Big Data Technologies
From the "Elephant" to the "Spark": Architecting Petabyte-Scale Analytics.
The Backbone of Modern Analytics
Big Data technologies allow you to process datasets that are too large (Volume), too fast (Velocity), or too messy (Variety) for a single server. The ecosystem has evolved from the disk-based reliability of Hadoop to the lightning-fast, memory-based engines like Apache Spark.
1. Hadoop: The Storage Foundation
Hadoop is the bedrock. It solves the problem of storing petabytes of data cheaply using a distributed architecture.
- HDFS (Distributed File System): Chops massive files into blocks (128MB) and scatters them across 1,000+ cheap servers with 3x redundancy. If a server fails, the system self-heals.
- YARN (Resource Negotiator): The "Traffic Cop" that manages CPU and RAM across the cluster, deciding which applications run where.
Scale-Out Storage
In-Memory Muscle
2. Apache Spark: The Processing Engine
Spark is the muscle, solving the problem of speed through In-Memory Computing.
- 100x Faster: By keeping data in RAM instead of writing to disk after every step, Spark excels at iterative workloads like Machine Learning.
- Lazy Evaluation & DAG: Spark waits until a result is requested to calculate the most efficient path (Directed Acyclic Graph), optimizing performance automatically.
Hadoop vs. Spark: Strategic Comparison
| 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 (overnight). | Interactive Data Science, AI/ML, Streaming. |
| Cost | Low (Cheap Hardware) | High (Requires massive RAM servers) |
Note: In 2026, they are almost always used together: HDFS for storage, Spark for processing.
Architect Your Data Future
Download our "Big Data Ecosystem Map" to see how Hive, Presto, and Kafka integrate with your Spark cluster.
Download Big Data Guide (.docx)