October 3, 2024 • 10 minute read •

10 Reasons Why No-Code Solutions Almost Always Fail

No-code solutions sound easy – until they aren’t. Here’s why they often fail and what you can do about it for your data engineering.

Name: TéJaun RiChard
Handle: @tejaun

No-code development platforms are the shortcuts of data engineering. They promise quick pipeline management, orchestration, fast deployment, and quick wins without writing a single line of code—perfect for non-technical users.

However, even the best no-code platform will fall short when building scalable, maintainable, complex data pipelines and platforms.

In this piece, we’ll go over why no-code solutions often fail, some alternatives, and how to make a better choice for your business.

Why No-Code Solutions Fail

Limited Flexibility and Customization

No-code platforms limit users to pre-built components and workflows, making implementing custom business logic or complex scenarios hard. This means you usually can’t build truly custom solutions.

Imagine your e-commerce company is using Airbyte to replicate data from a source API to Snowflake, but Airbyte doesn’t have the connector for this API. You’d run into a wall trying to implement the data transfer because you’d have to write code for the connector yourself or rely on the community or Airbyte team to do it for you. At least with Airbyte, you can write the code for the connector, but some tools don’t have this flexibility. The rigidness of the platform would limit your options and prevent you from integrating data effectively.

Poor Scalability

As your e-commerce business grows and processes get increasingly complex, no-code solutions may struggle to keep up because they often lack the reliability for enterprise-grade data operations, experiencing performance issues and bottlenecks.

For instance, you might use a no-code platform for inventory management, only to run into slowdowns or crashes during high-traffic events (Black Friday, Christmas, etc.) because the platform can’t handle the increased data volume and transactions.

Limited or Shallow Integration Capabilities

No-code platforms might offer integrations, but when they do, they aren’t as in-depth or flexible when connecting to multiple data sources and other tools. When integrations exist, no-code frameworks often sacrifice depth for breadth to reach a broad audience. The lack of connection normally means data silos and inefficiency across your teams.

Your finance department might use a no-code platform for reporting but find that they aren’t able to connect and sync data from multiple sources like your ERP systems, CRM platforms, and custom databases. As a result, they have to enter data manually and expose themselves to potential errors.

Security and Compliance Issues

No-code solutions offer little access or control over the underlying infrastructure or platform security protocols. This means they might only sometimes meet security and compliance standards, putting your organization at risk of data breaches or regulatory issues.

For instance, if you’re handling sensitive customer information, you might be unable to ensure that you’re entirely PCI DSS-compliant using a no-code platform, risking customer privacy and potentially facing legal and financial penalties.

Vendor Lock-In

Because no-code development platforms are rigid and difficult to integrate, users often get locked into the platform’s ecosystem, making it hard and expensive to move to other solutions if needed. This lack of portability limits future options and flexibility and can hurt an organization’s ability to adapt or migrate its needs as they evolve.

Returning to your e-commerce company – let’s say you’re using a no-code platform for inventory management. As your business grows, you’ll probably find that the platform can’t handle your increased data volume or that it’s missing a feature you need. However, moving it to a more compatible solution becomes tedious and complicated because your processes and data are configured within your platform’s specific ecosystem.

You might even have to rebuild the entire inventory management from scratch on a new platform. This means manually recreating workflows, re-entering data, and retraining your staff on a brand-new system. The time, cost, and potential for errors during this transition are big and can hold your business hostage to the limitations of your no-code choice.

Platform-Specific Skills with Limited Transferability

No-code platforms often create isolated pockets of expertise due to their unique interfaces and workflows. This platform-specific knowledge can lead to several issues:

Each no-code platform has its way of doing things, making it difficult to establish consistent practices across different projects or teams.
As projects grow in complexity, the limitations of platform-specific knowledge become more apparent, potentially hurting your ability to scale.
Onboarding new team members or switching to a different platform can be time-consuming and expensive due to the need to learn platform-specific skills.

While this isolation can occur in high-code solutions, they also have a big advantage: platform owners can define frameworks that standardize practices, making the knowledge more broadly applicable across different pipelines. No-code environments normally don’t have this sort of standardization.

