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The Costs and Disadvantages of Building an ETL From Scratch

By Posted in Engineering July 25, 2022

ETL and pipelines are at the center of DataOps as they determine a company’s success in managing data. One way you can increase your chances of failing at data management is by building an ETL process from scratch without using a platform like StreamSets. In-house ETL may provide specific custom functions, but it is error-prone and requires more time to build.

In this article, you will learn why building an ETL from scratch may be a bad idea for your company.

Building an ETL from Scratch Requires More Programming Skills

Data is a sensitive topic, and best practices have to always be implemented when handling it. ETL developers need more skills and experience to implement the ever-increasing best practices that combat cybercrime.

Learning ETL development takes about 50 hours, but one needs the following background skills to join the ETL development:

Programmers also have to be equipped with the right programming language and tools. Sometimes they are forced to learn a new language in order to build the desired custom ETL pipelines. They need programming languages such as:

  • Python
  • Perl
  • Bash

Acquiring the above skills takes time. But, for a large company, this isn’t the main problem or focus; the problem arises when you have to find a new ETL developer or replace an ETL developer. The demand for data engineers is very high.

A survey by Mckinsey Analytics has shown that 60% of the respondents find it hard to recruit data engineers due to severe talent shortages.

Using ETL platforms to build and maintain ETL processes removes the burden of hiring high-end data engineers because ETL-as-a-service platforms provide drag-and-drop functionalities. They also give you pre-built connectors that have been fine-tuned to follow data engineering security conventions.

In-house ETL is Expensive to Maintain

Making changes or repairing an ETL process takes time. The debugging and testing are inevitable if you are coding and pushing the program’s limits; bugs will always show up in your code. The debugging process can turn a 2-week project into a 2-month project. This is because developers will be eliminating bugs they have no experience working on and the highlight is that they do not have any technical support when building custom ETL and connectors from scratch. 

An in-house ETL offers a great opportunity to create your own custom and flexible features. However, this comes at a high cost because in-house ETL solutions have hidden, varying costs such as:

  • Cloud hosting fees
  • Hardware costs
  • Troubleshooting costs
  • Data volume charges

In-house ETL tends to be cheap when building but the real costs show up when it’s time to monitor and update them.

In addition, labor costs for high-caliber ETL developers are high. An ETL developer is paid $106,338 on average in the United States of America. Looking at the fact that there is a shortage of data engineers companies often compete for data engineers. The companies that win these data engineers are the ones who raise their offers. If you want to build a good in-house ETL platform from scratch you have to hire multiple high-caliber ETL developers who earn around $166,400 a year each.

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In-house ETL Needs More Time to Build

Building your own ETL process takes weeks due to a lack of support and usage of different techniques. When you use a platform like StreamSets you don’t have to start from zero. There are tools such as debugging tools, batch scheduling, and continuous monitoring. What you are supposed to build is already available. All you have to do is to choose what’s best for you.

In-house ETL May Not Comply with Security Standards and Best Practices

ETL-as-a-service platforms give you secure pre-built connectors because they eliminate vulnerabilities found in connectors built from scratch. Building an ETL platform from scratch requires a lot of work, starting from data modeling and designing an architecture to coding the ETL. It is easy to get lost and default to techniques that are not secure. But, skills aren’t the leading cause of insecure ETL processes; communication breakdowns and tight deadlines lead to developers compromising design and security features.These things are inevitable when building ETL processes from scratch.

ETL-as-a-service Platforms Eliminate the Downsides of In-house ETL

An ETL-as-a-service platform provides the tools and processes that help you build your ETL process smoothly. In addition, it also offers pre-built connectors and data transformers. You don’t have to start from scratch. Building an ETL pipeline using these platforms is also easy as they provide technical support in case you’re stuck or need more clarity about a particular function.

These platforms have graphical user interfaces that let you drag and drop components that help you track and manage your data resources in one single platform. You will also get automated data migration and warehouse metrics and the ability to collaborate with your teammates on these platforms.

ETL platforms also provide amenities and developer tools such as:

  • SQL editors and language support
  • Low-code ETL
  • API generation

StreamSets is one of the data integration platforms that is simplifying ETL development. With StreamSets you can use a drag and drop UI to build an ETL. In addition, StreamSets removes the stress of changing data formats. This is good for businesses that import raw data from multiple sources.

Conclusion

ETL-as-a-service platforms give you speed where your company is prone to lag and struggle since they give you pre-built connectors and data transformations. Also, you get the opportunity to focus on the right components and build new tools that collect quality data and simplify data analysis.  For help with your data integration needs, reach out to StreamSets and be connected with an expert.


This article was written by Boemo, a software developer who embraces innovative approaches. He likes diving deep into complex concepts in order to learn and write articles that can help the reader understand complex methodologies in a simple and fun way.

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