Glossary
Streaming ETL vs Traditional ETL
A comprehensive comparison of batch and streaming ETL approaches, exploring their architectures, use cases, and when to choose each pattern for modern data pipelines.
Streaming ETL vs Traditional ETL
ETL (Extract, Transform, Load) has been the backbone of data integration for decades. As organizations demand faster insights and real-time decision-making, the choice between traditional batch ETL and streaming ETL has become a fundamental architectural decision. This article explores both approaches, their technical characteristics, and how to choose the right pattern for your use case.
What is ETL?
ETL is a data integration process that moves and transforms data from source systems to destination systems, typically data warehouses or data lakes.
Extract retrieves data from one or more sources—databases, APIs, files, or message queues. Transform applies business logic, data cleaning, aggregations, or enrichment to make the data suitable for analysis. Load writes the processed data to a target system where it can be queried and analyzed.
The ETL pattern separates data collection from data consumption, allowing analytics systems to operate independently from operational systems. This separation has proven valuable for decades but comes with trade-offs in latency and complexity.
Traditional Batch ETL
Traditional ETL operates on a batch processing model. Data is collected over a time window (hourly, daily, weekly) and processed as a discrete batch job.
A typical batch ETL workflow runs on a schedule: extract data accumulated since the last run, transform it using tools like Apache Spark or custom scripts, and load it into a data warehouse such as Snowflake or BigQuery. Each batch represents a complete processing cycle with clear start and end points.
Characteristics of batch ETL:
Scheduled execution: Jobs run at fixed intervals (e.g., every night at 2 AM)
High throughput: Optimized for processing large volumes efficiently
Simpler failure recovery: Failed batches can be reprocessed in isolation
Higher latency: Data freshness depends on batch frequency
Resource optimization: Processing resources can be allocated during off-peak hours
Batch ETL excels when data doesn't need to be immediately available. A retail company might run nightly ETL jobs to update their data warehouse with yesterday's sales, inventory movements, and customer activities. The overnight processing window provides sufficient time for complex transformations and ensures analysts have fresh data each morning.
Streaming ETL
Streaming ETL processes data continuously as it arrives, transforming and loading records individually or in micro-batches within seconds or milliseconds.
Instead of waiting for scheduled intervals, streaming ETL pipelines react to new data events. When a user clicks a button, a sensor emits a reading, or a transaction occurs, the event flows through the pipeline immediately. Transformations happen in-flight, and results are continuously updated in the destination system.
Characteristics of streaming ETL:
Continuous processing: Data flows through the pipeline 24/7
Low latency: Near real-time data availability (seconds to milliseconds)
Event-driven: Processing triggered by data arrival, not schedules
Stateful operations: Maintains context across events (windowing, joins, aggregations)
Higher operational complexity: Requires always-on infrastructure and monitoring
Streaming ETL is essential when timely data drives business value. A fraud detection system might use streaming ETL to analyze credit card transactions as they occur, flagging suspicious patterns within milliseconds. An IoT platform could process sensor data from thousands of devices, detecting anomalies and triggering alerts before problems escalate.
Key Differences Between Batch and Streaming ETL
The choice between batch and streaming ETL involves several technical and business trade-offs.
Latency: Batch ETL inherently has higher latency—from minutes to hours or days depending on batch frequency. Streaming ETL achieves subsecond to low-second latencies. If your use case can tolerate data that's hours old, batch may suffice. If decisions depend on current state, streaming is necessary.
Complexity: Batch ETL is conceptually simpler. Each batch is independent, making debugging and testing straightforward. Streaming ETL requires managing stateful operations, handling out-of-order events, and dealing with exactly-once processing semantics. The operational overhead of maintaining streaming infrastructure is higher.
Cost: Batch ETL can be more cost-effective for high-volume, infrequent processing. You spin up compute resources, process the batch, and shut down. Streaming ETL requires continuously running infrastructure, though cloud-native streaming platforms have made this more economical with auto-scaling and managed services.
Use cases: Batch ETL suits historical reporting, compliance archiving, and bulk data migrations. Streaming ETL fits real-time dashboards, operational analytics, fraud detection, and event-driven architectures.
