The ETL (extract, transform, load) process is one of the most critical, and one of the most challenging, parts of enterprise data integration. But what if we told you there was a low-code ETL solution to your problems?
Data professionals often affectionately (and not so affectionately) call ETL “extremely tough to load.”
This process should not be confused with the ELT (extract, load, and transform) method of data processing.
The most common ETL challenges include:
- The need for manual work and advanced expertise at many stages of the ETL process.
- The steep learning curve associated with many ETL tools and platforms.
- The difficulties posed as the volume, variety, and velocity of enterprise data continue to increase.
The good news is that there’s an answer to every one of these problems: low-code ETL.
A growing number of ETL tools and platforms allow you to create production-ready ETL data pipelines in the cloud, without even writing a single line of code—and yes, that includes Xplenty.
However, not everyone is ready to jump on the low-code ETL bandwagon just yet. Many organizations remain attached to manually coding their ETL processes, unsure about the pros and cons of low-code ETL.
So what’s the verdict on low-code ETL platforms, and how do they stack up against coding your own ETL processes? In this article, we’ll discuss the question of low-code ETL vs. manual ETL before delivering a final verdict.
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Table of Contents:
- What is ETL Code?
- Low-Code ETL Explained
- Manual ETL Explained
- Low-Code ETL vs. Manual ETL
- A Final Word on ETL Code
What is ETL Code?
ETL means the Extract, Transform, and Load process of collecting and synthesizing data. The process collects and processes data from various data sources into a single data store used for business intelligence analysis.
Traditionally, the ETL process has been hard-coded. Programmers set instructions to extract data from its source, transform it into a usable format, and load the transformed data into the appropriate target system. Some organizations even synthesize data through manual processes and spreadsheets as it comes in.
These processes are no longer as viable as businesses scale their data pipelines and require data to be processed and stored more quickly and efficiently.
Hard-coding data introduces a lot of problems, including ongoing maintenance, invalid or incorrect data, limited ability to blend datasets, inflexibility and in general, it’s just more costly.
Luckily, some platforms, like Xplenty, have introduced low-code data that removes these roadblocks as companies scale their data structure and perform more sophisticated data analysis.
Low-Code ETL Explained
The term “low-code ETL” refers to a software platform that builds ETL and data integration pipelines nearly automatically, requiring little or no input from developers. Low-code ETL platforms often run in the cloud and usually have a simple, drag-and-drop visual interface, allowing users to easily understand the flow of data throughout the enterprise.
In the past few years, there’s been a lot of hype about so-called “low-code” or “no-code” solutions. According to the IT research firm Forrester, the low-code development platform market will reach a value of $21.2 billion by 2022, growing at an annual rate of 40 percent. What’s more, 45 percent of developers have already used a low-code platform or expect to do so in the near future.
Going in the low-code direction allows businesses to not only revamp their ETL process but also to move on to more sophisticated data transformations, like a data lake or data mart.
It also will improve data quality and make it simpler to blend disparate data types when data warehousing.
Manual ETL Explained
The term “manual ETL” refers to the traditional way of performing ETL: writing ETL code with the help of one or more ETL developers.
Manual ETL development requires a wide range of skills, including:
- Documenting requirements and outlining the ETL process.
- Creating models to describe the data extraction taking place during ETL.
- Formulating the architecture of the target data warehouse.
- Developing the data pipelines that transport information from source databases to the data warehouse.
- Testing the system and running regular performance checks.
Again, manual ETL has proved inefficient for organizations that rely heavily on large data sets to make decisions. Your ETL pipeline should be clean, uncomplicated and flexible. Data management can be so much easier for your organization with low-code ETL.
Low-Code ETL vs. Manual ETL: Major Differences
Now that we’ve defined low-code ETL and manual ETL, let’s discuss the major differences between these two alternatives.
1. Ease of Use
Writing your own ETL code isn’t a trivial task, even for experienced developers. As discussed above, ETL development requires many different data science and data analytics skills, as well as in-depth knowledge of one or more programming languages. The extraction process alone can be a huge headache.
Low-code ETL platforms are by design much easier to use than a manually written codebase. Even non-technical employees can design and execute ETL processes and create data models, thanks to an intuitive user interface that provides a visual depiction of ETL data flows.
The bottom line: Coding your own ETL processes is tempting but difficult, even for experienced developers. Low-code ETL platforms keep ETL development manageable and under control.
Let’s speak plainly: maintaining your ETL code manually sucks.
First, there’s the question of programming language. ETL code could be in SQL, Java, Python, Apache Pig, or any number of alternatives. Maintaining this code requires you to find an experienced ETL developer who speaks the right language fluently enough to understand it and make changes as necessary.
Second, your ETL code might be out-of-date or poorly maintained, creating a massive headache for anyone who tries to dive into the codebase. If fixing bugs and performing optimizations are difficult enough, doing version management and upgrades will be a nightmare.
