Big Data technologies are essential for handling large volumes of data efficiently. Here's a breakdown of key frameworks and tools:

1. Hadoop 📊

A distributed storage and processing framework.

  • Core Components: HDFS, MapReduce
  • Use Case: Batch processing of structured/unstructured data
Hadoop

2. Apache Spark ⚡

Fast, in-memory data processing engine.

  • Features: Real-time analytics, machine learning
  • Integration: Works with Hadoop ecosystem
Apache_Spark

3. NoSQL Databases 🌐

Designed for scalability and flexibility.

  • Examples: MongoDB, Cassandra, Redis
  • Advantages: Schema-less data storage
NoSQL

4. Data Warehousing 🏢

Centralized storage for structured data.

  • Tools: Amazon Redshift, Google BigQuery
  • Purpose: Business intelligence and reporting
Data_Warehouse

5. Cloud Computing ☁️

Scalable infrastructure for data storage and processing.

  • Providers: AWS, Google Cloud, Azure
  • Benefits: On-demand resources and global reach
Cloud_Computing

For deeper insights, explore our guide on Data Warehousing or Cloud Computing. 🌟