Ezz-Eldin S.’s Post

Deletion Vectors in Databricks | Efficient DELETE, UPDATE & MERGE at Scale Deletion Vectors are a Delta Lake storage optimization in Databricks that significantly improve the performance of DELETE, UPDATE, and MERGE operations—especially when dealing with many small changes(DML Operations). Instead of rewriting large Parquet files, Databricks uses soft deletes by tracking removed rows separately. 🔍 How Deletion Vectors Work 1️⃣ Soft Deletion Deleted or updated rows are marked in small deletion vector files (similar to a bitmap) instead of rewriting the original Parquet file. 2️⃣ Merge-on-Read (MoR) During query execution, Databricks combines: • Base Parquet data • Deletion vectors • Delta transaction log to return correct results. 3️⃣ Lazy Physical Deletion Actual file rewrites happen later during: • OPTIMIZE → file compaction • VACUUM → permanent cleanup ⚡ Key Benefits ✅ Faster DELETE / UPDATE / MERGE ✅ Reduced file rewrites and I/O ✅ Lower storage and compute cost ✅ Ideal for GDPR / PII deletes ✅ Easier rollback using Delta transaction history ⚙️ Usage & Configuration • Enable using table property: delta.enableDeletionVectors = true • Recommended runtime for writes: DBR 14.3 LTS+ • Read support: DBR 12.2 LTS+ • Requires periodic OPTIMIZE / VACUUM for cleanup #AzureDatabricks #ApacheSpark #PySpark #SparkSQL #DeltaLake #DataEngineering #BigData #PerformanceOptimization #AzureDatabricks #Databricks #ADB #Spark #PerformanceTuning #SparkSql #AzureDataEngineer #SparkPerformance #QueryOptimization #DataOptimization #BigDataPerformance #DistributedComputing #ScalableSystems #TechLearning #DailyLearning #DataEngineerLife #LearningInPublic #LinkedInTech #DataCommunity #LiquidClustering #Lakehouse #DataArchitecture #CloudData #ModernDataStack #DeletionVectors

Day 7: How to Copy 1TB of Data in 10 Seconds (Shallow Clones) One of the biggest bottlenecks in Data Engineering? Setting up a proper Test Environment. 🚧 We’ve all been there: You need to debug a failure that only happens on "Production Data," but copying the full 1TB production table to a Dev environment takes hours and doubles your storage bill. So, you test on a tiny sample... and then the code breaks in Prod anyway. 🙃 The Databricks Solution: Shallow Clones. Instead of copying the physical data files, a Shallow Clone just copies the metadata (the transaction log). It points to the existing files but creates a new, independent table history. Why is this a superpower? 1. Speed: You can clone a Petabyte-scale table in seconds. 2. Cost: It costs $0 in extra storage (until you start modifying the clone). 3. Safety: You can run destructive updates (DELETE, MERGE) on the clone. It will write new files for the changes, leaving the original Production table completely untouched. The Magic Command: CREATE TABLE dev.sales_test SHALLOW CLONE prod.sales_gold; That’s it. You now have a full-scale sandbox to break things in, without the wait or the bill. Reflection: How does your team handle "Prod vs. Dev" data syncs? Do you clone, sample, or just pray? 🙏 #Databricks #DeltaLake #DevOps #DataEngineering #Testing #SQL #BigData #Azure #Efficiency #Optimization #Spark #Pyspark

How to Copy 1TB of Data in 10 Seconds (Shallow Clones) One of the biggest bottlenecks in Data Engineering? Setting up a proper Test Environment. 🚧 We’ve all been there: You need to debug a failure that only happens on "Production Data," but copying the full 1TB production table to a Dev environment takes hours and doubles your storage bill. So, you test on a tiny sample... and then the code breaks in Prod anyway. 🙃 The Databricks Solution: Shallow Clones. Instead of copying the physical data files, a Shallow Clone just copies the metadata (the transaction log). It points to the existing files but creates a new, independent table history. Why is this a superpower? 1. Speed: You can clone a Petabyte-scale table in seconds. 2. Cost: It costs $0 in extra storage (until you start modifying the clone). 3. Safety: You can run destructive updates (DELETE, MERGE) on the clone. It will write new files for the changes, leaving the original Production table completely untouched. The Magic Command: CREATE TABLE dev.sales_test SHALLOW CLONE prod.sales_gold; That’s it. You now have a full-scale sandbox to break things in, without the wait or the bill. Reflection: How does your team handle "Prod vs. Dev" data syncs? Do you clone, sample, or just pray? 🙏 #Databricks #DeltaLake #DevOps #DataEngineering #Testing #SQL #BigData #Azure #Efficiency #Optimization #Spark #Pyspark

