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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

Databricks Tip of the Day: Deep vs Shallow Clone in Delta Lake Understanding the difference between deep and shallow clones in Delta Lake can save you both time and storage costs when working with table copies. - 🔄 Deep clone copies all data files and metadata to create a fully independent table - ⚡ Shallow clone references source data files instead of copying them, making it much faster and cheaper (useful for replicating data for testing) - 📌 Both clone types maintain independent metadata and history from the source table - 🎯 Shallow clones are great for short-term experiments or testing, while deep clones are better for archival or when you need complete independence Deep clones are more expensive to create because they copy all the data, but they're completely independent of the source table. Shallow clones are fast and cheap since they just reference the source files, but they depend on those files remaining available. You can create clones at specific versions or timestamps, which is really useful for reproducing results or analyzing historical data states. More on the Clones - https://lnkd.in/d8k5G4uB #Databricks #DeltaLake #DataEngineering

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

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

🧱 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

“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

I learned today, in #data systems, performance isn’t just about volume; it’s about structure!! In #SQL, indexing and join design define response time! In #Spark, partitioning and file layout drive shuffle efficiency. In #Databricks, liquid clustering and data skipping shape query speed. In #graph #databases, relationship depth and direction control traversal cost. In #NoSQL, key and document design decide how well data scales. No matter the technology; how you model and organize data determines how well you can use it! #dataengineering

🚀 𝗦𝗽𝗮𝗿𝗸 𝗦𝗤𝗟 𝗣𝗿𝗼-𝗧𝗶𝗽: 𝗦𝘁𝗼𝗽 𝘁𝗵𝗲 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝗮𝗺𝗲𝗱 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀! If you're using multiple window functions (like ROW_NUMBER(), LAG(), or aggregates) with the exact same partitioning, ordering, or framing logic, don't define it repeatedly! Use the 𝗡𝗮𝗺𝗲𝗱 𝗪𝗜𝗡𝗗𝗢𝗪 clause in Spark SQL to name your window specification. This makes your code cleaner, more readable, and far less repetitive. This trick is essential for writing efficient, readable, and maintainable data transformation pipelines. Don't skip it! What are your favorite Spark SQL efficiency hacks? Share below! 👇 #DataEngineering #SoftwareEngineering #DataOps #Spark #DataBricks

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

Got a storage problem? Your PySpark code might be too 'inflated'! A crucial part of Data Engineering is optimizing the storage of your data lake. For Parquet files, choosing the right compression codec is a must. Here's a quick demo showing the difference: •Snappy \rightarrow Fast and efficient. •Gzip \rightarrow Maximum compression, but slower. •None \rightarrow Danger! Use only when absolutely necessary. Which compression codec do you use most often in your production pipelines and why? Let me know in the comments! 👇 #DataEngineering #PySpark #BigData #CloudComputing #PerformanceTuning