Spark vs MapReduce Comparison

A compiler translates source code into machine code.

Combines multiple features into a single vector.

Yarn-client driver runs locally; yarn-cluster runs on the cluster.

Allows querying structured data using SQL-like interface.

Performs stateful computations across multiple time windows (streaming).

Displays the schema of a relation.

Splits data into 'k' parts for training and validation (repeatedly).

Saves metadata/state to recovery on failure.

Performs an inner join based on common keys.

Creating and preparing features for machine learning models.

Spark leverages in-memory data caching to avoid disk I/O, significantly speeding up data processing compared to MapReduce, which often involves repeated disk reads.

DataFrames are higher-level abstractions built on top of RDDs in Spark. They provide schema information and are optimized for relational operations.

A Discretized Stream (DStream) in Spark Streaming breaks a continuous data stream into batches for processing as Resilient Distributed Datasets (RDDs), making processing easier.

Spark's PipelineModel allows for efficient reuse and organization of data preparation steps and model training in machine learning workflows.

Apache Pig simplifies complex data operations with a SQL-like language. It abstracts away low-level MapReduce programming complexities.

Spark's lazy evaluation optimizes processing by postponing computations until necessary, combining transformations efficiently and preventing unnecessary intermediate data processing.

Apache Pig's interpreter converts Pig Latin scripts into MapReduce jobs, allowing them to run on Hadoop clusters.

Spark's in-memory processing and lazy evaluation optimize data processing, enabling significantly faster query performance and lower data movements than traditional disk-centric methods.