#databricks_basics_48 🚀 How Delta Lake Enables ACID Transactions in Databricks Building reliable data pipelines requires strong transactional guarantees — and Delta Lake delivers exactly that. Here's a quick breakdown 👇 🔒 Atomicity – All or Nothing Every operation is broken into commits recorded in the Delta Log. A commit is added only when all actions succeed, ensuring no partial writes. 📏 Consistency – Always Valid State Delta Lake enforces strong schema checks, ensuring tables remain in a valid state after every transaction. 🔀 Isolation – Safe Concurrent Writes Uses optimistic concurrency control, where each write checks for conflicting updates before committing — preventing data corruption even under heavy concurrency. 💾 Durability – Changes That Stick Committed changes are written to underlying Parquet files and logged in the Delta Log, ensuring permanence even after failures. 📁 Delta Log – The ACID Backbone The hidden _delta_log stores ordered JSON commit files that act as the single source of truth for the latest table state. ⚙️ Databricks Enhancements Databricks layers ACID over cloud storage using transactional commits (_started_ & _committed_ metadata files) and provides snapshot isolation for reads + write‑serializable isolation for writes. 🔥 Bottom line: Delta Lake transforms your data lake into a trustworthy, transactional system, making analytics and ML pipelines more robust and reliable. You might remember from my earlier post that this Delta Logs itself also enables Databricks features such as time travel - the point being, delta log is backbone to databricks ecoosystem #DeltaLake #Databricks #ACIDTransactions #DataEngineering #BigData #DataLakehouse #OptimisticConcurrency #DataReliability #DataConsistency #Parquet v4c.ai#TransactionalData #DistributedSystems #DataPipelines #AnalyticsEngineering #LakehouseArchitecture #Spark #DataOps #ETL #MachineLearningPipelines #EnterpriseData

Databricks offers a ton of features and is adding more almost daily. However, I still have a wishlist of things I'd love to see: 1. ABAC GRANT and DENY I love tags in Databricks and ABAC provides a ton of value when used right. But currently ABAC only covers Row Level Filtering and Column Masking. Having this on actual securables as either a GRANT or DENY would be huge. 2. Tagging support for more securables Tags are amazingly powerful and will only continue to grow in importance. For best leverage, it's critical that more securables can be tagged. Currently Vector Search Indexes and Endpoints aren't covered yet. 3. More granular Vector Search Index ACLs The Vector Search Index is a powerful tool, but currently you can only share it while also allowing others to change it. A READ ONLY or USE ONLY permission would be great. 4. Serverless Base Environment via API The GUI isn't working well yet, but having this capability over the SDK would be valuable from a platform management perspective. 5. UNDROP for Schemas and Catalogs There's UNDROP TABLE, but once you drop a schema with managed tables, you need to call support. An UNDROP SCHEMA would address these difficult woopsies. 6. VIEW_PERMISSIONS permission To see permissions on a securable you don't own, you need MANAGE. From an operational standpoint, it would be better to view ALL permissions without MANAGE access—just to validate. 7. Role Based Exclusive Access. Some workspaces are now made to address the issue that people can be part of teams where data cannot be combined. In this case Role Based Exclusive Access is brilliant. What's on your Databricks wishlist? #Databricks #UnityCatalog #DataEngineering #DataPlatform #ABAC #DataGovernance #DataSecurity #CloudData #BigData #DataArchitecture

Your Databricks pipeline says "Running" for 4 hours. It should take 20 minutes. Now what? I've been there more times than I want to admit. The pipeline works. The data looks right. But something is burning through cluster hours and nobody can figure out why. Here's the thing about Spark optimization guides: they tell you about partitioning, broadcast joins, and caching. All true. All important. But they don't tell you how to actually debug when none of that obvious stuff is the problem. The real issue I kept hitting: Data skew that didn't show up until production scale. Your test data? Perfect. Your prod data? One partition has 80% of the records and Spark is waiting for that one slow task while 200 cores do nothing. What actually worked: Stop trusting the Spark UI's first page. Go deep into the stage details. Find the task taking 10x longer than the others. Look at the input size. That's your skew. Then, and this took me way too long to figure out, salt your join keys. Add a random suffix to break up the hot partition, do the join, then deduplicate. Ugly? Yes. Effective? Absolutely. One pipeline went from 4.5 hours to 18 minutes. Same logic. Same cluster. Just redistributed the work so Spark could actually parallelize it. The lesson: Databricks is incredible, but it's not magic. It can only parallelize work that's parallelizable. If your data distribution is broken, no amount of cluster scaling will save you. Now when I build pipelines, I don't just think "does this work." I think "will this partition evenly at 10x scale?" Because production doesn't care about your local testing. Quick debugging checklist if you're dealing with slow jobs: Check Spark UI stage details, not just the summary. Look for task duration variance (one task taking 100x longer = skew). Inspect input data size per partition. Consider salting, repartitioning, or broadcast joins based on what you find. Test at production scale. The performance issues that matter don't show up in testing. They show up when real data hits real workloads. What's the worst performance issue you've debugged in Spark or Databricks? Curious what nightmares are out there. #Databricks #DataEngineering #Spark #ApacheSpark #BigData #DataPipelines

