Batch ETL vs Streaming ETL

ETL stands for extract, transform, and load data from a  variety of sources, and this process can be done in two ways: either in batches or in streams.

ETL tools help you integrate data to meet your business needs, whether they are operated from traditional or data warehouses.

All types of integration projects require an ETL process to extract, transform, and load data, regardless of whether the data is extracted, transformed or loaded. The advantage of using ETL tools is that they optimize ETL processing. Modern ETL tools are designed to process structured data from a wide range of sources.

So in this article, we will discuss what exactly ETL is, and go through the difference between Batch ETL and Streaming ETL, and when to use each one based on your business needs.

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 your data warehouse system. Data is collected from various sources, transformed, and being loaded into a data warehouse.

If you choose a good ETL tool, you can extract data from multiple sources in your system and aggregate them in a data lake, transform it into a queryable format, and start analyzing the data right away with your preferred BI tool, then load it into the database or Warehouse of your choice.

Transformation and loading can be done simultaneously in the target database, and there is no need for an ETL server.

The ETL process is completed when the data Warehouse is updated with the latest data and the ETL is performed to keep it up to date. Data can be recorded quickly and efficiently, especially when speed is critical.

A typical ETL process refines different types of data and then delivers it to a traditional data repository. After the data transformation, the ETL process can then be fed back into the traditional Warehouse, with data from the original source database then fed into the data warehouse.

Batch ETL Explained

Batch ETL processing basically means that users collect and store data in batches during a batch window. This can save time and improves the efficiency of processing the data and helps organizations and companies in managing large amounts of data and processing it quickly.

The Data Warehouse executes batch tasks in any order, and the workflow for each batch is defined by the order in which they are executed.

In some cases, the batch data becomes so large that the ETL tools simply cannot process it fast enough. In fact, many IT managers are now finding it difficult to meet their existing infrastructure requirements for batch processing while at the same time reducing the batch window for ETL processing bottlenecks.

Typically, data from a variety of company databases is loaded into the master scheme of the data warehouse in batches once or twice a day. 

ETL tools are designed to focus narrowly on the batch connection of databases in the data Warehouse in batches once or twice a day. 

Building an enterprise-wide ETL workflow from scratch can be challenging, as we typically rely on ETL tools like Stitch to simplify and automate much of this process.

The system must perform ETL in the data stream using batch processing and should handle high rates to scale the system. With an ETL training layer, you have to take care of things that are difficult to separate from S3, such as the size of your data lake and the number of data streams. All of this needs to be done right if you want reasonable performance at reasonable cloud costs.

Streaming ETL Explained

Streaming allows you to stream events from any source, and it helps you make changes to the data while you’re on the run. The entire process can be in one stream while you stream data, whether you stream data to a data warehouse or a database.

Streaming ETL process is useful for real-time use cases. Fortunately, there are tools that make it easy to convert periodic batch jobs into a real-time data pipeline.

The results of the streaming processing can be loaded into a data lake based on AmazonS3. This is a powerful and scalable ETL pipeline that you can use in your core business applications.

Transformation and load data can be extracted using a stream-based data pipeline to perform SQL queries and generate reports and dashboards. 

The streaming application ETL can extract data from any source and publish it directly to the streaming ETL application, or the source can publish the data directly to the streaming ETL application and extract it from another source.

Upsolver is a popular tool for real-time data processing. You can extract data with and allow ETL to stream in the cloud in real-time, without the need for complex systems that require coding.

The ETL architecture for streaming is scalable and manageable, offering a wide variety of ETL scenarios, including a variety of data types.

Conclusion

By now you should have a better understanding of the two processes and how they work. Each one has its own use cases. If you want real-time data processing then streaming ETL to process the data in real-time is the best option. 

But If you’re going to do a data migration, then a batch ETL processing option is more suitable for your needs. 

 

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