Failure handling: Batch jobs can be rerun if they fail, reprocessing the same input. Streaming pipelines must handle failures while data continues arriving, requiring checkpointing and state management to avoid data loss or duplication.
Streaming ETL Technologies and Platforms
Modern streaming ETL relies on specialized platforms designed for continuous data processing.
Apache Kafka serves as the backbone for many streaming ETL architectures. Kafka acts as a distributed event streaming platform, capturing data from sources and making it available to downstream processors. Kafka Connect provides connectors to extract data from databases, applications, and cloud services, while Kafka Streams enables lightweight stream processing.
Apache Flink is a stream processing framework built for stateful computations over unbounded data streams. Flink excels at complex event processing, windowed aggregations, and exactly-once semantics. It can process millions of events per second with millisecond latency.
Other technologies include Apache Pulsar (an alternative to Kafka with built-in multi-tenancy), Apache Beam (a unified programming model for batch and streaming), and cloud-native services like AWS Kinesis, Google Cloud Dataflow, and Azure Stream Analytics.
A typical streaming ETL pipeline might use Kafka to capture database changes via Debezium (change data capture), Kafka Streams or Flink to transform and enrich the events, and another Kafka Connect sink to load results into Elasticsearch for real-time search or a data warehouse for analytics.
Platforms like Conduktor help teams manage Kafka-based streaming ETL pipelines by providing tools for monitoring data flows, testing transformations, and ensuring data quality in production environments. As streaming architectures grow in complexity, visibility and governance become critical operational requirements.
Choosing Between Batch and Streaming ETL
The decision isn't always binary. Many organizations use both approaches for different use cases or combine them in hybrid architectures.
Choose batch ETL when:
Data freshness requirements are measured in hours or days
Processing large historical datasets efficiently is the priority
Simpler operational requirements are preferred
Cost optimization through scheduled resource allocation is important
Use cases involve complex transformations better suited to batch processing frameworks
Choose streaming ETL when:
Real-time or near-real-time data is required for business decisions
Event-driven architectures are being built
Continuous monitoring or alerting is needed
Data volume is manageable for continuous processing
The organization has expertise in streaming platforms
Hybrid approaches are increasingly common. A company might use streaming ETL to feed real-time dashboards and operational systems while running batch ETL overnight to update historical reporting tables with complex transformations. The Lambda architecture pattern explicitly combines batch and streaming layers, though modern tools increasingly favor the simpler Kappa architecture (streaming-only with reprocessing capabilities).
Consider starting with batch ETL for foundational analytics needs, then introducing streaming ETL for specific high-value real-time use cases. This pragmatic approach balances business value against operational complexity.
Summary
Traditional batch ETL and streaming ETL represent different architectural approaches to data integration, each with distinct strengths. Batch ETL offers simplicity, cost-effectiveness, and high throughput for scheduled processing, making it ideal for historical reporting and bulk data loads. Streaming ETL provides low latency and continuous processing, essential for real-time analytics and event-driven applications.
The choice depends on latency requirements, operational complexity tolerance, cost considerations, and specific use cases. Modern data platforms often employ both patterns, using streaming for time-sensitive workloads and batch for comprehensive historical processing. Technologies like Apache Kafka and Apache Flink have matured streaming ETL into a production-ready approach, while tools for managing these pipelines continue to evolve.
Understanding both approaches allows data engineers to design architectures that balance business requirements with technical and operational realities.
Sources and References
Kleppmann, Martin. Designing Data-Intensive Applications. O'Reilly Media, 2017. (Chapter 11: Stream Processing)
Apache Kafka Documentation. "Kafka Streams" and "Kafka Connect". https://kafka.apache.org/documentation/
Apache Flink Documentation. "DataStream API" and "Stateful Stream Processing". https://flink.apache.org/
Narkhede, Neha, Gwen Shapira, and Todd Palino. Kafka: The Definitive Guide. O'Reilly Media, 2017.
Databricks. "Delta Live Tables: ETL Framework for Batch and Streaming". https://www.databricks.com/product/delta-live-tables