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1. INTRODUCTION TO DATABRICKS
DELTA LAKE
UNIFIED DATA MANAGEMENT FOR BIG DATA & AI
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2. • Delta Lake is an open-source storage layer that
brings ACID transactions to Apache Spark and
big data workloads.
• Developed by Databricks to solve reliability
issues in data lakes.
• Built on top of Apache Parquet format.
What is Delta Lake?
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3. Why Delta Lake?
• Solves the “Data Lake Reliability” problem.
• Enables atomic writes, schema enforcement, and time travel.
• Improves data quality, consistency, and scalability.
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4. Core Features
• ✅ ACID Transactions
• 🧩 Schema Enforcement & Evolution
• ⏪ Time Travel (data versioning)
• ♻️Upserts & Deletes (MERGE support)
• 🚀 Performance Optimizations with Caching & Z-Ordering
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5. Delta Lake Architecture
• Data stored in Parquet format
• A transaction log (delta log) tracks
changes to data
• Delta tables support batch and
streaming data processing
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6. Delta Table Structure
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7. Common Delta Lake Operations
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8. Delta Lake Use Cases
• Building reliable ETL pipelines
• Powering data warehousing
• Real-time streaming analytics
• Enabling machine learning with clean
data
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9. Delta Lake on Databricks
• Fully integrated into the Databricks Lakehouse Platform
• Easily convert existing Parquet tables to Delta
• Support for Unity Catalog, Auto Optimize, and Streaming
Reads
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10. Benefits Summary
• Combines reliability of data warehouses with scale &
flexibility of data lakes
• Ideal for big data and AI workloads
• Compatible with Apache Spark, Databricks, and other
platforms
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11. Contact Details:
📧 contact@accentfuture.com
🌐 AccentFuture
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DATABRICKS ONLINE COURSE
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