GUIDE

Stream Processing vs Batch Processing

Q: Is stream processing always better than batch processing?

No. Batch processing is better suited for workloads that are naturally periodic (monthly reports, full data reprocessing, training ML models on historical data). Stream processing excels when freshness matters — real-time dashboards, fraud detection, operational alerting, and event-driven architectures.

Q: Can I do both stream and batch processing in RisingWave?

Yes. RisingWave supports both continuous streaming queries (materialized views) and ad-hoc batch queries (SELECT statements) using the same SQL syntax. You can run one-time analytical queries against materialized views or source data without setting up a separate batch processing system.

Q: How does stream processing handle historical data?

RisingWave can ingest historical data from sources like Kafka (by reading from the beginning of a topic) or S3/file sources. When a materialized view is created, it processes all existing data first (backfill), then transitions to processing new events incrementally.

Q: What happens if my stream processing pipeline fails?

RisingWave uses barrier-based checkpointing to persist state periodically. On failure, it automatically recovers from the last checkpoint and reprocesses only the events since that checkpoint. Exactly-once semantics are maintained without manual intervention.

Stream processing handles data continuously as it arrives. Batch processing collects data and processes it in intervals. Learn the trade-offs, when to use each, and how RisingWave bridges the gap with SQL-based streaming.

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Continuous

Event Processing

Stream processing handles each record individually as it arrives, producing results continuously

Milliseconds

Not Hours

Results are available in milliseconds vs. minutes or hours with batch ETL pipelines

Steady

Resource Usage

Always-on processing spreads compute evenly — no bursty cluster spin-ups for periodic jobs

Unified

SQL Interface

RisingWave handles both streaming and batch queries with the same PostgreSQL-compatible SQL

Comparison

What is the fundamental difference between stream and batch processing?

Batch processing collects data over a period, then processes it all at once as a finite job. Stream processing handles each record individually as it arrives, producing results continuously. The fundamental distinction is bounded vs. unbounded data: batch operates on a known, complete dataset, while streaming operates on an open-ended flow of events.

Dimension	Stream Processing	Batch Processing
Data Model	Unbounded, continuous stream	Bounded, finite dataset
Latency	Milliseconds to seconds	Minutes to hours
Processing Trigger	Each event arrival	Schedule or manual trigger
State Management	Continuous, incremental	Per-job, recomputed each run
Resource Usage	Steady, always-on	Bursty, periodic spikes
Failure Recovery	Checkpoint-based, resume from last state	Restart entire job from scratch
Typical Tools	RisingWave, Flink, Kafka Streams	Spark, Hadoop, dbt, Airflow
Best For	Real-time dashboards, alerting, CDC	Reports, backfills, ML training

→If your users ask "why is this data from yesterday?" — you need stream processing
→If compliance requires audit trails with sub-minute latency — you need stream processing
→If your batch jobs are running every 5 minutes to approximate real-time — you need stream processing
→If your reporting can wait until end-of-day or end-of-month — batch processing is simpler and sufficient

Use Cases

When should you choose stream processing over batch?

Choose stream processing when data freshness directly impacts business outcomes. If a one-hour delay in processing means missed fraud, stale dashboards, or outdated recommendations, streaming is the right architecture. If data can wait until the end of the day or the end of the month, batch is simpler and often more cost-effective.

Real-Time Dashboards

Business metrics, operational KPIs, and customer-facing analytics that must reflect the current state — not yesterday's state.

Fraud and Anomaly Detection

Every second of delay increases exposure. Stream processing evaluates transactions as they happen, blocking fraud before it completes.

Event-Driven Architectures

Microservices that react to events in real-time — inventory updates, order processing, notification triggers, and workflow orchestration.

CDC and Data Synchronization

Keeping multiple systems in sync requires continuous propagation of changes. Batch replication introduces drift windows.

Unified SQL

How does RisingWave unify streaming and batch with SQL?

RisingWave eliminates the traditional divide between streaming and batch by using a single SQL interface for both. Materialized views handle continuous streaming computation, while ad-hoc SELECT queries provide batch-style analytics — all in the same database, using the same syntax, against the same data. No separate infrastructure for streaming vs. batch.

→Streaming: CREATE MATERIALIZED VIEW revenue AS SELECT region, sum(amount) FROM orders GROUP BY region — updates continuously
→Batch: SELECT * FROM revenue WHERE region = 'US' — ad-hoc query against the materialized view, returns in milliseconds
→Historical: Create a new materialized view and RisingWave backfills all existing data before switching to real-time mode
→Downstream: CREATE SINK to push streaming results to Kafka, PostgreSQL, Elasticsearch, or data lakes

Frequently Asked Questions

Is stream processing always better than batch processing?

Can I do both stream and batch processing in RisingWave?

How does stream processing handle historical data?

What happens if my stream processing pipeline fails?

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