Have you ever felt like data engineering is evolving at the speed of light? With new tech emerging almost daily, it’s no surprise that staying ahead of the curve is harder than ever. As we step into the fantastic year 2025 ahead, the rate at which data engineering changes is at an all-time high. New shiny technology, modern articulate ideas, or the latest buzzword emerge out of nowhere almost daily.
Today, I will discuss the crucial data engineering predictions that might shape the world of data engineering through 2025. This profession has been at the forefront of dynamism, but the trends expected to appear soon will call for a redefinition of how we work with, process, and harness data. From open table formats to the accelerating role of DataOps and MLOps, here are my predictions for the data engineering landscape in 2025.
1. Open Table Formats
Remember those data silos? Trying to share data between teams or different tools used to be incredibly challenging – constantly converting formats dealing with compatibility issues… it was a real headache. (Anyone else been there?) Thankfully, things have been changing, and one of the biggest shifts has been the emergence of open table formats. While we saw their potential in 2024, 2025 is shaping up to be the year they truly take off.
Think of open table formats as a universal language for data. They let different systems communicate seamlessly. Formats like Apache Iceberg, Amazon S3 Tables, Delta Lake, and Hudi aren’t just buzzwords we heard last year; they’re becoming the foundation of modern data infrastructure. We’re moving from early adoption to widespread implementation.
- Prediction: Organizations will heavily invest in table formats to future-proof their data pipelines, allowing for real-time processing and eliminating vendor lock-in.
- Impact: Companies will build truly unified data ecosystems, making implementing advanced analytics and machine learning workflows easier.
- What to Watch: Enhanced features like in-line schema evolution and tighter integration with cloud services.
These open formats effectively democratize data, ensuring businesses can move faster and innovate without being constrained by legacy systems.
2. Domain-Specific LLMs and the Surge of Small Language Models (SLMs)
The era of general-purpose large language models (LLMs) has paved the way for domain-specific and small language models (SLMs). These are tailor-made solutions for niche problems designed to deliver efficiency without sacrificing performance.
Domain-specific LLMs: These are tailor-made to perform exceptionally well in specific industries or fields. For instance, a healthcare-focused LLM could analyze medical records and provide actionable insights without needing general-purpose capabilities or human intervention.
SLMs: These are lightweight, efficient models designed for tasks that don’t require the horsepower of a massive LLM.
- Prediction: Domain-specific LLMs will be the future of healthcare, finance, and logistics, providing special capabilities that a general model cannot.
- Impact: SLMs will be widely adopted for lightweight applications, reducing costs and energy consumption while maintaining high accuracy.
- What to Watch: Open-source frameworks allow companies to build custom LLMs faster and cheaper.
Why is this important? Well, not every problem needs a sledgehammer to be solved. Organizations can save resources and achieve better results by using the right tools for the job. These models are smarter and have a more targeted approach to AI, reflecting the industry’s shift from “bigger is better” to “right-sized for the job.”
3. Data Contracts
In the past, building data pipelines often meant making guesses about how the data would look, which led to broken systems and problems down the line. Data contracts offer a better way. They’re like agreements between the people who create and use data, clearly defining how that data should be organized and shared.
This trend is all about better data management and teamwork, making sure data is reliable and easy to use. In 2025, data contracts will be a key part of good data engineering, preventing future problems and making data work much smoother.
- Prediction: By 2025, most organizations will standardize data contracts as part of their development workflows.
- Impact: This will lead to fewer downstream issues, faster delivery of data products, and improved collaboration across teams.
- What to Watch: New tools that automate and enforce data contracts during pipeline execution.
Data contracts ensure that everyone—engineers, analysts, and business users, can rely on consistent, accurate data.
4. DataOps and MLOps
In 2025, DataOps and MLOps will be at the forefront of engineering best practices, transforming how data and machine learning workflows are managed.
- DataOps Prediction: It will evolve as a holistic framework for automating data pipelines, ensuring data quality, and streamlining collaboration between data teams.
- Impact: Businesses will reduce bottlenecks and deliver reliable, high-quality data products faster.
- MLOps Prediction: MLOps will mature to include features like model lifecycle management, CI/CD for machine learning, and governance for deployed models.
- Impact: Teams will confidently deploy AI solutions, ensuring scalability and accountability.
- What to Watch: Unified platforms integrating DataOps and MLOps, offering seamless management from raw data to actionable models.
The synergy between DataOps and MLOps is set to redefine operational efficiency and scalability in data-driven organizations.
5. Data Mesh
Data Mesh is no longer a buzzword—it’s a transformative concept reshaping how organizations think about data ownership and management.
- Prediction: By 2025, data mesh will gain widespread adoption as businesses embrace decentralized, domain-oriented approaches to data architecture.
- Impact: Teams will become data product owners, leading to greater accountability, better collaboration, and more scalable solutions.
- What to Watch: Toolkits that simplify the implementation of data mesh principles across diverse organizational structures.
Data mesh shifts the focus from centralized data lakes to domain-driven design, ensuring that data is closer to its consumers and better aligned with business needs.
Conclusion
The data engineering industry is entering a transformative era in 2025. These trends push the boundaries of what’s possible, combining innovation and responsibility. As we adopt technologies such as DataOps, Data Mesh, and SLMs, the challenge will be ensuring a balance between ambition and ethics, efficiency and scale.
What do you think about these predictions? Which trend excites you the most? Sign up to stay ahead of the latest trends, and let’s shape the future of data engineering together!
Sid Lasley, Vice President of Global Sales Engineering at Hevo Data, brings over 25 years of expertise in sales, customer success, and sales engineering leadership to the table. A visionary leader, Sid has a proven track record of building high-performing teams and driving revenue growth on a global scale.
