Feature image ETL Streaming

ETL Streaming:
The Future of Data Processing

By Brian Laleye · November 1, 2022 · 5 min read

ETL stands for Extract-Transform-Load. Data from various sources are processed and made ready for future operations using ETL.

ETL streaming is a new type of data processing that is becoming increasingly popular.

ETL streaming is absolutely crucial for businesses today. By incorporating a streaming data analytics solution, businesses can make smarter and more informed business decisions in real-time.

This article will explore the meaning, benefits and drawbacks of ETL streaming, as well as its future prospects.

What is ETL Streaming?

ETL stands for extract, transform and load. Let’s break that down: you can use an ETL tool  to “extract” data from one system and transfer that data to another system where you can “transform” it, then finally “load” it into a third system.

Way back when, ETL was designed as a batch process – meaning that the steps happened infrequently and in specified intervals (like once a week).

But with modern data volume and availability challenges, companies need to rethink the way their data flows in an agile manner – to be more real-time.

ETL Streaming is a new way to access data in real time since it allows data to be accessed in real-time and combined with other data sources to create new business insights.

With ETL Streaming, you will be able to have your data set up in minutes rather than months or years!

Benefits of ETL Streaming

ETL streaming offers a number of benefits over traditional data processing methods. 

Perhaps the most significant benefit is that it allows for real-time processing of data. This is in contrast to traditional ETL methods, which can often take hours or even days to process data.

In addition, ETL streaming is much more flexible than traditional methods, as it can easily be adapted to changing needs.

Finally, ETL streaming is generally much easier to set up and use than traditional methods.

Drawbacks of ETL Streaming

There are some drawbacks to using ETL streaming, as well.

One significant drawback is that it can be quite resource-intensive.

This is due to the need to constantly process data in real-time. As a result, ETL streaming can often put a strain on resources such as CPU and memory. 

In addition, ETL streaming can be difficult to manage and monitor, as there is often a large amount of data to keep track of. As a result, it is important to have a good understanding of ETL streaming before attempting to use it.

Another drawback of ETL streaming is that it can be difficult to scale. This is because the data processing needs to be done in real-time, which can limit the amount of data that can be processed at any given time.

In addition, ETL streaming can be more expensive than traditional ETL, as it requires more resources to run. As a result, it is important to carefully consider whether ETL streaming is the right solution for your needs.

ETL streaming can be a great solution for some needs, but it is important to understand the pros and cons before deciding whether it is the right solution for you.

Future of Data Processing

Despite some drawbacks, ETL streaming is generally seen as the future of data processing.

This is due to its many advantages over traditional methods. In addition, ETL streaming is constantly evolving, and new software and technologies are making it more accessible and easier to use.

As a result, it is likely that ETL streaming will continue to grow in popularity in the years to come.

There are many reasons why ETL streaming is seen as the future of data processing. 

One reason is that it is much faster than traditional methods. Moreover, it is more flexible and can be easily customized to meet the specific needs of a business. 

Additionally, ETL streaming is more reliable and accurate than traditional methods.

Finally, ETL streaming is constantly evolving, and new software and technologies are making it more accessible and easier to use with No Code / Low Code tools.

Conclusion

At RestApp, we’re building a Data Activation Platform for modern data teams with our large built-in library of connectors to databases data warehouses and business apps.

We have designed our next-gen data modeling editor to be intuitive and easy to use.

If you’re interested in starting with connecting all your favorite tools, check out the RestApp website or try it for free with a sample dataset.

Discover the next-gen end-to-end data pipeline platform with our built-in No Code SQL, Python and NoSQL functions. Data modeling has never been easier and safer thanks to the No Code revolution, so you can simply create your data pipelines with drag-and-drop functions and stop wasting your time by coding what can now be done in minutes! 

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Brian Laleye
Brian Laleye
Brian is the co-founder of RestApp. He is a technology evangelist and passionate about innovation. He has an extensive experience focusing on modern data stack.

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