The modern data stack, once a symbol of streamlined efficiency, is cracking under its own weight. What seemed like a dream come true for engineering teams has become a complexity trap that requires more and more maintenance as it scales. Technical debt is piling up and teams are struggling to keep pace, suggesting a future where there will be no capacity left for innovation. Maintaining the modern data stack consumes too many resources, and the only way to free up engineers’ time is to evolve the current architecture. The industry needs a post-modern data stack.

To understand what this looks like, it’s helpful to look at how we got here. Data management has come a long way over the past decade, evolving from Teradata and Informatica, to Hadoop, and now to the age of Snowflake, Databricks, and Google BigQuery. Today’s modern, cloud-based architecture has an alluring simplicity, but as the task of managing and maintaining it grows in complexity it undermines the very benefits it has set out to provide. We’ve made progress, but we’re not there yet.

An unwieldy mass of tools

Even with a modern cloud architecture, significant complexity remains. Data engineers have been given a vast array of new tools, but there are so many of these tools that they have become unwieldy. Data sources have multiplied, pipelines have become more intricate, and the resulting infrastructure is not self-aware, requiring significant manual maintenance. The challenges of the modern data stack outweigh the benefits, leaving teams disillusioned and overworked.

All roads lead to automation

When we look at the history of enterprise technologies, it becomes clear that all roads lead to consolidation, simplification, productization, and ultimately automation. Here are two examples that illustrate this:

  • Early databases evolved into relational databases and standardized on the SQL language. Disparate functionality and features were consolidated into unified data management systems that now automate routine maintenance tasks, enhancing performance and reducing operational burden.
  • More recently, provisioning and managing cloud infrastructure has been greatly simplified by automation tools like Terraform and Kubernetes, eliminating manual errors and complexity.

The modern data stack is ripe for a comparable wave of evolution, to reduce the complexity and free up engineers’ time so that they can work on projects that actually move the needle for the business. The post-modern data stack requires a more self-aware, unified architecture that opens the door to intelligent automation.

Characteristics of the post-modern data stack

The post-modern data stack is powerful because it takes a holistic and synergistic approach, with characteristics including:

  • Optimized Metadata Collection and Storage: Centralizing metadata allows engineering teams to streamline the entire ingest-to-observability process, allowing systems to be automated and optimized based on a shared metadata backbone.
  • Intelligent Pipelines: Intelligent pipelines can adapt to changes in code and data, reducing dependencies and allowing for more efficient data processing with minimal human intervention.
  • Value-Driven Data Products: By reducing manual work required by the modern data stack, teams can devote their resources and expertise to building new data products that drive value and meaningful outcomes for the business.
Unleash the power of your data – and your teams

The modern data stack may not have been the panacea we were hoping for but the path forward is clear. By prioritizing fewer, more unified tools and embracing automation powered by centralized metadata, engineering leaders can unlock not just the full potential of their data but the full potential of their data teams. 

The history of technology shows that all roads lead to automation. The post-modern data stack is the vehicle for getting there, offering a path to increased productivity and greater innovation. The future of data management is here, and it’s intelligent, streamlined, and designed for value.