Optimizing Databricks Workloads: Key Points & Techniques After reviewing the official Databricks guide, here’s what every advanced data team should keep in mind: Delta Lake + ACID Provides a reliable foundation for data skipping, column pruning, and high-performance pipelines. Data Organization • File size: ~1 GB after OPTIMIZE • Strategic partitioning: only on frequently filtered columns • ZORDER for high-use columns Spark Optimization • Reduce shuffles: broadcast joins, CBO, AQE • Monitor skew and large joins • Persist intermediate results only when necessary Reads & Caching • Column pruning and predicate pushdown • Spark cache / Delta cache for frequently accessed datasets Cluster Tuning • Autoscaling + auto-termination • Choose appropriate instance types and pools • Photon / optimized runtime for improved performance and cost efficiency Conclusion: Workload optimization = architecture + storage + queries + cluster. Goal: fast, scalable, and cost-controlled pipelines. Full guide: https://lnkd.in/evPac3wR #Databricks #Spark #DeltaLake #DataEngineering #Lakehouse

🚀 Deletion Vectors in Databricks | Efficient DELETE, UPDATE & MERGE at Scale Deletion Vectors are a Delta Lake storage optimization in Databricks that significantly improve the performance of DELETE, UPDATE, and MERGE operations—especially when dealing with many small changes(DML Operations). Instead of rewriting large Parquet files, Databricks uses soft deletes by tracking removed rows separately. 🔍 How Deletion Vectors Work 1️⃣ Soft Deletion Deleted or updated rows are marked in small deletion vector files (similar to a bitmap) instead of rewriting the original Parquet file. 2️⃣ Merge-on-Read (MoR) During query execution, Databricks combines: • Base Parquet data • Deletion vectors • Delta transaction log to return correct results. 3️⃣ Lazy Physical Deletion Actual file rewrites happen later during: • OPTIMIZE → file compaction • VACUUM → permanent cleanup ⚡ Key Benefits ✅ Faster DELETE / UPDATE / MERGE ✅ Reduced file rewrites and I/O ✅ Lower storage and compute cost ✅ Ideal for GDPR / PII deletes ✅ Easier rollback using Delta transaction history ⚙️ Usage & Configuration • Enable using table property: delta.enableDeletionVectors = true • Recommended runtime for writes: DBR 14.3 LTS+ • Read support: DBR 12.2 LTS+ • Requires periodic OPTIMIZE / VACUUM for cleanup #AzureDatabricks #ApacheSpark #PySpark #SparkSQL #DeltaLake #DataEngineering #BigData #PerformanceOptimization #AzureDatabricks #Databricks #ADB #Spark #PerformanceTuning #SparkSql #AzureDataEngineer #SparkPerformance #QueryOptimization #DataOptimization #BigDataPerformance #DistributedComputing #ScalableSystems #TechLearning #DailyLearning #DataEngineerLife #LearningInPublic #LinkedInTech #DataCommunity #LiquidClustering #Lakehouse #DataArchitecture #CloudData #ModernDataStack #DeletionVectors

A friend of mine told me about this So sharing the post if it can help someone Usage of VARIANT type in Databricks My pipeline had 180 lines just to define the schema of a single JSON column. Every time the source system changed a field, the pipeline broke. No warning. No changelog. Just a failed job and a Slack alert at the wrong hour. I was sick of it... The data came from an external API. The vendor kept adding new fields, renaming things, and changing types, and a field that was a string last week became an array this week. Each change meant the same cycle. Find the broken field -> Update the struct -> Test -> Redeploy -> Repeat. I was spending more time maintaining the schema than building anything useful. So I looked at alternatives. Keeping the fixed schema was not sustainable... too fragile, too much manual work. Using schema_of_json() did not help either. It only infers from one row at a time, misses fields, and does not work well in streaming. Ingesting everything as a raw STRING and parsing downstream was an option but it just moves the problem somewhere else. Then I tested VARIANT. VARIANT is a semi-structured type native to Databricks since Runtime 15.3. It accepts any JSON structure without a predefined schema. New fields show up automatically. No code changes needed when the source evolves. I replaced the entire struct definition on the body column with a single VARIANT type. From 180 lines to one. There are trade-offs. - Query syntax changes. Instead of .field you write :field::type to access and cast values, so downstream consumers need to adapt their queries (This was not an issue since this was on early phase). - And you need DBR 15.3 or higher. But for my use case, where the source was unstable and the schema was always shifting, VARIANT was the right call. One full refresh to migrate, and after that, zero schema maintenance. If you are dealing with unpredictable JSON from external sources, VARIANT might save you a lot of reactive work. _______________ Follow me for more Data Engineering tips. #databricks #variant #dataengineering

A lot of people have been asking me how to actually learn Delta Lake beyond just theory — so I built a hands-on Databricks project and open-sourced it . This repo shows a complete end-to-end data engineering workflow using: • Databricks & PySpark • Delta Lake (ACID, schema evolution, external tables) • Azure Data Lake (ADLS Gen2) • Bronze → Silver → Gold architecture • Star schema with fact & dimension tables You’ll see how raw data is transformed into analytics-ready fact_sales, dim_products, and dim_customers tables, just like in real production systems. If you’re learning Delta Lake, Databricks, or modern data engineering, this should give you a practical starting point. 🔗https://lnkd.in/eQjZ2Fda Feedback and contributions are always welcome. #DataEngineering #DeltaLake #Databricks #BigData #Azure #ETL #Analytics #OpenSource