The situation couldn’t be different for low-code ETL platforms, where maintenance is a no-brainer. You don’t need a degree in computer science in order to make changes—you can just use the straightforward, drag-and-drop user interface.
Maintenance is a no-brainer on ETL platforms. Changes are easy to implement, and they don’t require coding skills. Nonetheless, if you are a control freak who prefers to manage everything yourself even though it’s not comfortable, you’ll keep writing your own code.
The bottom line: ETL platforms require little maintenance, which makes them the winner in this category. Still, if you’re a control freak who prefers to have the last word on your ETL codebase, writing your own code might sound more appealing.
Coding your own ETL can be a huge benefit in terms of performance optimization. If you have an expert data engineer on board who knows your ETL processes, you can really fine-tune your ETL process to run as smoothly as possible.
Related Reading: How to Improve Your ETL Performance
But let’s not give the point to manual ETL development just yet. With the nationwide data science shortage, finding and training an expert ETL developer is both challenging and time-consuming. If you don’t have such a person already on staff, using a low-code ETL platform may produce higher-quality code than your average ETL developer.
Here at Xplenty, for example, some of our clients reported that our low-code ETL platform generated code that ran twice as fast as their own codebase.
The bottom line: If you already have an elite data engineer, your own ETL code will likely perform better. However, low-code ETL platforms can often produce code that runs faster than that written by your average developer. And it can be spread across your organization - every person can have real-time access to the ETL process.
If you write your own ETL code, you have to make sure everything is nice and neat. For example, you need to generate well-formatted logs, handle exceptions and errors, and store everything in one well-organized repository.
Low-code ETL platforms eliminate all of these concerns for you. Using an ETL tool allows you to manage the different data flows using visual representation. This way, all members of your team can see the big picture as well as the smaller details without needing to understand how to read code. It also facilitates reusing logic without having to rewrite the same code multiple times, and schedules jobs in a way that controls the dependencies between the components in the data flow. In the rare case that you’ll have to look at the codebase yourself, the code generated by these platforms is clean and easily comprehensible.
The bottom line: Low-code ETL platforms are more organized than writing your own code.
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Your manual ETL code may or may not be scalable, depending on which framework you use. However, the same is true if you use a low-code ETL platform, because it also relies on a framework—whether it’s Hadoop, Spark, or another open-source or commercial solution.
It’s important to make sure that your framework scales out rather than up. In other words, make sure you can easily add more nodes to the cluster, rather than having to upgrade a single machine.
No matter how big your budget is, a single machine will always have a silicone ceiling when it comes to adding more memory and CPU. This will inevitably lead to problems as the size of your data continues to grow. So whether you code your own ETL or use a low-code ETL platform, make sure that you can scale out.
The bottom line: In both cases, the scalability of your codebase will depend on the framework. Make sure that you choose a solution that allows you to scale out.
6. Workflow Management
Designing and managing workflows is an important part of the ETL process. Too many developers code workflows themselves, which requires a great deal of management and maintenance. Using a workflow management framework like Luigi is a better alternative, but even this option needs some manual coding.
ETL platforms provide workflow management that’s much easier to use, usually via a straightforward point-and-click interface. There’s no need to manage any framework when development and maintenance is a whole lot simpler.
The bottom line: Low-code ETL platforms provide easier workflow management than manual ETL development.
If you’re writing your own ETL code, hiring an ETL developer is an absolute must. According to the job search marketplace ZipRecruiter, the average full-time salary of an ETL developer in the U.S. is over $110,000.
Manual ETL development may or may not require additional costs. If you use a free open-source framework such as Hadoop or Spark, you’ll be able to keep your expenses to a minimum.
Costs vary when it comes to low-code ETL platforms. Xplenty’s ETL data integration platform keeps ETL costs lower than even the lowest developer salary.New Xplenty users get a free 7-day trial and a free set-up session with our implementation team.
The bottom line: Using a low-code ETL platform can decrease costs, since you don’t have to pay the salary of one or more ETL developers.
If you’re looking for flexibility, coding your own ETL is the way to go. Manual ETL development lets you write complex transformations and unique algorithms that low-code ETL platforms can’t provide through a simple user interface. If your ETL workflows require this type of niche data processing, flexibility isn’t just a benefit—it’s a must.
Still, you can enjoy the advantage of flexibility if your low-code ETL platform also lets you write your own code. Depending on the platform, some low-code ETL solutions may or may not let you perform custom data manipulations.
The bottom line: Writing your own code provides more flexibility, unless your low-code ETL platform also lets you make custom modifications to the codebase.
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A Final Word on ETL Code
As we’ve discussed throughout this article, using a low-code ETL platform has plenty of advantages. The benefits of low-code ETL platforms are:
- Higher ease of use
- More manageable in the long term
- Less maintenance required
- Better organized
- Simpler workflow management
- Lower costs
Want to explore the advantages of low-code ETL for yourself? Xplenty is a low-code ETL data integration platform that makes it easy to build pipelines for your enterprise data. Get in touch with our team today for a personalized demo and a free 7-day trial of the Xplenty platform.