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HIGHLIGHTS
- Migrating extract, transform, load (ETL) jobs from legacy platforms to cloud-native solutions is a critical step in an enterprise’s initiative to modernise data ecosystems.
- The future of ETL migration lies in leveraging artificial intelligence (AI)-driven automation while maintaining human oversight and custom components embodying practitioner expertise.
- Large language models (LLMs) offer a promising way to automate ETL job conversions, but challenges like runtime errors, hallucinations, and token size limitations remain.
- A hybrid approach combining LLMs, domain expertise, and custom tools can address these challenges effectively.
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NextGen ETL on cloud
In today’s fast-paced digital landscape, enterprises are increasingly moving their data ecosystems from on-premises infrastructure to hybrid or cloud-native architectures.
This shift is driven by the need for scalability, flexibility, and cost efficiency.
A critical part of this transformation involves migrating extract, transform, load (ETL) processes from legacy platforms like Informatica PowerCenter, IBM DataStage, and AbInitio to modern, cloud-agnostic solutions such as PySpark. PySpark, with its combination of Python’s simplicity and Apache Spark’s distributed processing power, has become a go-to choice for modern data processing.
However, this migration is far from straightforward. Legacy ETL tools rely on graphical interfaces and metadata-driven workflows, while PySpark requires programmatic implementation using Python and Spark SQL. The structural and functional differences between these technologies make ETL migration a complex and resource-intensive process.
ETL migration using LLMs
Large language models (LLMs), such as GPT, have emerged as powerful tools for automating ETL job conversions.
These artificial intelligence (AI) models can analyse XML files exported from legacy ETL tools and generate PySpark SQL scripts, offering several advantages:
- Automation: Reduces manual effort by generating code automatically
- Efficiency: Accelerates the conversion process compared with traditional methods
- Scalability: Handles large volumes of XML files with consistent quality
By acting as co-pilots for developers, LLMs can streamline the migration process, enabling faster and more efficient transitions to modern data platforms.
Challenges
While LLMs hold great promise, they are not without limitations.
Runtime errors and inaccuracies often arise in the generated PySpark SQL code, stemming from the following challenges:
1. Hallucinations and incomplete code
LLMs may misinterpret the flow of ETL processes, leading to incorrect or incomplete outputs. Instruction overload (too many instructions under the same prompt), mapping flow issues, as well as complex column renaming logic can also result in hallucinations.
2. Complex transformations
Intricate logic, such as reusable transformations, custom SQL overrides, and sequence generators, can be difficult for LLMs to interpret accurately.
3. Token size limitations
Large XML files often exceed the token size limits of LLMs, requiring additional processing techniques like recursive chunking.
4. Platform-specific issues
- Informatica PowerCenter: Challenges include handling router transformations, source qualifier overrides, sequence generators, router transformations hanging sources and large XML files.
- IBM DataStage: Issues arise with surrogate key generation, stage variables parameterised workflows, row generators, and pivot stages.
Mitigating the gaps
To overcome the challenges of LLM-based ETL migration, organisations need to adopt some strategies.
These include:
- Recursive chunking- Breaking large XML files into manageable chunks to address token size limitations.
- Prompt engineering- Optimising prompts to improve the accuracy and relevance of LLM-generated code.
- Custom code enhancements- Supplementing LLM outputs with Python scripts to handle complex transformations and exceptions.
- Manual intervention- For highly complex scenarios, human expertise remains essential to ensure accuracy and functionality.
- Agentic validations- Automated validation using additional checks through agentic flow.
It is important to note that future releases of LLMs may address some of these issues. At present, however, human-in-the-loop interventions or a custom code base capable of applying the necessary modifications to the converted code can serve as practical solutions, based on practitioner experience, to the challenges that LLMs pose for ETL conversions.
The way forward
The migration of ETL jobs from legacy platforms to modern cloud-native solutions is a complex but necessary step in data modernisation.
While LLMs offer significant potential to automate and bring efficiency in this process, their limitations highlight the need for a hybrid approach.
By combining AI-driven automation with domain expertise and custom toolsets, organisations can overcome the challenges of ETL migration and unlock the full potential of modern data platforms. This approach ensures that migrated ETL processes maintain their original functionality while leveraging the scalability and flexibility of PySpark.
As LLMs continue to evolve, their capabilities will only improve, reducing the need for manual intervention and enabling even more efficient migrations. The future of ETL modernisation lies in embracing AI-driven solutions while maintaining a strategic focus on accuracy, scalability, and innovation.