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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RDD vs DATAFRAME vs DATASET

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 Spark - RDD, Dataframe and Dataset!!





Let's start with RDDs (Resilient Distributed Datasets). 

Q Explain what an RDD is and its role in distributed computing?

RDD : An RDD is a fundamental data structure in Apache Spark, designed to handle large-scale data processing across clusters. It represents an immutable, partitioned collection of records that can be operated on in parallel. RDDs provide fault tolerance through lineage information, enabling recomputation of lost data partitions.

Q How does Spark's RDD differ from traditional data structures like arrays or lists?

 Unlike arrays or lists, RDDs are distributed across multiple nodes in a cluster, allowing for parallel processing and fault tolerance. RDDs are immutable, meaning their contents cannot be changed once created. Operations on RDDs are lazily evaluated, allowing Spark to optimize execution plans and perform transformations efficiently.

Q Moving on to dataframes in Spark. What is a dataframe, and how does it differ from RDDs?

 A dataframe is a distributed collection of data organized into named columns, similar to a table in a relational database or a dataframe in Pandas. Unlike RDDs, dataframes provide a higher-level abstraction, allowing for structured data processing with support for SQL queries, optimizations, and integration with other data sources. Dataframes offer better performance and ease of use compared to RDDs for structured data processing tasks.

Q Demonstrate how you would create a dataframe in Spark and perform some basic operations on it?

 We can create a dataframe by loading data from various sources like CSV, JSON, or Parquet files using SparkSession. Once created, we can perform operations like selecting columns, filtering rows, aggregating data, and joining with other dataframes using DataFrame APIs or SQL queries.

 let's discuss datasets in Spark. 

Q How do datasets differ from dataframes, and when would you choose one over the other?

Datasets are a newer API introduced in Spark that combine the benefits of RDDs and dataframes. Like dataframes, datasets support structured data processing with optimizations and type safety. However, datasets also provide the flexibility and performance of RDDs through user-defined functions (UDFs) and custom transformations. I would choose datasets over dataframes when dealing with complex data types or when fine-grained control over serialization and performance is required.

This concludes our discussion on RDDs, dataframes, and datasets in Spark.

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