The forced specialization limits team flexibility and makes it hard to adapt to new tools and platforms in the future. On top of that, your developers might become (or already be) proficient in a particular no-code environment but struggle because their skills don’t translate well when switching to a different solution or returning to traditional code.

Knowledge Bottlenecks

On top of your team’s issues with platform-specific skills, no-code tools often create knowledge silos where only a few people in the team fully understand the ins and outs of it.

When key members with specialized no-code platform knowledge are unavailable, they create huge bottlenecks or even voids in the team's capabilities. If they leave the organization altogether, that critical information basically walks out the door with them.

If your business relies on a single person who knows the no-code CRM setup, you’ll run into crippling challenges and disruptions if that person leaves or is unavailable. Other team members might struggle to maintain the system and modify or scale it to meet the needs of the business.

Tech Debt Accumulation

Inevitably, companies evolve. Older projects might become incompatible or hard to maintain, and you’ll accumulate tech debt, some of which can’t be fixed without big reworks.

While this is an issue for all types of software development approaches, no-code platforms make this worse due to the additional barriers to addressing this problem they bring by way of the limited flexibility for things like refactoring or updating the underlying system and potential compatibility issues that come with being dependent on the platform (and its updates) as opposed to other approaches where you typically have more control over the tech stack.

Projects built on that platform might become increasingly harder to maintain and update as your company evolves. Maybe new features and integrations aren’t compatible with an older version of a no-code platform you’ve been using for order processing. This would put you in a situation where you must use a lot of time and resources to overhaul your system – all because your platform doesn’t let you make incremental updates or implement custom modifications.

Inconsistent Practices

Without governance, no-code platforms can lead to inconsistent (or outright abandonment of) software development best practices across an organization, making it hard to maintain and scale projects.

Different departments in your corporation may use different no-code tools for project management, for instance. This would lead to inconsistent data formats and processes in the organization, complicating data for company-wide reporting.

Hidden Costs and Financial Overhead

No-code solutions seem cost-effective initially, but they can ramp up and become surprisingly expensive as your organization scales. This is because no-code platforms typically have rigid pricing structures that don’t align well with businesses as they grow.

Many of these platforms charge based on the number of users, data volume, and pipelines—costs that can increase quickly as data needs increase and your company grows.

Additionally, the lack of customization in no-code platforms and their usual ‘one-size-fits-all’ approach can result in paying for features you don’t need or use. Conversely, a custom solution or more flexible platform would let you pay only for the resources and features you’re using.

You also have to consider the potential need for third-party integrations when using no-code platforms to overcome some of the other limitations we previously mentioned. These integrations often have their own pricing models and structures, which can increase your overall expenses.

In the long run, you might find that your e-commerce company is spending more on a no-code solution than it would have if you had just developed and maintained a solution that fit your unique needs and scaled with your growth.

The Alternatives

Now that we know some of the pitfalls associated with no-code platforms, let’s look at some of the alternatives and see how they measure up in terms of flexibility, scalability, and ease-of-use.

No-Code vs. Traditional Code

Traditional code means building software using programming languages like Python, Java, or C++. Going into the trenches with this approach gives you full flexibility and control and is usually great for when you need to make a complex and custom solution that performs well and scales.

The main problem with traditional code platforms, though, is that they take a lot of time compared to no-code ones and require specialized skills (skills that even Liam Neeson might not have), which might end up costing a lot for smaller projects.

Normally, large enterprises or tech companies with extremely complex requirements and equally big budgets will look to this as their solution.

No-Code vs. Low Code

Low code platforms offer visual development tools and a bit of custom code when needed. They normally provide a good blend of speed and customization that is good for organizations that need flexibility, but they often have issues with scalability and vendor lock-in, just like no-code platforms.

Mid-sized companies or departments within large organizations that need to balance fast development with minor customization typically find these solutions useful.

No-Code vs. Hybrid Code

Hybrid code frameworks combine the best visual development and traditional coding, offering no-code speed for simple tasks and the backing power of custom code for complex operations.

