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When it comes to data pipelines code was always the go-to. Code allowed us to build exactly what we wanted when we wanted and how we wanted it. But as processes needed to become faster to meet TTM requirements and faster data delivery of needed business insights, we started to realize the drawbacks. From the length of time required for maintenance and debugging to the issues related to collaborating with different company stakeholders, other options were needed – and the industry delivered.
Now DEs whether they are working as individuals or as teams need to start considering the different code classes and how they can utilize them, among themselves and/or with external stakeholders, to build data pipelines with the most impact.
With the many solutions that have become available allowing for the combination of code and no code offerings – how is this going to play out for Data engineers themselves? In this article we will go over the different types of solutions available today and how they will affect those in the data engineering space from a data engineer’s perspective.
Code is King in Data Engineering
Code is king. Programming languages such as Python and Scala have exploded in popularity over the years as the go-to languages for building data pipelines. DEs have unmatched levels of flexibility with code to build whatever their heart desires. We now have entire ecosystems solving a myriad of pipeline-related problems.
Code works very well for/with DEs working as individuals but introduces some complexity once you’re a team working on a data pipeline. Questions such as “How do we collaboratively build this pipeline?” or “How do we not override each other’s changes?” and many others start to pop up.
To overcome this, we DEs have now started integrating the use of version control options like git (Github, Gitlab, etc.) which is very popular in the Software Engineering world to collaborate on projects (intra-team collaboration). Different git workflows, code reviews, and Pull Requests are now quite common in the field.
While these options make collaboration easier, they introduce some overhead when managing workflows. It is important to introduce and enforce a culture of regular and well-commented commits. DEs also have to introduce processes for conducting code reviews; either peer-to-peer or from the team lead. At the end of the day, however, intra-team collaboration around code leads to higher-quality data pipelines.
On the flip side, cross-team collaboration can be negatively impacted when stakeholders don’t have programming capabilities. It is at this point where the most they can do is update an issue board – if one exists that is. They can’t really get into the nitty-gritty of the implementation.
This means they need to pretty much trust that the DEs understand their needs and will produce good data pipelines. Even when the stakeholders are coders e.g. Data Scientists, processes need to be set up for them to be part of the development workflows without negatively impacting the codebase.
Documentation of how everything works also needs to be actively set up and maintained… something that almost always gets neglected.
No Code solutions were built to try and solve the issues with code. No Code attempts to remove the overhead to manage collaboration while also eradicating the need to know the nitty-gritty of technical implementation. Anyone (technical or otherwise) can take part in the process of building data pipelines!
Today, we have access to a lot of these UI-driven drag-and-drop specialized solutions for each step of data pipelines we build making it easy to clean and prepare data, and with the BI tools available today, now anyone can visualize the data as well.
These options make cross-team collaboration especially possible. A simple UI (usually web) gives all team members access to the data sources that they can interact with and build on as needed. The aforementioned marketing team now has access to their ad data but also the production data and they can play with both! The non-technical stakeholders can also see what the DEs are doing and contribute/request changes as desired.
No Code can also have inbuilt documentation capabilities. The burden of documenting every single thing is taken away from the DEs who can focus on building, letting stakeholders utilize the documentation for the knowledge they require.
All in all, no code options are more easily accessible to other employees in the company empowering more cross-team collaboration than code offers. However, as much as anyone can take part in building a data pipeline, that doesn’t mean everyone should take part.
Among the aforementioned, no-code solutions are also quite inflexible in their abilities (you can only do what the vendor has made available) hence limiting how creative DEs can be in their solutions, and in some cases, halting operations in their entirety as tasks cannot even be completed due to a ‘little’ missing feature.
Code and No Code – The Combination Makes the Perfect Transformation
Write code when you desire, and use no-code solutions as an option.
Platforms like Datorios offer DEs a solution that harmoniously combines Code and No Code in a single tool for pipeline development.
Datorios for instance provides you the (No Code) connectors you need to read from a myriad of data sources and write to lots of destinations. You can then define complex transformations and joins of these data sources using code!
Users are presented with a responsive UI with clear and obvious steps in the pipeline. Non-technical users can use pre-built transformations in The Mapper feature to transform the data
… while DEs can write code in the Code Capsule feature!
These features also offer the ability to validate their work.
With Code/No Code features, DEs save a lot of time such as the time that used to be necessary to focus on things like data consolidation which involves a lot of doing such implementations such as connectors to sources and destinations and all the transformation logic in between.
DEs now have time to collaborate with stakeholders, understand their needs, and implement the best solution.
Non-technical stakeholders can also set up their own pipelines but need to work with DEs to ensure they’re transforming data correctly! The result is easy-to-access data (and its tooling) while controls and consistency are maintained.
We’ve explored the pros and cons of code and no code options and we’ve looked at what this means for Data Engineering, but how will this play out for DEs as we move forward?
Now that no code is available in an industry once dominated by code, DEs are better able to collaborate, have more time to focus on company-value-adding tasks, and have easier methods to perform routine tasks like maintenance and debugging. To put it simply, we’ve found that a mix of the different methods plays out the best for DEs and the companies they work for alike. If this code/no code solution can be offered in an easy-to-use interface made to offer complete visibility of all processes, immediate feedback loops, and even faster, more accurate business insights, then even better!
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