Stream Processing vs. Incremental Computing: Picking the Right Tool for Real-Time Data Challenges

-- Users stream
CREATE TABLE users (
  user_id BIGINT,
  user_name STRING,
  user_level STRING,
  country STRING,
  city STRING,
  signup_time TIMESTAMP(3),
  WATERMARK FOR signup_time AS signup_time - INTERVAL '10' SECOND
) WITH (
  'connector' = 'kafka',
  'topic' = 'users',
  'properties.bootstrap.servers' = 'localhost:9092',
  'format' = 'json'
);

-- Orders stream
CREATE TABLE orders (
  order_id BIGINT,
  user_id BIGINT,
  order_status STRING,
  order_amount DECIMAL(10,2),
  payment_method STRING,
  order_time TIMESTAMP(3),
  WATERMARK FOR order_time AS order_time - INTERVAL '10' SECOND
) WITH (
  'connector' = 'kafka',
  'topic' = 'orders',
  'properties.bootstrap.servers' = 'localhost:9092',
  'format' = 'json'
);

-- Merged view via JOIN
CREATE VIEW unified_view AS
SELECT
  o.order_id,
  o.user_id,
  u.user_name,
  u.user_level,
  u.country,
  u.city,
  u.signup_time,
  o.order_status,
  o.order_amount,
  o.payment_method,
  o.order_time
FROM orders o
LEFT JOIN users FOR SYSTEM_TIME AS OF o.order_time AS u
  ON o.user_id = u.user_id;

-- Export downstream (Kafka/DB) or aggregate
INSERT INTO output_kafka
SELECT * FROM unified_view;

-- For corrections or backfills (e.g., fixing a missed event), this might require expensive full stream replays, as partial updates aren't natively supported without custom logic.

This is for illustration only—actual production use may need extensive tuning for your specific setup.

The Incremental Computing Route

Incremental Computing's Edge in Action

• Lower Learning Curve: Work with familiar tables and SQL—no complex timing concepts required.
• Strong Consistency: CDC captures every change accurately, avoiding stream processing artifacts.
• Efficient Resource Usage: Delta-only processing reduces overhead; durable views outperform volatile states.
• Computational Reuse: Share computations across multiple queries for enhanced efficiency.

Final Thoughts