One of the strengths of hybrid code solutions is their flexibility and ability to cater to different skill levels within an organization. For example, skilled data engineers can continue to write their pipelines in Python using a hybrid code platform. At the same time, a custom Domain-Specific Language (DSL) can be created for less technical stakeholders who would rather configure things with YAML.

While hybrid code solutions typically require a learning curve and careful integration for you to fully benefit from them, they offer incredible versatility and can adapt to different business needs.

This solution is suitable for growing companies and organizations with different technical needs that want fast development for simple stuff and the ability to handle complex custom requirements.

Our Recommendation

We believe that hybrid code approaches are best for data engineering projects because they combine the ease-of-use and fast deployment of no-code platforms with the flexibility and power of traditional coding, making them suitable for companies of all sizes.

Hybrid approaches let organizations use visual tools for simple tasks and prototyping while using custom code for complex operations. This balance lets teams work efficiently without sacrificing the ability to handle unique business needs or scale. Plus, they can grow with your company and adapt to increasing complexity and scaling, meaning that startups and large enterprises can benefit from them.

Use Dagster for the Best of Both Worlds

Dagster is a code-first solution for building data platforms. While it’s not a low-code or hybrid solution out of the box, the flexibility of the Dagster core APIs and architecture allows platform owners to add layers of abstraction and create custom tooling and DSLs to enable their users and engineers.

This flexibility allows Dagster to be used as a hybrid platform for both technical data engineers and those who prefer low-code. Platform owners can set standards and create custom abstractions to meet the needs of their organization, using Dagster’s full software development lifecycle.

Note that Dagster provides the tools and flexibility to create a hybrid environment, but the implementation of low-code or no-code abstractions is up to the platform owner. This way organizations can tailor their data platform to their needs, code-first and low-code as needed.

Dagster also lets you control your DSL and low-code solutions and get visual and code-based development.

Here’s how:

Dagster’s asset-based approach lets engineers use software-defined assets to declaratively specify pipeline behavior and structure to effectively handle unique business logic and edge cases — meaning flexible and customizable pipelines.
Dagster supports parallel and distributed execution so that you can scale from a laptop to an entire production-grade cluster. You can also deploy it in different environments (including Kubernetes), which means efficiency and scalability.
Dagster plays nice with many tools and platforms (e.g., Spark, dbt, various cloud services) and supports different APIs — meaning easy integration and customization to your specific needs.
Dagster is compliant by design and gives detailed insights into pipeline runs so you can see who did what and when within your data processes — meaning data governance and compliance for your teams.
Dagster is open-source and can be deployed on-prem, in-cloud, or in a hybrid setup, meaning there is no vendor lock-in or control of your infrastructure.
Dagster has a growing community, extensive documentation, active support channels, and Dagster University to help you find and share knowledge -- meaning a large user base with lots of resources to reduce the learning curve.
Dagster’s declarative configuration, asset-based approach, and modularity help isolate and manage different parts of your pipeline and streamline updates and maintenance — reducing tech debt.
Dagster’s declarative configuration and asset-based approach also ensure consistency across the organization and give a straightforward way to define and manage data assets — meaning standardization for management and scaling.
Dagster’s asset-based approach and extensive documentation help distribute knowledge across the team, meaning there are no knowledge bottlenecks.
Dagster is open-source and has flexible deployment options, letting you avoid recurring licensing costs and choose the infrastructure that fits your needs and budget — meaning lower financial overhead over time.

Conclusion

No-code platforms can provide quick wins but often fail to deliver scalable, maintainable, and complex data pipelines. Hybrid code approaches, including using Dagster as a hybrid platform, are more reliable and flexible for managing data assets.

When choosing a solution for your data engineering needs, consider the long game. Scalability, complexity, and the requirements of your data operations should be at the forefront of your mind. A hybrid approach might give you the best speed, flexibility, and power balance while helping you manage costs.

Interested in seeing how a hybrid approach can improve your data operations?

Go to our platform page for more info or start a free trial of Dagster+ to put together your first project.

We're always happy to hear your feedback, so please reach out to us! If you have any questions, ask them in the Dagster community Slack (join here!) or start a Github discussion. If you run into any bugs, let us know with a Github issue. And if you're interested in working with us, check out our open roles!

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