ApacheSpark #LearnDatabricks #DataPipeline #DataCommunity #AzureData #DataEngineer #TechLearning #MisterBanerjee #LinkedInLearningChallenge

⚡️ foreach vs foreachPartition in Databricks: The Hidden Performance Lever Most Spark developers know about foreach. Fewer truly understand the power of foreachPartition. And that difference can make or break your Databricks workload. --- 🔍 The Core Difference - foreach → Executes your function once per row. 👉 Great for lightweight operations, but can be painfully slow when you’re writing to external systems (e.g., databases, APIs). - foreachPartition → Executes your function once per partition. 👉 You get a batch of rows at a time, which means fewer connections, less overhead, and massive performance gains. --- 💡 Example in Databricks Imagine writing 100M records to an external database: - With foreach: 100M function calls → 100M connections. - With foreachPartition: 200 partitions → just 200 function calls. That’s the difference between hours of runtime and minutes. --- 📊 Real-World Impact At one client, switching from foreach to foreachPartition for a streaming sink reduced job runtime from 9 hours → 45 minutes. Same code logic. Different function. Huge savings. --- 🌟 Takeaway - Use foreach when you need row-level operations. - Use foreachPartition when writing to external systems or doing heavy I/O. - Always think: Do I need to operate per row, or per partition? --- 💬 Curious to hear: Have you ever optimized a Spark job just by changing this one function? #Databricks #ApacheSpark #BigData #DataEngineering #PerformanceOptimization

Nothing like visual decision trees to take a decision. Mariusz Kujawski here proposed a way to decide which type of cluster to choose when creating an architecture.

Selecting the Right Compute in Databricks – My Decision Tree Summary I noticed that my last post about decision trees got a lot of attention, so I decided to create another (hopefully useful!) decision tree — this time on selecting the right compute. When I work with customers, one of the most common questions I get is: “Which compute should we use?” And, as always, it depends. To make this decision easier, I put together a Decision Tree for Compute Selection to guide you through the main options: ✅ Personal Compute – Perfect for individual development, exploration, and testing. ⚙️ Shared Compute – Great for collaborative development and exploration. ⏱️ Job Clusters – Optimized for scheduled ETL jobs, production pipelines, and automation. 💡 Serverless SQL & Warehouses – Ideal for instant BI queries and ad-hoc analytics, fully managed by Databricks. The goal: To make compute selection simpler, faster, and more transparent, so teams can focus on what really matters. I’d love to hear how you approach compute selection in your Databricks projects! 👇 #Databricks #DataEngineering #DataEngineerDiary #Compute #DataIngestion #Lakehouse

Running a Databricks platform is one thing. Running it efficiently is another. 🚀   A poorly designed architecture leads to slow queries, spiraling costs, and governance headaches. We just published a comprehensive technical guide on Databricks Architecture Best Practices to fix that.   Our guide covers the critical pillars for a high-performance Lakehouse: 🔹 Performance: Scaling, query optimization, and runtimes. 🔹 Cost: Using job clusters, spot instances, and auto-scaling. 🔹 Governance: Implementing Unity Catalog and RBAC. 🔹 Operations: CI/CD, monitoring, and the Medallion architecture.   Stop guessing and start building a truly AI-ready data platform. Read the full guide here: https://lnkd.in/e5d7Fz24   #Databricks #DataArchitecture #Lakehouse #DataEngineering #BigData #UnityCatalog #Dateonic

⚡ Handling Schema Drift in Databricks Pipelines — The Silent Failure Catcher One of the most underestimated challenges in data engineering is schema drift — when source files suddenly gain, lose, or rename columns. It looks small… until your pipeline fails at 2 AM 😅 🔹 Why It Happens • Upstream teams add or remove fields • File headers change unexpectedly • Nested JSON structures evolve silently 🔹 How Databricks Helps • mergeSchema automatically evolves Delta table structures • Autoloader detects schema changes and updates metadata dynamically • Schema inference logs reveal drift before it breaks pipelines 🔹 Best Practices ✔ Maintain a schema registry or metadata version table ✔ Enable evolution only when absolutely needed — uncontrolled drift = hidden data quality issues ✔ Set alerts for column count or datatype changes 💡 Takeaway: Schema drift isn’t a bug — it’s proof your data is evolving. The key is designing pipelines that adapt intelligently without losing governance or trust. #Databricks #PySpark #DeltaLake #DataEngineering #BigData #ETL #Azure