The Post-Modern Data Stack: Boosting Productivity and Value

This article is sponsored and originally appeared on Ascend.io.

The “modern data stack” has become increasingly prominent in recent years, promising a streamlined approach to data processing. However, this well-intentioned foundation has begun to crack under its own complexity. Engineering leaders are investing more time and energy into their modern data stacks without seeing a proportional return on investment, leaving them to question whether an array of shiny new tools is better than a carefully curated, efficient set.

In this post, I’ll explore the origins of the modern data stack, discuss why its promised benefits have proven elusive, and advocate for a post-modern approach to data management that prioritizes productivity and value.

Where did the modern data stack come from?

The modern data stack emerged as a response to a glaring gap in the data ecosystem: a dearth of developer tools. Previous eras of data infrastructure, such as Teradata and Informatica, gave way to “big data” platforms like Hadoop and Spark, which initially catered to infrastructure experts rather than a broader audience.

The subsequent introduction of Snowflake, Databricks, and BigQuery transformed the landscape, enabling business analysts, analytics engineers, and software engineers to more readily participate in the data lifecycle. This broader, “upstack-oriented” audience demanded more accessible, consumer-grade data products, which in turn led to the development of the modern data stack as we know it.

The deceptive simplicity of the modern data stack

At first glance, the modern data stack seems like a dream come true. Ingesting data, transforming it, orchestrating a basic pipeline, and achieving observability appear simple and efficient. However, this initial ease quickly gives way to a more complex reality. 

As data sources multiply and pipelines become more intricate, engineering teams find themselves grappling with an unwieldy array of tools. While each one is ostensibly the “best-of-breed” for its specific purpose, this added complexity undermines the very benefits the modern data stack claims to offer.

The “best-of-breed” trap

One of the major selling points of the modern data stack is the notion that engineering teams can choose the absolute best tool for each step of the data processing journey. However, this “best-of-breed” approach has proven to be more of a myth than a reality. 

Despite employing top-notch tools, engineering teams spend an increasingly large percentage of their time consumed by integrations and maintenance, while the time devoted to the creation of new data products​​ asymptotically approaches zero. This best-of-breed hangover has left many teams disillusioned and craving a more streamlined approach.

Embracing the post-modern data stack

Increasingly, data leaders are recognizing that a best-of-breed strategy rarely yields best-of-breed outcomes. This realization has inadvertently bolstered the case for a post-modern approach to data management, one that favors fewer, more unified tools.

By adopting a post-modern data stack, engineering leaders can reduce their reliance on a multitude of vendors (and the exorbitant costs that accompany them) and open-source tools, minimize time-consuming integration work, and accelerate their overall data architecture.

The post-modern data stack is characterized by:

1. Optimized metadata collection and storage: By centralizing metadata, engineering teams can streamline the entire ingest-to-observability process, not only making more informed decisions themselves, but ensuring all systems can be sufficiently automated and optimized based upon a shared metadata backbone.
2. Intelligent pipelines: Reducing dependencies and enabling more efficient data processing, intelligent data pipelines allow teams to focus on what truly matters: creating impactful data products. By utilizing a shared metadata backbone, intelligent pipelines are designed to better respond and adapt to changes in code and data, greatly enhancing performance while simultaneously reducing maintenance burdens.
3. Value-driven data products: By shedding the excess baggage of the modern data stack, teams can devote their resources and expertise to developing data products that drive value and meaningful outcomes for their organizations. By continuing to shift focus to the data products themselves and away from the tooling stack, engineering teams can accelerate time-to-production and deliver results that truly make a difference.

In conclusion, while the modern data stack has certainly paved the way for new possibilities in data processing, it’s time to embrace the post-modern approach. By prioritizing fewer, more unified tools, engineering leaders can increase productivity, reduce costs, and ultimately, unlock the full potential of their data. The post-modern data stack offers a promising path forward for organizations seeking to create valuable data products and drive meaningful outcomes.