Deep vs Shallow Clone in Delta Lake: A Databricks Tip

🧱 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐃𝐞𝐥𝐞𝐭𝐢𝐨𝐧 𝐕𝐞𝐜𝐭𝐨𝐫 𝐢𝐧 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬? Deletion Vectors are a smart storage optimization in Delta Lake. Instead of rewriting entire Parquet files when you DELETE, UPDATE, or MERGE rows Databricks simply marks them as deleted internally. 💡 Think of it like: Someone removed eggs from the fridge, but instead of throwing them out, they just added a sticker “Not edible. Ignore.” 🥚🚫 ⚙️ 𝐖𝐡𝐲 𝐮𝐬𝐞 𝐃𝐞𝐥𝐞𝐭𝐢𝐨𝐧 𝐕𝐞𝐜𝐭𝐨𝐫𝐬? ✅ Speeds up DELETE and MERGE operations ✅ Avoids rewriting large Parquet files ✅ Saves compute & I/O costs ✅ Great for large-scale Delta tables 📦 𝐖𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞𝐲 𝐬𝐭𝐨𝐫𝐞𝐝? Inside the Delta table’s _delta_log, and sometimes as bitmap files that mark which rows to hide all managed automatically by Delta Lake. ⚠️ 𝐓𝐡𝐞 𝐂𝐚𝐭𝐜𝐡? Over time, too many deletion vectors can slow reads because old data still physically exists. To clean it up: REORG TABLE my_table APPLY (PURGE DELETION VECTORS); VACUUM my_table; 💪 This sequence rewrites only live rows, removes obsolete data, and boosts performance. ⚡ 𝐖𝐡𝐞𝐧 𝐝𝐨 𝐭𝐡𝐞𝐲 𝐤𝐢𝐜𝐤 𝐢𝐧? Automatically when you: • DELETE rows • UPDATE rows • MERGE rows Fully supported in Unity Catalog and newer Delta Lake runtimes. 🔁 Share to help others prep for data interviews. For more content, follow Anuj Shrivastav💡📈 #Databricks #DeltaLake #DataEngineering #BigData #Spark #Lakehouse #ETL #AzureDatabricks #DataEngineerDiary

Delete Rows—Not Entire Files! Title: Databricks Deletion Vectors & Liquid Clustering: The Secret Sauce for Faster Delta Tables Link: https://lnkd.in/gwDwfasN Caption: Deleting 1 row shouldn’t rewrite a 2GB Parquet file. 😩 With Deletion Vectors, Delta Lake marks rows as deleted—no full rewrites. With Liquid Clustering, your large tables auto-optimize based on query patterns—no manual Z-Ordering! Result? 60%+ faster queries, lower DBU costs, and happier engineers. #DeltaLake #Databricks #BigData #DataEngineering #PerformanceOptimization #LiquidClustering #DeletionVectors #Spark

When working with large-scale data in Databricks Delta Lake, creating table copies is a common practice — whether for testing, development, or archiving. But did you know not all copies behave the same way? In Delta Lake, shallow copy and deep copy may sound similar, yet they differ drastically in performance, storage, and data isolation. Choosing the right one can save both time and cost in your data workflows. Want to understand which copy method best fits your use case? Read the full blog to learn more: https://lnkd.in/gQnaZvTQ #Databricks #DeltaLake #DataEngineering #BigData #DataManagement #DeepCopy #ShallowCopy #CloudData #DataPerformance #Lakehouse #ETL #DataArchitecture William Rathinasamy Sekhar Reddy Anuj Kumar Sen Lawrance Amburose Satya Srinivas Veerabadrachari R Brindha Sendhil Rashika S Praveen Kumar C Parthiban Raja Mallikharjuna Reddy Meka

Recently explored Liquid Clustering and Deletion Vectors in Databricks, and it’s impressive how these features improve both performance and cost efficiency. Liquid Clustering helps data organize dynamically for faster queries, while Deletion Vectors make record deletions lightweight by marking rows as invisible instead of rewriting files. Together, they make Delta Tables smarter, cleaner, and more scalable. #Databricks #DeltaLake #DataEngineering #BigData #DataOptimization #DataPerformance #DeletionVectors #LiquidClustering #CloudData #DataProcessing #AzureDatabricks #ETL #DataStorage #ModernDataStack #DataAnalytics #DataPipeline #DataOps #TechLearning #CloudComputing #Spark #DataTransformation #Lakehouse #SQL #DataInnovation #DataPlatform

Recently, I started diving deeper into Databricks, and one thing that really stood out to me is Delta Lake — it’s not just another storage layer, it’s a complete upgrade for data reliability and performance. 🚀 Here’s what makes it so powerful 👇 ✅ ACID Transactions — no more half-written data or broken pipelines. ✅ Schema Enforcement — keeps your data clean and consistent. ✅ Time Travel — yes, you can literally query your data’s history! 🕒 It’s amazing how Delta Lake combines the flexibility of a data lake with the reliability of a warehouse — making it perfect for production-grade ETL pipelines. I’m still exploring its full potential, but honestly, this feels like a must-know concept for every Data Engineer. 💪 Have you worked with Delta Lake before? What’s your favorite feature? Let’s discuss 👇 #Databricks #DeltaLake #DataEngineering #ETL #DataAnalytics #DataLakehouse #LearningJourney #DataCommunity #LearningEveryday #CareerGrowth #DataCareer #GrowthMindset #KeepLearning #Upskilling #DataCommunity #ContinuousLearning #JobSearch

