If Delta Lake Uses Immutable Files, How Do UPDATE, DELETE, and MERGE Work?

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Listen and Watch here One of the most common questions data engineers ask is: if Delta Lake stores data in immutable Parquet files, how can it support operations like UPDATE , DELETE , and MERGE ? The answer lies in Delta Lake’s transaction log and its clever file rewrite mechanism. 🔍 Immutable Files in Delta Lake Delta Lake stores data in Parquet files, which are immutable by design. This immutability ensures consistency and prevents accidental corruption. But immutability doesn’t mean data can’t change — it means changes are handled by creating new versions of files rather than editing them in place. ⚡ How UPDATE Works When you run an UPDATE statement, Delta Lake: Identifies the files containing rows that match the update condition. Reads those files and applies the update logic. Writes out new Parquet files with the updated rows. Marks the old files as removed in the transaction log. UPDATE people SET age = age + 1 WHERE country = 'India'; Result: ...
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File Format in PySpark

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When working with PySpark, understanding different file formats for data ingestion is key to efficient data processing. Here are some common file formats supported by PySpark:


1️⃣ CSV (Comma-Separated Values): CSV files are widely used for tabular data. PySpark provides easy-to-use methods for reading and writing CSV files, making it simple to work with structured data.

2️⃣ Parquet: Parquet is a columnar storage format that is highly efficient for analytics workloads. PySpark's native support for Parquet enables fast reading and writing of large datasets, making it ideal for big data applications.

3️⃣ JSON (JavaScript Object Notation): JSON is a popular format for semi-structured data. PySpark can easily handle JSON files, making it convenient for working with data that may have varying schema.

4️⃣ Avro: Avro is a binary serialization format that provides rich data structures and schema evolution capabilities. PySpark supports Avro files, allowing for efficient data exchange between different systems.

5️⃣ ORC (Optimized Row Columnar): ORC is another columnar storage format optimized for Hive workloads. PySpark's support for ORC enables high-performance data processing for analytics applications.

Each of these file formats has its own advantages and use cases. By leveraging PySpark's capabilities, you can efficiently ingest and process data in various formats to meet your analytical needs.

Hope it helps!

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