The Great Unbundling: Is the All-in-One Data Platform Dead?

Back when stacks meant Teradata boxes and Oracle licenses, leaders invested in a single, monolithic platform as a single source of stable, comprehensive truth. This model often resulted in glacial innovation cycles, inflexible architecture and vendor lock-in. The industry then went in the opposite direction with the “great unbundling,” which gave rise to the modern data stack. This best-of-breed approach involved stitching together specialized tools, but it introduced the fatigue of keeping many tools humming and secure and diagnosing breakages.

 This triggered the “great rebundling.” Platforms have broadened into end-to-end ecosystems, leaving data professionals wondering if the all-in-one platform is making a comeback. The real question is not which camp is better. It is how organizations combine both to build a stack that is modular and integrated where it matters most.

The Case for Unbundling — The Rise of the Modern Data Stack

The cloud was the engine behind the unbundling movement. Cloud-native architecture fundamentally decoupled storage from compute, and this single change made the monolithic model obsolete and allowed a new breed of specialized tools to flourish. Teams could scale storage and compute independently, cut idle spending, and choose fit-for-purpose engines without moving any data.

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This approach provided benefits impossible to ignore, allowing teams to build a tailored stack. The main advantages included: 

•     Cost optimization: Pay-as-you-go models and the ability to choose solutions for each part of the stack enabled more granular financial control.
•     Rapid innovation: A company focused on data transformation will innovate faster in that niche than the transformation module of a massive platform.
•     Freedom from vendor lock-in: The ability to swap components as better technology emerges becomes a strategic advantage.
•     Flexibility and control: Data teams can select the best tool for a specific job.

 Unbundling is about building a dream team of components. Standard formats hold data steady while engines evolve and pipelines improve in place. Analysts and data scientists get tools they actually like using, and the platform team preserves the option to refresh a layer when the market moves. The combination of open formats and modular engines makes that possible.

The Case for Rebundling — Hidden Costs of Complexity

Unbundling has a downside, which many call the Frankenstack — it is characterized by many legacy systems stitched together over time. Each new tool adds configuration, permissions, connectors and failure modes. Writers across the data community have chronicled how tool sprawl drives complexity and how the modern stack’s promise of single, modular building blocks often turns into an operational nightmare.

 The integration tax is real. Getting dozens of components to interoperate is not a one-time project. It requires frequent upgrades and compatibility testing. Even in adjacent disciplines like security, independent research highlights the cost of multi-vendor toolchains, from inconsistent visibility to operational drag. This experience maps closely to data teams managing overlapping quality, observability and lineage tools.

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Security and governance also stretch thin across many products. Role-based access, retention and compliance are difficult to apply uniformly when policies are in different places. Academic and industry research on data pipeline quality and data-intensive systems consistently highlights compatibility issues and architectural friction happening as systems scale.

 There is also the “blame game” cycle — every time a data pipeline breaks, troubleshooting is challenging. Is it because of the ingestion tool, the transformation layer or the BI platform? It becomes nearly impossible to pinpoint the source of the problem, leading to unresolved issues.

 Additionally, cognitive overhead becomes a hiring and training issue. New team members must learn many user interfaces, command-line interfaces and domain-specific languages. Leaders have to choose between broad generalists who can keep the stack coherent and specialists who push one layer forward.

The stakes are high outside the data team’s walls, too. A single IoT-driven breach averages more than $330,000 once response, fines, remediation and reputation damage are counted — a reminder that fragmented controls raise business risk, not just operational toil.

The Middle Ground — The Core and Ecosystem Model

The industry is not swinging back to the past — it is rising to a different level of abstraction. Think of a core that grounds the platform and a periphery that innovates on top of it.

The core is the warehouse or lakehouse where data lives and policies are anchored. Open table formats turn that core into shared infrastructure. Then engines can read the same tables and teams can switch processing layers without rewriting storage. A practical core puts baseline controls into place. This includes encryption for data in transit and at rest, resilience through tested backups, masking or sanitation to reduce exposure during analytics and sharing, and erasure processes that remove data when policy or regulation requires it.

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Specialized tools thrive in the periphery. Observability, semantic layers, notebook-native exploration or domain-specific machine learning services can move fast as long as they respect open interfaces and the core’s governance. That is the difference between parallel silos and an ecosystem.

Why Do This Now?

Open standards have matured, and vendors are aligning around them. In 2024, Snowflake announced the Polaris Catalog for Iceberg and emphasized interoperability across cloud providers. The industry framed it as a step toward vendor-neutral catalogs rather than a closed garden. Databricks has similarly embraced interoperability by contributing to Delta Lake as an open standard and expanding its support for other formats.

The middle ground accepts a truth both sides recognize — teams want choice without fragmentation. The practical route is to keep data centralized on open foundations, then plug in best-of-breed tools that speak to those foundations instead of bypassing them.

The Future State — The Rise of the Composable Platform

This leads to the industry’s next logical destination — the composable platform. This is the key prediction for the future of data architecture. It allows a company to start with a strong, integrated core from a major vendor. The core provides the foundation, including the data lakehouse, governance, security and basic tooling. From there, the company composes its ideal stack by adding the tools that integrate seamlessly.

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The composable platform is like building with Legos. If a new, better “brick” comes along, teams can easily swap out old parts and snap the new one in without disturbing the rest of the model.

Data engineers can focus less on building custom integrations and now spend more time creating data products on top of the core. Data scientists get a more unified experience and access data using specialized tools. Data leaders also adopt new tools to solve specific business problems.

Industry experts say that a likely feature of this shift is the data app store model. If platforms can expose stable APIs and catalogs, third parties can distribute extensions discoverable within the core. This is seen in Snowflake’s Native App Framework and Marketplace and Databricks’ Marketplace, which adds first-class applications.

From All-in-One to All-You-Need

The all-in-one data platform, in its old, monolithic form, is dead. Its demise gave way to the unbundled modern data stack, which prioritized flexibility but introduced costly complexity. Now, the market is correcting itself.

The idea of a platform is returning, reshaped by open formats, embeddable engines and catalogs that welcome an ecosystem around them. Mastering the composable approach is the new competitive differentiator. The most successful companies will be those that learn to adopt innovation without rewriting their foundation and maintain control without slowing discovery.

About the author: Ellie Gabel is a freelance writer who also works as an associate editor for Revolutionized.com. She enjoys keeping up with the latest innovations in tech and science and writing about how they’re shaping the world we live in. She resides in Raleigh, NC, with her husband and their cats.

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