How Will Large Language Models And Gen AI Impact Data Engineering?

Ajith Sankaran, Senior Vice President, Course5 Intelligence.

Over the years, the field of data engineering has seen significant changes and paradigm shifts driven by the phenomenal growth of data and by major technological advances such as cloud computing, data lakes, distributed computing, containerization, serverless computing, machine learning, graph database, etc.

Large language models (LLMs) and Generative AI (Gen AI) technologies would be the next major disruptor or driver that will have a huge impact on the field of data engineering. LLMs has the potential to revolutionize the field of data engineering and can drive significant efficiencies and performance improvements. Some of the areas where this would manifest are:

1. Data Collation And Data Cleaning

Data across all formats continues to grow, and there is the complex task of collating, cleaning and labelling the data before it can be used for driving analytics. These are time-consuming tasks, and this is where LLMs and Gen AI can have a major impact.

LLMs and Gen AI can assist data engineers in identifying anomalies, inconsistencies and errors within the data, saving hours of manual inspection. LLMs and Gen AI can help with establishing data lineage and helping data engineers with migration challenges. LLMs can also leverage the extensive knowledge bases to automate data labelling, adding significant efficiencies right at the start of a data engineering program. There are already proven use cases being discussed where LLMs and Gen AI have been able to help with data cleaning and driving efficiencies and improvements in data quality.

While it is yet to get much attention, LLMs and Gen AI can really help in data collection, especially when it comes to unstructured data in the form of free text, audio and video files.

2. Data Integration

Integrating the complex, ever growing and diverse data sources and enhancing them for analysis is another daunting task for data engineers. LLMs and Gen AI can be leveraged by data engineers to synthesize and integrate data assets more effectively and with agility. Further, LLMs and Gen AI can augment and enhance data by identifying and filling in missing values and even suggesting new data sources for enrichment.

3. ETL (Extract, Transform, Load)

At the core of data engineering is the complex and time-consuming process of ETL–extracting, transforming and loading data. With ever-increasing size and complexity of data sets, combined with the expectation of speed and agility, there are significant challenges for data engineers while managing the ETL jobs. This is where LLMs and Gen AI can come in to drive automation and process efficiencies. With their inherent ability to understand the context, LLMs and Gen AI can reduce the manual effort required to generate ETL pipelines and implement workflows. LLMs and Gen AI can even identify different bottlenecks and suggest ML-driven process improvements to optimize ETL processes.

4. Creating Training Data Sets

One of the key challenges for AI and analytics programs, which manifests during the data engineering stage, is the availability of training data for developing the AI/analytics models. LLMs and Gen AI can efficiently and quickly generate synthetic data to address the challenge of limited training data. This is a critical area when historical data is not available and/or it is not accessible.

5. Model Tuning And Optimization

While model building is the mandate for the data scientists, there is an important role that data engineers play in helping with model tuning and optimization, leveraging the data pipelines built during the data engineering stage. LLMs and Gen AI can play a big role in fine-tuning the performance of AI/machine learning models and drive the optimization of model hyperparameters, without time and effort consuming manual processes. This can lead to better AI models and faster turnaround times.

6. Data Governance

LLMs and Gen AI can help with driving data governance, a critical aspect of data engineering. Apart from the already discussed aspects of data cleaning and data quality management, LLMs and Gen AI can help with automation of policies, guidelines and documentation; automation of policy enforcement and compliance, managing data access and data privacy aspects, training development and data governance documentation.

Tips For Leveraging LLMs And Gen AI For Data Engineering

• Make LLMs and Gen AI a part of the road map for all the data analytics and AI initiatives. Even if the initial role is limited, the positive impact from LLMs and Gen AI will be significant across analytics and AI projects.

• Identify smaller wins to showcase the benefits of LLMs and Gen AI for data engineering. These could be in data labelling and data cleaning, rather than model refinement in the initial days.

• Leverage LLMs and Gen AI right core to analytics automation initiatives.

• Develop Gen AI and prompt engineering skills for data engineering teams within the organization.

• Drive data-first culture in the organization by leveraging LLMs and Gen AI, which can facilitate communication within the data engineering team and other technical and non-technical stakeholders.

Conclusion

LLMs and Gen AI will play a pivotal role in shaping the data engineering landscape in the coming months and years. Driving huge efficiency gains and enhanced model performance, the integration of LLMs and Gen AI with data engineering is set to pave the way for a more agile, innovative and data-driven future.

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