Data Storage and Processing Architectures
Handling large-scale data efficiently requires robust storage and processing architectures. The choice of architecture depends on the volume, velocity, and variety of data being processed.
1. SQL vs NoSQL Databases
- SQL (Structured Query Language): Used for structured, relational data (e.g., MySQL, PostgreSQL).
- NoSQL: Handles unstructured or semi-structured data with scalability (e.g., MongoDB, Cassandra).
2. Big Data Processing Frameworks
- Apache Hadoop – Distributed storage (HDFS) and batch processing using MapReduce.
- Apache Spark – In-memory processing for real-time data analytics.
3. Cloud-Based Data Storage
- AWS S3, Google Cloud Storage, Azure Blob Storage – Scalable cloud storage solutions.
- BigQuery, Snowflake, Redshift – Managed data warehouses for analytics.
4. Streaming Data Processing
- Apache Kafka – Real-time data ingestion and event streaming.
- Apache Flink – Stream processing framework for real-time analytics.
Choosing the right data storage and processing architecture depends on the specific requirements of the Data Science application, such as latency, scalability, and consistency needs.
