Distributed Systems & Data Processing
Description
< What is it? >
Distributed systems coordinate software across multiple machines. Data processing transforms, stores, and analyzes data at a scale that may exceed one machine's compute, memory, or storage capacity.
Key points
< Why it matters for AI >
- Large AI workloads rely on distributed storage, data pipelines, training, and inference infrastructure
- Systems must balance scale, latency, reliability, cost, and coordination complexity
- Batch models such as MapReduce divide large jobs into parallel transformations and aggregations
Comparison
< MapReduce, Hadoop, Spark, and Snowflake >
| Technology | Primary role | Example |
|---|---|---|
| MapReduce | Distributed batch-processing model | Count events by mapping records to keys and reducing each group |
| Apache Hadoop | Framework for distributed storage, resource management, and processing | Store logs in HDFS and schedule jobs with YARN |
| Apache Spark | Distributed processing engine | Transform files and aggregate data with PySpark or SQL |
| Snowflake | Managed cloud data platform | Store analytical tables and query them using virtual warehouses |
These technologies can work together: Spark can run on Hadoop YARN, read HDFS data, and produce results for downstream analytical systems.