4 Data Engineering Trends in 2024

Whether your business is on track to embracing AI or you’re immersing more and more into the tools and technology, understanding the importance of data engineering is foundational.

The landscape is constantly evolving. Which brings with it new scale and methodologies that keep reshaping how we approach and handle the influx of data. 

Looking at the most recent insights, the volume of data out there is, well, insane to say the least. 

  • In 2020 alone, 40 zettabytes of data were created. This equates to roughly 1.7mb of data generated per second for every internet user in the world.  
  • This exponential growth is projected to continue, with the volume of data generated, consumed, copied, and stored expected to reach more than 180 zettabytes by 2025.

Let’s dive into four of the most significant data engineering trends in 2024. That way, you can look at solutions to not only stay ahead of the curve, but also take advantage of these changes.

1. LLMs: Automating the Data Stack

We’ve known this for years: Traditional data analysis methods are becoming obsolete.

With the influx of data, tasks like manual analysis and manipulation are a nightmare to work with. Nevermind taking you down a path of inefficiencies and missed opportunities.

But with the versatility of Large Language Models (LLMs), more opportunists are coming out of the woodwork to help revolutionise data engineering by automating analysis and activation processes. 

And this is where concepts like Retrieval Augmented Generation (RAG) come into play. 

Kortical reveals that LLMs are helping with enhancing data operations, particularly through the application of RAG. 

RAG essentially helps integrate that influx of real-time data with LLMs, helping ensure that AI outputs are not only accurate but also contextually relevant – something crucial for applications like chatbots, or sports analytics where up-to-date information is essential.

The integration of LLMs with RAG allows for the creation of more advanced AI agents capable of executing complex tasks, leading to greater automation and efficiency in various business processes. 

This shift not only streamlines workflows, but also democratises data accessibility, making advanced insights available across every level of your organisation.

The takeaway: Explore (and possibly embrace) LLMs to automate your data stack. See it as not just an upgrade, but rather a means to making complex insights accessible at all levels. In real-time.

2. Data Teams as Product Managers

Data is often underutilised as a strategic asset.

Without proper management, data assets can become siloed and outdated, hindering their potential to drive business growth. With it, the role of data teams is evolving, bringing a growing emphasis on their functioning as product managers.

This shift is driven by the need for data teams to ensure that LLM development is production-grade, a task that requires a deep understanding of the underlying tech stack, data quality, and data observability.

Lior Gavish explores this in more detail in his must-read article, where he unpacks the value of RAG architecture for Data Engineering and enterprise AI as a whole. 

“In many ways, LLMs are going to make data engineers more valuable – and that’s exciting!… RAG gives data engineers the best seat at the table when it comes to owning and driving ROI for generative AI investments.”

The convergence of data and analytics has made Industrial DataOps an operational necessity, with data teams playing a pivotal role in delivering business-ready, trusted, actionable, high-quality data to all data consumers, Cognite reveals.

This involves improving the time to value, quality, predictability, and scale of the operational data analytics life cycle.

By adopting a product-centric approach, data teams are transforming how we effectively use data. Treating data as a product – with clear requirements, documentation, sprints, and SLAs – ensures it remains relevant, valuable, and aligned with user needs. 

It’s a mindset that centers itself on elevating your data assets into tools that power innovation and decision-making.

3. The Convergence of Engineering and Data Science

The divide between software engineering and data science is becoming a bottleneck to AI. It’s a separation that slows innovation and complicates the development of AI-driven projects.

That’s where Industrial DataOps comes in. The convergence of engineering and data science is evident in the adoption of Industrial DataOps, which is infused with AI to improve data management and accessibility. 

This approach maximises the productive time of data workers by automating various aspects of data management, including metadata management, unstructured data management, and data integration.

Industrial DataOps platforms help data workers deploy automated workflows to extract, ingest, and integrate data from industrial data sources, offering a workbench for data quality, transformation, and enrichment. This data is then made available through specific application services for humans, machines, and systems to leverage.

This trend not only accelerates the development of AI models, but also fosters a more collaborative and efficient environment. 

4. More RAG: Ensuring AI Product Excellence

AI reliability is a major concern for enterprises.

Without clean, reliable, and contextual data, AI projects risk delivering subpar results or failing altogether.

RAG is critical to ensuring the excellence of AI products, particularly in the context of enterprise-grade AI. It involves fine-tuning LLMs to ensure that AI applications are secure, private, scalable, and trusted. 

The secret to unlocking these pillars? Data Engineers. The gold lies in the data pipelines, where RAG plays a crucial role in fine-tuning and ensuring data quality and observability.

RAG enhances the capabilities of LLMs by incorporating real-time data, ensuring that the AI’s output is not only factual but also aligned with the latest developments, which is particularly important in applications requiring up-to-date information.

RAG techniques – focusing on Reliable, Augmented, and Curated data – are becoming essential for enhancing AI product reliability. Prioritising these principles ensures your AI solutions are built on a solid foundation, significantly increasing their effectiveness and value.

By understanding and adopting these developments, organisations can not only navigate the complexities of today’s data challenges but also unlock new opportunities for growth and innovation. 

Stay ahead of the curve by integrating these practices into your data strategy, ensuring your business remains competitive and data-driven in the ever-evolving digital age.

Looking at Data Engineering services? Thinking about how ready your business is for AI?

Reach out and we’ll find a way to make your AI work.

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