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻’𝘁 𝗸𝗻𝗼𝘄 𝘁𝗵𝗶𝘀 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗦𝗤𝗟 𝘁𝗿𝗶𝗰𝗸 You can use 𝗠𝗘𝗥𝗚𝗘 in Databricks (Delta Lake) to update and insert data but did you know you can do it 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗹𝗶𝘀𝘁𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆 𝗰𝗼𝗹𝘂𝗺𝗻? No need for long UPDATE SET col1 = s.col1, col2 = s.col2... statements. If your source and target tables have the same column names, Databricks lets you do this: ✅ UPDATE SET * → automatically updates all matching columns 𝗯𝘆 𝗻𝗮𝗺𝗲 ✅ INSERT * → automatically inserts all columns 𝗯𝘆 𝗻𝗮𝗺𝗲 No manual mapping. No typos. No headaches. It’s small tricks like this that make Databricks SQL feel powerful and efficient — especially when you’re maintaining 100+ merge statements in production pipelines. 👉 Have you tried using UPDATE SET * yet in your Delta tables? It’s a game changer for clean, schema-aligned merges. #Databricks #DeltaLake #SQL #DataEngineering #Spark

“Why rewrite the whole file when you just deleted one row?” That’s exactly what Deletion Vectors in Databricks were built to fix. When you run a DELETE, UPDATE, or MERGE on a Delta table, Databricks traditionally rewrites the entire Parquet file containing that record — slow and costly. Deletion Vectors change the game. Instead of rewriting, Databricks simply marks the deleted or updated rows as “logically removed” — like tagging them “ignore this” instead of throwing the whole file away. Why It’s Powerful Much faster DELETE / UPDATE / MERGE operations Lower compute cost (no massive file rewrites) Ideal for large Delta tables with frequent updates Caution Too many deletion vectors over time can slow down reads — so remember to periodically run OPTIMIZE or VACUUM to compact your data and clean old records. In One Line “Deletion Vectors let Delta Lake skip rewriting files by tracking deleted rows — boosting performance and efficiency for modern data lakes.” #Databricks #DeltaLake #DataEngineering #BigData #Optimization #Spark #CloudComputing

Boosting Delta Lake Performance with Deletion Vectors If you’ve ever run a DELETE or UPDATE on a large Delta table, you probably noticed one thing — it’s slow. That’s because traditionally, even deleting a single row required rewriting entire Parquet files. Enter Deletion Vectors (DVs) — a game changer introduced in Delta Lake 2.3+ and Databricks Runtime 13.0+. 🔹 What are they? Deletion Vectors act like a “mask” that marks specific rows as deleted without physically rewriting the data files. 🔹 Why it matters: ⚡ Faster DELETE, UPDATE, and MERGE operations 💾 Less I/O and storage overhead 🔁 Perfect for streaming and CDC use cases 🧹 Physical files are only rewritten during VACUUM or compaction 🔹 How it works: Instead of deleting rows directly from Parquet files, Delta creates a lightweight .dv file that tracks which rows are deleted — the data appears gone to queries, but the file itself remains untouched. 🔹 Enable it: ALTER TABLE my_table SET TBLPROPERTIES ( 'delta.enableDeletionVectors' = 'true' ); A small change — but a huge performance win for data engineers working with massive Delta tables. #DataEngineering #DeltaLake #Spark #Databricks #BigData #Optimization

🚨 Data Quality Isn’t Optional — It’s Engineered. 🔍 Let’s talk about constraints in Databricks and why they matter more than ever. In the world of Delta Lake, constraints aren’t just metadata — they’re your first line of defense against bad data. 🧱 Databricks supports two types of constraints: - ✅ Enforced Constraints: - NOT NULL: No more silent nulls sneaking into your pipeline. - CHECK: Custom logic to validate each row — think of it as SQL-powered gatekeeping. - 📎 Informational Constraints: - PRIMARY KEY and FOREIGN KEY: Not enforced, but crucial for lineage, documentation, and downstream tools like dbt. 💡 Pro tip: If you're using Lakeflow Declarative Pipelines, you can take this further with expectations — declarative data quality rules that scale with your workflows. 🔗 Constraints aren’t just about prevention — they’re about trust. When your data platform enforces integrity at the source, your analytics, ML models, and business decisions become bulletproof. 👊 Let’s stop treating data quality as a postmortem. Start engineering it into the foundation. #Databricks #DeltaLake #DataEngineering #DataQuality #dbt #Lakehouse #SQL #BigData #TechLeadership