Big Data Technologies are the backbone of modern analytics.¹ They allow you to process datasets that are too large (Volume), too fast (Velocity), or too messy (Variety) for a single server to handle.

The ecosystem has evolved from the disk-based "Elephant" (Hadoop) to the lightning-fast, memory-based engine (Spark).²

Here is the breakdown of the two giants, how they work together, and the downloadable Word file.

1. Hadoop: The Storage Foundation

Hadoop (2006) is the bedrock.³ It solves the problem of storing petabytes of data cheaply.⁴

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• HDFS (Hadoop Distributed File System):⁵
• The Concept: It takes a massive file (e.g., 10 TB log file) and chops it into small blocks (128 MB).⁶
• Distribution: It scatters these blocks across 1,000 cheap servers.
• Redundancy: It saves 3 copies of every block.⁷ If a server catches fire, you lose nothing. The system heals itself.
• YARN (Yet Another Resource Negotiator): The "Traffic Cop."⁸ It manages the CPU and RAM of the cluster, deciding which application gets to run.⁹

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2. Apache Spark: The Processing Engine

Spark (2014) is the muscle. It solves the problem of speed.¹⁰

• In-Memory Computing: Unlike Hadoop MapReduce, which writes to the hard drive after every calculation, Spark keeps data in RAM.
• The Benefit: This makes it 100x faster for iterative workloads like Machine Learning, where the algorithm loops over the same data 50 times.¹¹
• Lazy Evaluation: Spark is smart.¹² If you tell it to "Filter data" then "Map data," it does nothing immediately. It waits until you ask for a result, then calculates the most efficient path (DAG - Directed Acyclic Graph) to get there.

3. When to Use Which?

They are often used together, not separately. You store data in Hadoop (HDFS) and process it with Spark.

Feature	Hadoop MapReduce	Apache Spark
Primary Resource	Disk (HDD)	Memory (RAM)
Speed	Slow (High Latency)	Fast (Low Latency)
Best Use Case	Massive, simple Batch ETL jobs that run overnight.	Interactive Data Science, AI/ML Training, Real-Time Streaming.
Cost	Low (Cheap Hardware)	High (Requires servers with massive RAM).