AgenticFor DevelopersDatabases & Data Engineering

Real-Time Streaming Data Architecture.

When building a system that must react to events in real time rather than batch processing them.

ChatGPT Β· Claude Β· GeminiΒ·AdvancedΒ·~1650 tokens
Curated by the AIPP team
Last updated 14 May 2026 Β· v3
real-time-streaming-data-architecture-4.md Β· 1650 words
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task.

# Context
- Pack: Developers & Tech Professionals
- Category: Databases & Data Engineering
- Use case: Real-Time Streaming Data Architecture
- Source task:
  - Design a real-time data pipeline for {{describe_use_case}}. Data volume: {{events_per_second}}.
  - Step 1: architecture design (Kafka/Kinesis/Pulsar for ingestion, Flink/Spark Streaming/ksqlDB for processing, ClickHouse/Druid/BigQuery for serving).
  - Step 2: data schema design (Avro/Protobuf/JSON : choose and justify).
  - Step 3: windowing strategy (tumbling, sliding, session windows) for the core aggregation.
  - Step 4: late data and out-of-order event handling.

# Goal
Full streaming architecture design, schema format recommendation, windowing strategy, and late data handling approach.

# Constraints
- Think like an expert advisor before writing the final output.
- Ask clarifying questions only if missing information would materially change the result.
- Avoid generic filler, vague advice, and unsupported claims.
- Make the output specific, practical, and ready to use.

# Output
Full streaming architecture design, schema format recommendation, windowing strategy, and late data handling approach.

The variables to fill in

PlaceholderWhat to put thereExample
{{role}}Rolestreaming data architect
{{use_case}}Your specific valuereal-time streaming data architecture
{{describe_use_case}}Describe use caseclickstream analytics, IoT sensor data, fraud detection, live dashboards
{{events_per_second}}Events per secondEVENTS PER SECOND

How to customize this prompt

  1. Replace each {{double-curly}} with your real context.
  2. Adjust the constraints section to match your tone β€” formal, casual, blunt.
  3. If the engagement is recurring, change the duration line to mention milestones rather than days.
  4. Run it in your tool of choice. The output should be ready to paste with at most one small edit.

When to use

When building a system that must react to events in real time rather than batch processing them.

PRO TIP

Protobuf or Avro is almost always better than JSON for high-volume streaming β€” the schema enforcement and size reduction compound at millions of events.

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