Batch ETL vs Streaming ETL

ETL stands for Extract, Transform, and Load.  It refers to the processing of data from a variety of sources, either in batches or in streams.  Implementing ETL by hand is complex, slow, and error-prone, so many ETL tools now exist to help you derive value from your data and meet your business needs.

All types of data integration projects require an ETL process regardless of whether the data is extracted, transformed, or loaded. The advantage of using an ETL tool is that it optimizes ETL processing and can process structured data from a wide range of sources.

This article defines ETL, reviews the difference between batch ETL and streaming ETL, and introduces the idea of unifying batch and stream processing via a single tool.

What is ETL?

ETL is a process that extracts data from various sources in your system, transforms it, and applies business rules to it. As a final step, ETL loads the data to another system – typically a data warehouse – for further analysis.  Good ETL tools can extract data from multiple sources and aggregate them; transform the data into a queryable format; enable analysis via your preferred BI tool; and load your data into the database or data warehouse of your choice.

Transformation and loading can be done simultaneously; there is no need for an ETL server.  The ETL process is completed when the target system is updated with the latest data.  ETL also is used to update data quickly and efficiently.

Batch ETL Explained

In batch ETL processing, you collect and store data in batches during a specific window of time. This can improve the efficiency of data processing and help organizations and companies manage and quickly process large amounts of data.

Typically, data from a variety of company databases is loaded into the data warehouse in batches once or twice a day.  ETL tools are designed to focus narrowly on this batch connection of databases.  Data warehouses, meanwhile, execute batch tasks in any order.  This order defines the workflow for each batch. 

In some cases the batch can get so large that the ETL tools simply cannot process it fast enough. In fact, it can be difficult for many IT managers to meet their existing infrastructure requirements for batch processing while simultaneously shrinking the batch window for ETL processing bottlenecks.  There are also use cases for which batch simply isn’t sufficient, such as when:

• data freshness is mission-critical
• you’re working with a large dataset and you don’t need to access the entire dataset
• the data is coming in in streams and not in batches

Streaming ETL Explained

In streaming, data is generated as streams of events.  These can come from any source.  Streaming ETL helps you make changes to the data while it’s in motion.  The entire process can be in one stream, whether you stream data to a data warehouse or a database.

Streaming ETL is useful for real-time use cases.  As these use cases are increasingly in demand, there are tools built to make it easy to convert periodic batch jobs into a real-time data ETL pipeline.  The dataset resulting from stream processing can be loaded into a data lake, such as Amazon S3, for further processing or analysis. 

This amounts to a powerful and scalable ETL pipeline you can use in your core business applications.  You can extract, transform, and load data using a stream-based data pipeline to perform SQL queries and generate reports and dashboards. 

Streaming ETL can extract data from most any source and publish it directly to a streaming ETL application.

While stream processing is more complex than batch, there are important benefits (as compared to batch processing), primarily:

• Data freshness – since events are processed close to the time they are generated, you avoid the delays involved with batch processing and can more quickly make data available for analysis.
• Cost – events are processed one at a time, removing the need to run large operations on small servers. This helps keep your compute costs under control.

Learn more about open source event stream processing.

SQLake – Unifying Streaming and Batch Data Processing

Increasingly organizations are recognizing the need to do both batch and stream processing.  But it’s difficult to do both well, not least because batch and stream processing each traditionally has required separate tools and skills.  And more tools = more complexity.  But SQLake – Upsolver’s newest offering – unifies batch and streaming sources into a single stream processing flow that minimizes complexity.

Upsolver SQLake is a declarative data pipeline platform for both streaming and batch data. With SQLake you can easily develop, test, and deploy pipelines that extract, transform, and load data in the data lake and data warehouse in minutes instead of weeks.  Using only SQL, you:

• ingest streams and files into data lake tables in real-time
• create jobs that transform, join, and aggregate that data
• stream the transformed dataset into a variety of destination systems for analytics consumption

Summary: Batch, Streaming, or Both?

We hope this brief overview of batch and stream processing has clarified the differences between the two processes and how they work. Each one has its more applicable use cases.  For data migration, batch ETL processing is more suitable.  For real-time data processing, streaming ETL is the best option.

Try SQLake for free for 30 days

SQLake enables you to build and run reliable data pipelines on streaming and batch data via an all-SQL experience. Sign up for an unlimited free 30-day trial. Use our sample data or data from your own S3 bucket – no credit card required.

To learn more, visit the SQLake Builders Hub, where you can browse our pipeline templates and consult an assortment of how-to guides, technical blogs, and product documentation.

If you have any questions, or wish to discuss this integration or explore other use cases, start the conversation in our Upsolver Community Slack channel.