Features
Batch processing handles large, finite data sets at scheduled intervals, while stream processing ingests and analyzes data continuously in near-real time.
Batch processing and stream processing are two fundamental paradigms for transforming and analyzing data. Understanding how they differ—and when to apply each—will shape the scalability, latency, and cost profile of every modern data system.
Batch processing is the execution of a series of data jobs on a large but finite data set. The data is collected over time, stored (often in a data lake or warehouse), and processed at scheduled intervals—hourly, nightly, or on demand. Because the workload finishes after consuming all available input, results are typically delivered with minutes-to-hours of latency.
ETL or ELT pipelines.Stream processing is the continuous ingestion, transformation, and analysis of infinite data streams. Events are processed seconds—or milliseconds—after they are generated, enabling real-time monitoring, alerting, and personalization.
Batch favors throughput and compute efficiency; stream favors latency. A common rule of thumb is: if the question must be answered in under a minute, reach for streams. Otherwise, batch is usually simpler and cheaper.
Batch jobs read from fixed files or partitions; streams operate on unbounded event logs and require windowing (TUMBLE, HOP, SLIDE) to create logical slices of time.
Stream processors maintain long-lived, fault-tolerant state (counts, aggregations, ML models) that updates continuously. Batch jobs recompute state from scratch each run, which simplifies recovery at the expense of reprocessing cost.
Often you need both paradigms. The Lambda architecture pairs a streaming path for real-time views with a batch path for recomputation. The Kappa architecture simplifies this by treating all data as streams and replaying logs for historical rebuilds.
Suppose an e-commerce company wants to compute the rolling 1-hour revenue per product category.
SELECT
category_id,
DATE_TRUNC('hour', order_timestamp) AS hour_bucket,
SUM(amount) AS revenue
FROM raw.orders
WHERE order_timestamp >= DATEADD('hour', -24, CURRENT_TIMESTAMP())
GROUP BY 1, 2;
CREATE TABLE orders (
order_id STRING,
category_id STRING,
amount DECIMAL(10,2),
order_ts TIMESTAMP(3),
WATERMARK FOR order_ts AS order_ts - INTERVAL '5' SECOND
) WITH (...);
SELECT
category_id,
TUMBLE_START(order_ts, INTERVAL '1' HOUR) AS window_start,
SUM(amount) AS revenue
FROM orders
GROUP BY
category_id,
TUMBLE(order_ts, INTERVAL '1' HOUR);
Both paradigms benefit from idempotent writes and deterministic transforms so you can replay data after schema changes or failures.
Cloud object stores or log systems (Kafka) should be the source of truth. Compute layers (Spark, Flink) can then scale elastically.
Add validation checks (row counts, null ratios, schema enforcement) to catch divergence early—especially critical in always-on streams.
Real-time adds operational overhead (state management, exactly-once semantics). If dashboards refresh hourly, streaming is wasted effort.
With tools like Snowflake Snowpipe, Delta Live Tables, or BigQuery streaming inserts, micro-batching can push latency below five minutes.
Hybrid designs are common. Many companies start with batch and later add streaming for the critical low-latency slices.
Whether you query historical batch tables in Snowflake or materialized streaming views in ClickHouse, Galaxy’s modern SQL editor speeds up iteration. Parameterization, context-aware AI autocompletion, and shared Collections let teams version and endorse both batch ETL jobs and streaming queries without copying code into Slack. Use Galaxy to:
Batch processing and stream processing solve different latency-throughput trade-offs. By mastering both—and tools like Galaxy that make the SQL layer frictionless—you can build data platforms that deliver accurate historical insights and real-time intelligence.
Choosing between batch and stream processing determines how quickly data-driven products can react to events, how much infrastructure they require, and how expensive they are to operate. Data engineers must understand both paradigms to design pipelines that meet latency, throughput, and cost targets while keeping systems maintainable.
No. While real-time systems add overhead (state stores, always-on clusters), costs can be lower for workloads where early insights avoid losses (e.g., fraud). Cloud-native streaming engines that scale to zero also reduce idle spend.
Often yes. Start by capturing the same raw input as an append-only log (Kafka, Pub/Sub). Then move aggregations into a streaming engine that respects event-time semantics. Expect schema and state management work.
Galaxy offers a lightning-fast SQL editor with AI autocompletion and shared Collections, so you can prototype window functions for streaming data or optimize batch queries without leaving your IDE-like environment.
Use event-time processing with watermarks and configure an allowed lateness. Late events can still be incorporated via update or retract messages, or diverted to a correction pipeline.
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