Batch vs streaming ETL: Decision factors on AWS

As businesses generate and consume vast amounts of data, building effective ETL (Extract, Transform, Load) pipelines has become essential. When designing an ETL architecture on AWS (Amazon Web Services), one of the most critical decisions is choosing between batch processing and streaming processing. Each approach serves different use cases, and understanding the key decision factors can help you select the best solution for your needs.

In this blog, we’ll explore the differences between batch and streaming ETL and the primary factors to consider when making that choice on AWS.


Understanding Batch ETL

Batch ETL involves processing data in chunks or batches at scheduled intervals. It is ideal when data freshness is not critical, and the system can tolerate some latency.

Popular AWS services for batch ETL:

  • AWS Glue: Fully managed ETL service for batch data.
  • Amazon EMR: Managed Hadoop/Spark for large-scale data processing.
  • Amazon S3: Common storage layer for raw and transformed data.
  • Amazon Redshift: Used as a destination for analytics-ready data.

Use cases:

  • Daily reports
  • Periodic data aggregation
  • Data warehouse loading


Understanding Streaming ETL

Streaming ETL processes data continuously and in real-time or near real-time. It is used when insights are needed immediately after data is generated.

Popular AWS services for streaming ETL:

  • Amazon Kinesis Data Streams: Captures real-time streaming data.
  • Kinesis Data Firehose: Delivers streaming data to destinations like S3, Redshift, or Elasticsearch.
  • AWS Lambda: Serverless function to transform data on-the-fly.
  • Amazon MSK (Managed Streaming for Apache Kafka): For scalable, distributed event streaming.
  • AWS Glue Streaming Jobs: Real-time data transformation and enrichment.

Use cases:

  • Real-time fraud detection
  • Live user analytics
  • Monitoring and alerting systems
  • IoT device data processing


Decision Factors: Batch vs Streaming ETL

1. Latency Requirements

Batch: Suitable for high-latency tolerance (e.g., hourly or daily updates).

Streaming: Required when data must be processed within seconds or milliseconds.


2. Data Volume and Frequency

Batch: Works well for large volumes of static or infrequently updated data.

Streaming: Ideal for continuous inflow of data (e.g., logs, sensor data).


3. Cost Considerations

Batch: Generally more cost-effective for infrequent processing.

Streaming: May incur higher costs due to always-on infrastructure, but essential for real-time use cases.


4. Complexity and Maintenance

Batch: Simpler to implement and maintain with scheduled jobs.

Streaming: More complex due to continuous processing and monitoring requirements.


5. Business Use Case

Batch: Traditional BI reporting, offline analytics, backups.

Streaming: Real-time personalization, instant alerting, live dashboards.


6. Integration and Ecosystem

AWS provides seamless integration for both types:

Batch: AWS Glue + Amazon S3 + Redshift

Streaming: Kinesis + Lambda + S3 or Elasticsearch


Hybrid ETL: Best of Both Worlds

Many organizations adopt a hybrid approach, combining batch and streaming pipelines. For instance, real-time ingestion may happen through Kinesis, while heavy transformations and data archiving occur in scheduled batch jobs using Glue or EMR.


Conclusion

Choosing between batch and streaming ETL on AWS depends on your application’s latency needs, data characteristics, cost constraints, and business goals. While batch ETL is sufficient for many traditional workloads, streaming ETL is crucial for modern, real-time data processing needs.

Evaluate your use case carefully and consider leveraging AWS’s scalable and managed services to build efficient and reliable ETL pipelines tailored to your needs.

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