Data Pipeline Development (Streaming)

From Batch to Stream: A Fundamental Shift

Unlike traditional batch processing, streaming pipelines process data the moment it is created. This requires managing infinite event flows rather than static files. A robust architecture must handle Backpressure, State Management, and Fault Tolerance to ensure that no data is ever lost during the "firehose" effect.

Streaming Architectures: Lambda vs. Kappa

Lambda Architecture (The Hybrid)

Uses two lanes: A Speed Layer for real-time approximations and a Batch Layer for daily high-accuracy recalculations.

Pros: Extremely accurate. Cons: High maintenance (duplicate code).

Kappa Architecture (The Modern Standard)

Everything is a stream. Historical data is simply "replayed" through the same engine used for real-time events.

Pros: Single codebase. Cons: Requires robust log storage (Kafka).

The Three Stages of Ingestion

1. Ingestion (Buffer)

Tool: Apache Kafka. Acts as a decoupling buffer to prevent system crashes during sudden traffic spikes.

2. Processing (Brain)

Tool: Apache Flink. Performs transformations, joins click streams, and calculates rolling averages with ultra-low latency.

3. Serving (Sink)

Tool: Apache Druid / Pinot. Real-time OLAP databases that make streaming data visible to dashboards instantly.

Critical Engineering Concepts

Windowing (Measuring "Now")

Streams have no "Total." We use Tumbling (fixed intervals), Sliding (overlapping windows), and Session (user-activity based) windows to segment time.

Watermarking (Late Data)

Handling out-of-order events. If an IoT sensor reconnects late, watermarks define the threshold for how long the system waits before dropping the event.

Backpressure

The "Firehose" effect. When consumers can't keep up, they signal producers to slow down, preventing memory overflows and node crashes.

Streaming Toolkit

Category	Tool	Type	Usage
Message Bus	Apache Kafka	Log-based Broker	The industry standard buffer for millions of events/sec.
Stream Engine	Apache Flink	True Streaming	Ultra-low latency processing for complex stateful logic.
Stream Engine	Spark Streaming	Micro-Batch	Easier to use, but processes events in tiny batches (higher latency).
Orchestration	Apache Airflow	Workflow	Managing dependencies between Kafka, Flink, and the Sink.
Storage	Apache Druid	Real-Time DB	Sub-second queries on fresh streaming data.

Build Real-Time Resilience

Download our "Streaming Architecture Selection Guide" to learn when to choose Flink vs. Spark for your pipeline.

Download Pipeline Guide (.docx)