Streaming First: Events vs Batches
Three quick exercises: what makes a system "streaming," send your first event to a topic, and contrast event-at-a-time vs micro-batch processing.
Phases
3
StreamingPhase 1 freeFull access in Professional
Streaming Fundamentals
Event-driven architecture, message brokers, and real-time processing foundations.
Every streaming engine — Kafka, Flink, Spark, Pulsar — implements the same primitives: partitions, watermarks, state, delivery semantics. Learn the foundations once, apply them everywhere.
What you'll do
What you'll be able to do.
- Understand streaming vs batch processing trade-offs
- Build event-driven pipelines with message brokers
- Implement windowing, watermarks, and late-data handling
- Design reliable streaming architectures with exactly-once semantics
Phase roadmap.
Phase 1PRO REQUIRED
Streaming First Steps
Core concepts and streaming foundations
1.1
✓Streaming First: Events vs Batches
Three quick exercises: what makes a system "streaming," send your first event to a topic, and contrast event-at-a-time vs micro-batch processing.
1.2
✓Streaming vs Batch Architecture
Streaming vs batch trade-offs: latency, throughput, cost, ordering. Why most production stacks run both side-by-side, and how to choose per workload.
Phase 2PRO REQUIRED
Processing Patterns
Windowing, state, and delivery guarantees
2.1
⊘Kafka Core: Partitions, Brokers, Topics
Partition strategy, replication factor, broker failure modes, and consumer groups. The Kafka primitives every streaming engine inherits.
2.2
⊘Delivery Guarantees & Semantics
At-most-once vs at-least-once vs exactly-once. Idempotent producers, transactional writes, and the 2PC protocol that makes EOS work across systems.
2.3
⊘Time, Windows & Watermarks
Event-time vs processing-time, watermark generation, allowed lateness, and tumbling/sliding/session windows. The 4-knob model for late-data handling.
2.4
⊘Stateful Stream Processing
Keyed state, RocksDB-backed stores, checkpoint barriers, and incremental snapshots. How streaming engines survive failover without losing state.
Phase 3PRO REQUIRED
Production Streaming
Scaling, monitoring, and real-world patterns
3.1
⊘Event-Driven Architecture at Scale
Multi-cluster topologies, schema evolution, dead-letter queues, and back-pressure. The patterns that keep 10K-events/s pipelines from melting at 100K.
3.2
⊘Streaming Platform Operations
Lag monitoring, partition rebalancing, capacity planning, and the SLO model for streaming platforms. What an on-call rotation actually does.
What you'll ship
What you'll build.
- Event-driven pipeline with exactly-once delivery between Kafka and a sink
- Windowed aggregation (tumbling + sliding + session) with watermark tuning
- Stateful processor with checkpoint + restore against a key-partitioned topic
- Operational runbook covering consumer lag, rebalancing, and partition-skew detection
Definition
What is Streaming Fundamentals?
Streaming fundamentals covers the core concepts of real-time data processing: event-driven architecture, message brokers, windowing, watermarks, and delivery guarantees. These foundations apply to every streaming technology — Kafka, Flink, Spark Streaming — and are essential for building systems that process data as it arrives rather than in batch.
Production context
Why this matters in production.
Real-time systems power fraud detection at Stripe, ride matching at Uber, and recommendations at Netflix. Production streaming requires understanding exactly-once semantics, late-data handling, and backpressure — concepts that determine whether your system processes events reliably or loses data silently.
Use cases
Common use cases.
- Building event-driven pipelines that process data in real-time
- Implementing windowed aggregations for real-time dashboards and alerts
- Designing message broker architectures with proper delivery guarantees
- Handling late-arriving data with watermarks and allowed lateness
- Creating exactly-once processing pipelines for financial transactions
- Monitoring streaming pipeline health with lag and throughput metrics
Compare
Streaming vs alternatives.
StreamingvsBatch Processing
Streaming processes events as they arrive with low latency. Batch processes data in scheduled intervals with higher throughput. Most production systems use both — streaming for real-time needs, batch for historical analysis.
StreamingvsMicro-Batch
True streaming processes each event individually. Micro-batch (like Spark Streaming) processes small batches at short intervals. Micro-batch is simpler but adds latency compared to true event-at-a-time processing.
StreamingvsCDC Pipelines
Streaming fundamentals provide the foundation for CDC (Change Data Capture) pipelines. CDC captures database changes as events, which streaming systems process. Understanding streaming concepts is prerequisite to building CDC.
Why this matters
Why this skill matters.
Streaming foundations are the dividing line between mid and senior data engineers. Once you can reason about partitions, watermarks, and delivery semantics — you can debug any streaming engine in production, not just the one you trained on.
FAQ
Common questions about Streaming.
Streaming FundamentalsStart Phase 1