Data Systems
Batch Processing
Batch processing handles large volumes of data efficiently through parallelization. Master MapReduce, Apache Spark, and the fundamentals of distributed data processing.
- Parallelism â Process data in parallel across many machines
- Fault Tolerance â Automatic recovery from failures
- Scalability â Linear performance improvement with more machines
Batch processing turns days of work into hours, and hours into minutes.
Batch Processing Fundamentals
MapReduce
Apache Spark
| Component | Purpose |
|---|---|
| Spark Core | RDD abstraction, task scheduling |
| Spark SQL | Structured data processing with DataFrames |
| Spark Streaming | Micro-batch stream processing |
| MLlib | Machine learning library |
| GraphX | Graph processing |
RDD vs DataFrame vs Dataset
| Abstraction | Type Safety | Optimization | Use Case |
|---|---|---|---|
| RDD | Compile-time | None | Low-level control |
| DataFrame | Runtime | Catalyst optimizer | SQL-like queries |
| Dataset | Compile-time | Catalyst optimizer | Type-safe queries |
Data Parallelism
Fault Tolerance
| Mechanism | Description |
|---|---|
| Data replication | Store input data on multiple nodes |
| Task retry | Re-execute failed tasks on other nodes |
| Checkpointing | Save intermediate state to durable storage |
| Lineage | Rebuild lost data from transformation history |
Batch vs Stream: When to Use Each
| Use Case | Recommended | Reason |
|---|---|---|
| Daily analytics report | Batch | Complete data needed |
| Real-time dashboard | Stream | Low latency required |
| ML model training | Batch | Large dataset, no time pressure |
| Fraud detection | Stream | Immediate response needed |
| ETL pipeline | Batch | High throughput, predictable schedule |
| IoT sensor monitoring | Stream | Continuous data, real-time alerts |
Practice Exercises
-
MapReduce Design: Design a MapReduce job to find the top 10 most frequent words in a 10TB text corpus. What are the map and reduce functions?
-
Spark Optimization: Given a Spark job that processes 1TB of data, identify 3 optimization strategies to reduce processing time from 2 hours to 30 minutes.
-
Fault Tolerance: Explain how Spark's lineage-based fault tolerance works. What are the trade-offs compared to HDFS replication?
-
Architecture Decision: Design a batch processing pipeline for daily ETL from PostgreSQL to a data warehouse. What components would you include, and how do you handle failures?
What to Learn Next
-> Stream Processing Real-time data processing with Flink, Spark Streaming, and Kafka Streams.
-> Data Lake Architecture Storage, processing, and governance for large-scale data.
-> Kafka Deep Dive Event streaming, partitioning, and exactly-once semantics.
-> Message Queues Async processing, event-driven architecture, and pub/sub patterns.
-> Event-Driven Architecture Event sourcing, CQRS, and message-driven systems.
-> Observability Logging, metrics, tracing, and monitoring distributed systems.