Requirements

Functional Requirements:

List the key functional requirements for the system (Ask the AI for hints if stuck)...

These describe what the system does.

• Ingestion: The system must ingest data from multiple sources (IoT devices, server logs, transactional DBs).
• Processing:
  • Real-time: Process incoming streams for immediate alerts or live dashboards.
  • Batch: Process historical data for deep analytics and model training.
• Storage: Store raw data (immutable source of truth) and processed/aggregated data.
• Consumption: Provide an interface for SQL queries, visualization tools (Tableau/Grafana), and API access.

Non-Functional Requirements:

List the key non-functional requirements (performance, scalability, reliability, etc.)...

These describe how well the system performs.

• Scalability: Must handle spikes in traffic (horizontal scaling).
• Fault Tolerance: No data loss if a node crashes (replication/checkpointing).
• Data Integrity: Ensure Exactly-Once or At-Least-Once processing semantics.
• Latency: Low latency (milliseconds to seconds) for the real-time path.
• Throughput: High throughput for the batch path (optimizing for volume over speed).

API Design

Define the APIs expected from the system. This is your chance to analyze and define the read and write paths so that you can come up with the high-level design...

For a data pipeline, the "API" is usually the entry point (Ingestion) or the exit point (Consumption). Let's design the Ingestion API for a producer sending event data.

Endpoint: POST /v1/events

High-Level Design

Describe the overall system architecture. Identify the main components needed to solve the problem end-to-end. Use the diagramming tool to create a block diagram.

We will use the Lambda Architecture pattern. This divides the pipeline into three layers:

1. Batch Layer (Cold Path): Stores master dataset, processes heavy jobs. High latency, high accuracy.
2. Speed Layer (Hot Path): Processes recent data only. Low latency, lower accuracy (sometimes).
3. Serving Layer: Merges views from Batch and Speed layers for queries.

The Data Flow:

1. Sources send data to a Load Balancer.
2. Load Balancer pushes data to a Message Queue (The buffer).
3. The Fork:
   • Path A (Speed): A Stream Processor reads from the Queue → Updates Real-time DB.
   • Path B (Batch): An Archiver reads from the Queue → Dumps to Data Lake (Raw Storage) → Batch Processor runs nightly jobs → Updates Batch Views.
4. Analytics Clients query the Serving Layer (Data Warehouse or Query Engine).

Detailed Component Design

Deep dive into 2-3 key components. Explain how they work, how they scale, discuss tradeoffs, capacity, and any relevant algorithms or data structures.

1. Data Ingestion (The Buffer)

Component: Apache Kafka (or AWS Kinesis).

• Why? We need to decouple producers from consumers. Kafka acts as a shock absorber. If the processing layer crashes, data accumulates in Kafka and isn't lost.
• Design Detail: We partition Kafka topics by user_id or event_id to ensure ordering within a specific partition.

2. Storage Layer (The Data Lake)

Component: Amazon S3 (or HDFS).

• Why? It's cheap, infinitely scalable, and separates compute from storage.
• Format: Store raw data in Parquet or Avro format. These are columnar formats, which are much faster for analytics than JSON or CSV because they allow the system to skip unnecessary columns during a scan.

3. Batch Processing

Component: Apache Spark.

• Why? Fast, in-memory processing. Handles massive aggregations (e.g., "Calculate daily active users for the last year").
• Workflow: Spark jobs run nightly, read raw Parquet files from S3, aggregate them, and write the results to the Serving Layer.

4. Stream Processing (Real-Time)

Component: Apache Flink or Spark Streaming.

• Why? Flink supports true event-time processing and stateful computations (e.g., "Count clicks per user in a rolling 5-minute window").
• Workflow: Reads directly from Kafka, updates a fast NoSQL database (like Cassandra or Redis) for the "live view."

5. Serving Layer & Analytics

Component: Data Warehouse (Snowflake / BigQuery) or Presto/Trino.

• Why? Analysts need to write SQL. We cannot expect them to write Java/Scala code.
• Integration: The Batch processing job loads clean data into the Data Warehouse. The Visualization tools (Tableau/Looker) connect here.