The low-code/no-code trend has been up and coming for the past few years, with the promise of building applications, websites or software with minimum or no coding experience. This allows faster development and for a lower price – instead of financing software engineers and software architects, almost anyone (after a few weeks or couple of months of training) can build whatever the organisation needs.
These low-code methods can be found in data engineering solutions too. The rise of ETL (extract, transform, load) tools aim to simplify data management and to build data pipelines without extensive coding experience. This is where data engineering solutions come into play, with the help of low-code/no-code ETLs.
In this comprehensive guide, we’ll explore:
• What data engineering solutions are
• Why they’re critical for businesses
• The rise of low-code/no-code platforms
• Real-world use cases
• Key benefits
• Choosing the right solution for your needs
What are data engineering solutions?
Let’s dive deep, but start from the beginning. From driving decision-making to enabling predictive analytics, modern businesses rely heavily on data to maintain a competitive edge. But raw data alone isn’t enough - it needs to be organised, accessible, and actionable.
Data engineering refers to the process of designing, building, and maintaining systems that collect, store, and analyze large volumes of data. The goal is to create robust data pipelines and architectures that deliver clean, reliable, and timely data to users and applications.
Data engineering solutions are the tools (such as the ETL), platforms, and services that help businesses build these pipelines efficiently. These can range from open-source frameworks like Apache Spark and Kafka to fully managed cloud services such as Microsoft Azure Data Factory, AWS Glue, and Google Cloud Dataflow.
In recent years, low-code and no-code data engineering solutions have emerged, enabling non-technical users to participate in building and managing data pipelines without needing advanced coding skills. This means, that instead writing codes, these ETL tools use a visual, user-friendly interface to design, build or manage data. Drag-and-drop functionality (just like when you move folders or upload photos), reusable templates (such as WordPress) and pre-built connectors help you to work fast and efficiently.
Types of data engineering solutions: traditional, cloud-native and low-code
Before we dive deep into the low-code/no-code data engineering solutions and their benefits, we need to mention the other solutions.
1. Traditional code-first solutions
This is the “ancient” data engineering solution, which we all think about when it comes to data engineering. For organisations with in-house engineering expertise, traditional solutions like:
- Apache Airflow (workflow orchestration)
- Apache Spark (large-scale data processing)
- SQL scripts & stored procedures are still widely used.
These solutions provide maximum flexibility and customisation but require technical skills to maintain, expert software engineers and data engineers.
2. Cloud-Native Managed Services
Cloud providers have developed fully managed data engineering platforms, including:
- Azure Data Factory (ideal for Azure cloud ecosystems)
- AWS Glue (serverless ETL for AWS)
- Google Cloud Dataflow (for real-time and batch data processing)
These reduce infrastructure overhead and provide scalability on demand. If you want to know more about how cloud native applications work, we recently wrote a guide about cloud native applications.
3. Low-code/no-code platforms
For businesses wanting to accelerate development with fewer technical barriers, low-code/no-code platforms include:
- Alteryx
- Knime
- Microsoft Power BI Dataflows
- Google Cloud DataPrep
- Apache NiFi
These platforms offer drag-and-drop interfaces, pre-built connectors, and templates, allowing data analysts and business users to build pipelines without coding.
The rise of low-code/no-code in data engineering
Low-code/no-code data engineering solutions democratise access to data workflows. With a visual interface and minimal code, even teams without dedicated data engineers can:
- Extract data from multiple sources
- Clean and transform datasets
- Feed data into BI tools or ML models
Example:
A marketing team could use a low-code tool to automatically pull data from Google Analytics, clean it, and push it into a dashboard without writing any code. While low-code tools accelerate delivery and reduce IT bottlenecks, they’re typically best suited for small to medium-sized projects or for prototyping before full-scale engineering implementation.
Pros and cons of low-code and no-code in data engineering
Without the constraints of actual coding, these low-code no-code methods increase productivity in data engineering. These automated features also decrease the mistakes and errors caused by humans, which increases data quality. These also allow data engineers to focus on higher-level problem solving – instead of taking so much time to code, they can use their time to planning or strategic initiatives. It is also cost-effective for small and mid sized businesses, as it reduces the dependence on specialised developers.
However, one of the biggest downside of the ETLs is that these tools are less flexible when it comes to complex or large-scale systems. As with every automatisation and visually appealing user-interface, you cannot do anything in these tools you want to. These cannot be customised for specific use cases therefore they might not provide you exactly what you need for your organisation to thrive.
Real-world use cases of data engineering solutions
1. Retail
A large e-commerce platform uses AWS Glue to collect and transform purchase data across regions, delivering personalized product recommendations in real-time.
2. Finance
A fintech startup leverages Azure Data Factory for ETL pipelines that consolidate transactional data for real-time fraud detection.
3. Healthcare
A hospital network uses Knime, a no-code tool, to combine patient health records from various systems to improve diagnostics and operational efficiency.
The future of data engineering solutions
As businesses continue their journey toward digital transformation, the role of data engineering will only grow. The trend is clear:
- More automation
- Greater accessibility through low-code/no-code
- Deeper integration with AI and ML systems
Companies that invest in robust data engineering solutions today will set themselves up for competitive advantages tomorrow.
Key takeaways
Data engineering solutions are no longer optional - they are a business necessity. Whether you’re a small startup or a global enterprise, choosing the right mix of traditional, cloud-native, and low-code/no-code tools can accelerate your data-driven success.
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