Batch vs Streaming: Beginner

Data moves in scheduled chunks or in a continuous flow; the choice changes everything downstream

Data moves in scheduled chunks or in a continuous flow; the choice changes everything downstream

Category

Pipeline Architecture

Difficulty

beginner

Duration

25 minutes

Challenges

0 hands-on challenges

Topics covered: Two Ways Data Can Move, Batch: Picture, Rhythm, Example, Streaming: Picture, Rhythm, Example, What Real-Time Actually Means, Picking Batch or Streaming

Lesson Sections

1. Two Ways Data Can Move (concepts: paBatchVsStreaming)

   Data moves through a pipeline in one of two basic rhythms. The first rhythm is scheduled. Data piles up for a while, then a job wakes up, processes everything that has accumulated since the last run, and goes back to sleep. The second rhythm is continuous. Each new event flows through the pipeline as it arrives, with no waiting for a scheduled wake-up. Almost every pipeline in production fits into one of these two rhythms, or a hybrid that explicitly mixes them. Naming the rhythm is the first us

2. Batch: Picture, Rhythm, Example (concepts: paBatchProcessing)

   Batch processing is the older of the two rhythms and still the dominant pattern in production. Most analytical work in most companies runs as a batch job, often nightly, sometimes hourly. The pattern is so common that the word pipeline used without qualification almost always means a batch pipeline. Knowing the shape of a batch run cold is the foundation for everything else, because streaming is largely defined by what it changes about that shape. The Shape of a Batch Run The Nightly Run The can

3. Streaming: Picture, Rhythm, Example (concepts: paStreamProcessing)

   Streaming processing is the second basic rhythm. A streaming pipeline runs continuously. Each new event arrives at the source and flows through the transforms within milliseconds or seconds. There is no concept of a chunk and no concept of a scheduled wake-up. The pipeline is a long-running service, more like a web server than a script. The shape is more recent than batch in mainstream use, dating roughly from the rise of Apache Kafka in the early 2010s and the stream processors that grew up aro

4. What Real-Time Actually Means (concepts: paFreshnessTiers, paRealTimeMyth)

   Real-time is the most overloaded phrase in data engineering. A product manager asks for a real-time dashboard and means within an hour. A finance executive asks for real-time revenue and means by the start of the workday. A trading firm asks for real-time and means within five microseconds. The word is so elastic that it carries almost no information. The only useful response to a real-time request is to ask for the actual freshness target in concrete units of time, then translate that target in

5. Picking Batch or Streaming (concepts: paBatchVsStreamingChoice)

   Vocabulary becomes useful when applied to a specific decision. The exercise below picks between batch and streaming for three small concrete cases. The cases are intentionally simple so the choice is visible. Real production decisions are messier, but the same questions apply: what does the consumer need, when do they need it, and what does each option cost. Case 1: A Marketing Team's Daily Signup Count The marketing team wants a chart of new signups by country, by day, for the trailing 30 days.