Generative AI is a subset of AI that’s used to create new data or content, or even new patterns that mimic existing data. The new data generated is based on existing data that the AI models are trained on. Generative AI is transforming various industries around us already, and that includes data engineering as well. This capability of creating new data based on existing datasets is changing how data engineers handle data processing, data management, and data integration. 

In this blog post, we’ll discuss generative AI’s applications, benefits, challenges, and its future from a data engineer’s perspective.

What is Generative AI?

Generative AI, as the name suggests, is simply a type of AI that can generate new content (or images, videos, etc.) based on existing content or existing patterns that the AI has learned through it’s training. There are various models in generative AI including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based architectures. 

Applications of Generative AI

Data Augmentation

Data augmentation is mostly used in cases where we need more data than we already have. Examples of such cases include generative a few extra records to balance a dataset for machine learning model optimization, creating new pixels between existing ones to upscale an image or video to a higher resolution, etc. And it goes without saying that we need to be extra careful when we’re supplementing existing datasets with synthetic data.

Data Cleaning And Preprocessing

Data cleaning is one of the early steps in any data engineering pipeline, and is a crucial step as well. We need to clean data to make sure the format of the data is consistent, all missing values are filled with meaningful values, outliers are identified, and the data is overall standardized. We usually write custom logic for this for each pipeline. By training models to look at the existing data, we can easily automate this using generative AI. And in most cases, this works as expected. 

Schema Generation And Data Integration

When we are ingesting data from various sources, we need to make sure they all fit into a unified schema. This often involves a lot of manual effort to understand the data coming from each source and creating a schema that fits all of these data sources. We can automate this by making an AI model look at these various datasets and coming up with a schema that can fit all of this.

Metadata Generation

The next obvious step after understanding the data and creating a schema is to create some metadata around that data so that It’s easier for anybody to look at it and make sense of it. Generative AI models and do it too.

Anomaly Detection

One an AI model understands the data, it also understands the patterns in that data. So when there are outliers, it’s easy for an AI model to flag them as anomalies so that a human can take a look at the data later. This is particularly useful in applications such as fraud detection, industrial automation to predict failures and events, network security, etc.

Benefits Of Using AI In Data Engineering

Data Quality

Automated data cleaning and augmenting makes sure that any data generated will be high quality because by definition that augmented dataset will be inspired by existing data. Quality and consistency of data can be guaranteed.

Efficiency

Because these generative AI models will be continuously trained on new data, repetitive tasks such as data cleaning, augmenting, schema generation, metadata generation, etc. will be all be very efficient. 

Scalability

Because this is all automated and efficient, organizations can easily apply these models to large scale datasets easily without much manual effort, while making sure of the quality and the efficiency.

Cost Reduction

In general, automating all of this will decrease human effort, thereby reducing cost and improving project delivery times. 

Challenges Of Using Generative AI In Data Engineering

Data Privacy And Security 

Whenever any AI model is involved, there’s always a conversation about data privacy and security. Building a secure network of systems becomes paramount in such applications to prevent unauthorized access to data.

Model Accuracy And Bias

Like with any other AI model, generative AI models are as accurate as the data they are trained on. If there are biases in the training datasets, the generated datasets would have inherited those biases. So evaluating the output of these models and continually refining and tuning them is important to make sure the generated data is accurate and without any biases. 

Integration Complexity

Integrating generative AI into existing data engineering pipelines could be challenging as they need significant amount of system changes. This would not be favorable for all organizations. 

Ethical Considerations 

Generating synthetic data might not always be accepted as ethical, because of the end of the data, no matter how realistic these datasets are, they are still synthetic. So navigating these ethical challenges could be tricky.

Future Prospects Of Generative AI In Data Engineering

Advanced Automation

AI in the future could potentially take care of everything from integrations to data cleaning to transformations to loading. There could be minimal human intervention required for data engineering pipelines.

Real-Time Data Processing

AI models could become efficient enough to engage in real-time data processing. This could enable businesses to make immediate decisions driven by data.

Improved Collaboration

AI models could easily translate complex data processes into understandable insights, making collaboration between data engineers and business stakeholders easy.

Democratization Of Data Engineering

AI models could enable low code or no code infrastructure where non-technical users could potentially create data pipelines with minimal intervention of data engineers.

Conclusion

While generative models of today definitely have their drawbacks and challenges, future models could address these, providing the ability to automate repetitive tasks, improve the quality of data, and providing ways to innovate data solutions. Generative AI will come increasingly important in building scalable, intelligent, and efficient data pipelines in the future.