The data stack evolution: From modern to postmodern and beyond

The data engineering space has evolved a lot over the past decade. With cloud technology maturing, the modern data stack is now giving way to more efficient approaches that some call the “postmodern data stack.” 

So, what exactly does this shift mean, and how can you keep your data strategies a step ahead? 

Matthew Scullion, CEO of Matillion, and Joe Reis co-author of Fundamentals of Data Engineering, offer valuable insights on this evolution in Matillion’s Deep Dish Data Webinar, from the rise of the modern data stack to the future possibilities of AI-enhanced data engineering.

Let’s explore them. 

1. The modern data stack: what it brought to the table

In the past, data warehouses were expensive, and handling the infrastructure was cumbersome. Only large enterprises could afford high-quality data infrastructure, leaving smaller businesses at a disadvantage. 

That changed in the early 2010s when cloud technology took off. Tools like Amazon Redshift and Snowflake offered a new approach to data management. 

Organizations could quickly deploy affordable, flexible data warehouses that could be up and running within minutes. Data storage and analytics became accessible to companies of all sizes, enabling smaller businesses to perform complex data analysis.

Scullion explains how this shift empowered companies to build data pipelines and complete data projects faster and for less: “You had whole businesses that wouldn't necessarily have had one of those appliance-based data warehouses that cost seven digits, but now could not only have one but could have one in five minutes.”

Despite the benefits, this new freedom brought its own set of challenges as businesses expanded their cloud data usage. The modern data stack separated the different steps of the ETL (extract, transform, load) process into multiple tools. 

That meant teams had to juggle several platforms just to manage data ingestion, transformation, and organization. This approach, while effective, led to an increasingly complex setup.

2. Reducing complexity with the postmodern data stack

As data operations scaled, managing all the different parts of the modern data stack became overwhelming. With so many tools and processes to handle, teams found themselves spending more time managing workflows than getting insights from data. 

The postmodern data stack addresses this issue. Instead of relying on separate tools for every function, it emphasizes a more unified, integrated approach that simplifies operations.

Scullion describes this shift as similar to the evolution of desktop publishing. When it first became accessible, everyone created their own templates and experimented with designs. Eventually, companies realized it was more efficient to standardize templates across the board. 

The postmodern data stack takes a similar approach. Rather than reinventing the wheel with each new data project, companies can now rely on standard frameworks that save time, simplify maintenance, and ensure consistent data quality.

3. Boosting productivity with AI and automation

In a postmodern data stack, boosting productivity is key, especially with tighter budgets and growing demands on data teams. 

Scullion and Rees believe AI will play a big role here—not by replacing data engineers, but by empowering them. For example, tools like Matillion’s AI Copilot use natural language processing to generate data pipelines automatically. This helps teams automate routine tasks and speed up complex data transformations.

However, AI’s impact goes even further, as these tools not only cut down on manual work but also make it easier for new team members to learn the ropes. 

Scullion explains that by automating documentation and translating complex code, AI ensures pipelines are understandable, even for people new to data engineering. This speeds up onboarding and makes knowledge sharing much simpler across the team.

Another big shift is the move toward integrating unstructured data—like documents and images—with traditional, structured datasets. This combination supports advanced AI models and provides a more complex view of the business environment. 

It goes beyond processing tables and rows to build a connected data ecosystem that delivers deeper insights and supports a range of AI-driven use cases.

Data teams are also implementing a blend of low-code and high-code approaches. In this setup, data engineers tackle complex transformations with code while letting analysts handle simpler tasks through a low-code interface. It’s a flexible approach that speeds up development without compromising control or scalability.

4. Focusing on governance and DataOps

The postmodern data stack doesn’t just focus on speed and scalability—it also incorporates strong governance practices that support reliable, responsible data usage. 

Machine learning and generative AI offer exciting new ways to analyze data and make decisions. That said, it also requires high-quality, well-structured data to work effectively. 

Without solid governance, AI models can quickly go off track with unreliable inputs, which lead to inaccurate results and inefficiencies.

The postmodern data stack is, therefore, as much about responsible data usage as it is about getting things done faster. By implementing strong governance policies and investing in tools that provide transparency and oversight, you can unlock AI’s potential without compromising security or data integrity.

Good governance also means having version control and documentation in place. By keeping pipelines maintained as code, data teams can trace issues quickly and understand past changes, which makes it easier to troubleshoot. Automated testing further strengthens reliability, as it identifies problems before they reach production.

Embrace the postmodern data stack for future-ready data operations

The move from a modern to a postmodern data stack marks a new era in data engineering. This evolution reduces the complexity of handling multiple platforms while increasing productivity through AI capabilities, solid governance practices, and optimized pipeline management. 

As Scullion and Rees point out, AI will be a key enabler here. It will help data teams achieve more with less and create a scalable foundation for the future.

By embracing this unified, postmodern approach, businesses can make their data operations more sustainable and future-ready, meeting today’s challenges with ease and preparing for the innovations of tomorrow.