Stream Your First Event in 10 Minutes
Ship a working Flink job on Docker, send events through it, and read the output. The fastest path from zero to a running streaming app — no theory, no setup ceremony, just a green pipeline.
Phases
3
StreamingPhase 1 freeFull access in Professional
Apache Flink Streaming
Event-time processing, state management, and production Flink pipelines.
Flink is the framework production teams pick when sub-second latency, exactly-once, and stateful event-time logic all need to be true at the same time. Alibaba runs Singles Day on it; Uber, Netflix, and Pinterest run their event platforms on it. Senior streaming roles look for engineers who can defend watermark + checkpoint + savepoint decisions, not just write a DataStream.
What you'll do
What you'll be able to do.
- Build Flink streaming applications with event-time semantics
- Implement stateful processing with checkpointing and savepoints
- Design windowing patterns for complex event processing
- Deploy production Flink pipelines with Kafka integration
Phase roadmap.
Phase 1PRO REQUIRED
Flink Foundations
Your first Flink job and the core architecture. Run a streaming pipeline in 10 minutes, then go deep on the JobManager / TaskManager / slot model the rest of the path builds on.
1.1
✓Stream Your First Event in 10 Minutes
Ship a working Flink job on Docker, send events through it, and read the output. The fastest path from zero to a running streaming app — no theory, no setup ceremony, just a green pipeline.
1.2
✓Stream Processing Foundations
Streaming vs batch tradeoffs, the JobManager / TaskManager / slot architecture, the DataStream API (sources / transforms / sinks), parallelism + operator chaining, and how Flink recovers when a TaskManager dies.
Phase 2PRO REQUIRED
Advanced Processing
Time, state, windowing, and Kafka integration. Where Flink jobs graduate from working-on-clean-data to surviving out-of-order events, late data, RocksDB-backed state, and exactly-once Kafka pipelines.
2.1
⊘Time & Watermarks
Event time vs processing time, watermark generation strategies, allowed lateness + side outputs, timer service + ProcessFunction, and the debug recipes for the silent late-data drops that bite every first production deploy.
2.2
⊘Manage State at Scale (RocksDB, Checkpoints, Failures)
Keyed state types (Value / Map / List), heap vs RocksDB state backends, checkpointing for fault tolerance, savepoints for zero-downtime upgrades, state TTL + memory management, and a hands-on stateful fraud feature store.
2.3
⊘Design Real-Time Aggregations (Fraud, Metrics, Alerts)
Tumbling / sliding / session windows, reduce / aggregate / process window functions, custom triggers + evictors, global windows, multi-window late-data patterns, and the throughput tuning that decides whether your metrics ship on time.
2.4
⊘Kafka Integration & CDC
Kafka source offsets + consumer groups, exactly-once via two-phase commit + transactional sinks, Schema Registry + Avro deserialization, multi-source enrichment joins, Debezium CDC, and backpressure detection + resolution.
Phase 3PRO REQUIRED
Production & Capstone
Deployment, real-time ML, and capstone. Run Flink on Kubernetes with checkpoint tuning, zero-downtime savepoint upgrades, an online ML scoring pipeline, and a full streaming-platform design defended end-to-end.
3.1
⊘Run Flink in Production (Kubernetes, Scaling, Failure Recovery)
Flink K8s Operator deployment, checkpoint configuration for production, parallelism + slot + autoscaling strategy, restart strategies + failure budgets, JVM / off-heap / network memory tuning, and zero-downtime savepoint upgrades.
3.2
⊘Real-Time ML
Online feature computation in Flink state, low-latency model serving integration, feature drift detection, online learning from streams, A/B testing streaming models, and a real-time scoring pipeline built end-to-end.
3.3
⊘Design a Production Streaming Platform
Architect a fraud-detection platform: SLA definition, Kafka → Flink → Iceberg topology, checkpoint + state strategy, capacity + cost model, failure runbook + multi-region DR, and portfolio deliverables you can defend in a staff interview.
What you'll ship
What you'll build.
- Event-time pipeline with watermarks, allowed lateness, and side-output recovery for late events
- Exactly-once Kafka → Flink → Kafka job with transactional 2PC sinks and Schema Registry
- Windowed fraud-detection topology with keyed state, RocksDB state backend, and tuned checkpoints
- Production deployment on Kubernetes (Flink Operator) with savepoint upgrades, autoscaling, and a runbook
Definition
What is Apache Flink Streaming?
Apache Flink is a distributed stream processing framework designed for stateful computations over event streams. Unlike micro-batch systems, Flink processes events one at a time with true event-time semantics, making it the go-to choice for low-latency applications at companies like Alibaba, Uber, and Netflix.
Production context
Why this matters in production.
Flink powers the most demanding real-time systems. Alibaba processes billions of events per second with Flink during Singles Day. Production Flink requires understanding checkpointing, state backends, and backpressure handling to build pipelines that run reliably for months without restarts.
Use cases
Common use cases.
- Processing real-time event streams with sub-second latency
- Implementing complex event processing with windowing and pattern detection
- Building stateful streaming applications with exactly-once guarantees
- Running real-time feature engineering for ML inference pipelines
- Performing streaming joins between multiple event sources
- Deploying Flink SQL for real-time analytics without custom code
Compare
Flink vs alternatives.
FlinkvsSpark Streaming
Flink provides true event-at-a-time processing with lower latency. Spark Structured Streaming uses micro-batches with higher throughput. Flink is better for latency-critical workloads; Spark for batch-streaming unification.
FlinkvsKafka Streams
Flink offers more advanced windowing, event-time processing, and horizontal scaling. Kafka Streams is simpler to deploy as a library. Choose Flink for complex stateful processing, Kafka Streams for simpler transformations.
FlinkvsBeam
Apache Beam provides a unified API that runs on Flink, Spark, or Dataflow. Flink is the most popular Beam runner for streaming. Teams use Beam for portability, Flink directly for maximum control.
Build with this skill
Build real systems.
Why this matters
Why this skill matters.
Apache Flink is the streaming specialty that maps to senior + staff real-time engineering roles. Companies running Flink at scale (Uber, Alibaba, Netflix, Pinterest, Stripe) hire specifically for engineers who can defend watermark strategy, state backend choice, checkpoint tuning, and savepoint upgrade procedure — the exact decisions this path makes you defensible on.
FAQ
Common questions about Apache.
Apache Flink StreamingStart Phase 1