How Table Virtualization is Reshaping the Future of Data Architecture
Let's discover how open standards and virtualization are unlocking true composability and freedom in the modern data ecosystem.
The Shape of Technological Change
Not all technology announces itself with fireworks.
Some innovations erupt like volcanic events: sudden, spectacular, and impossible to ignore.
They burst into the public consciousness almost overnight, forcing boardrooms to convene emergency strategy sessions, regulators to scramble, and engineers to pivot mid-project.
Generative AI is the latest of these eruptions. One moment, language models were niche research toys. The next, they were front-page news, embedded into consumer apps, and generating both hype and existential dread in equal measure.
In less than a year, “prompt engineering” went from a term that didn’t exist to a skill on résumés. That’s the volcanic kind of change: visible, fast, and hard to look away from.
But most change in technology is not volcanic. It’s tectonic.
Slow. Patient. Almost geological.
The forces are always there, quietly reshaping the landscape beneath our feet, but rarely drawing attention. For years, you might not notice anything at all; until one day, you wake up and the map is different.
Object storage is a perfect example of this tectonic shift.
When Amazon S3 launched in 2006, it wasn’t marketed as the future of data architecture. It was positioned (and priced) as a cheap, reliable place to stash things you didn’t want to lose: backups, images, static website files.
It had no flashy pitch deck about “data mesh” or “analytics decoupling.” It was just storage, in the cloud, with a simple API.
Yet buried in that simplicity was a profound break from tradition:
It was effectively infinite — you didn’t buy disks, you just uploaded.
It was accessible over the network — storage no longer lived on a server you could physically point to.
It was priced for durability, not performance — the guarantee wasn’t speed, it was that your data wouldn’t disappear.
In the early years, these qualities seemed unremarkable, even boring. But they started to erode one of the oldest assumptions in computing: that storage and compute must live together, bound tightly in the same system.
Once data could live independently, the rest followed almost inevitably:
Data lakes emerged as a pattern.
Analytical engines became stateless, ephemeral, and elastic.
ETL pipelines started to treat storage as the central hub, not the sidecar.
Over time, the gravitational center of data architecture shifted from the database to the storage layer.
And with that shift came new questions:
If storage is independent and shared, how do we ensure consistent tables?
How do multiple compute engines read and write without stepping on each other?
How do we manage schema evolution in an unstructured lake?
Those questions led directly to open table formats, Apache Iceberg, Delta Lake, Apache Hudi, and to something even more subtle and transformative: table virtualization.
Where object storage made data location abstract, table virtualization makes data structure abstract.
It’s the next tectonic movement: and just like S3 in 2006, it’s starting quietly, without a volcanic eruption. But the plates are shifting again.
The Rise of Disaggregated Architectures
Before the age of cloud object storage, the world of data systems felt like living in a walled city.
The walls were tall, strong, and (for a while) a little bit comforting. Inside them, storage and compute were a single, inseparable thing.
Oracle, Teradata, Vertica: these systems were fortresses. The data lived inside the castle, guarded by the same machinery that processed it.
The database wasn’t just a random place where your queries ran; it was where your data lived.
This tight coupling had real upsides.
You had strong transactional guarantees; no mystery about whether a query would see stale data.
Performance was predictable because the system owned the entire stack from disk to SQL parser. And when things broke, you didn’t have to guess whose fault it was: one vendor, one support line, one throat to choke.
But there was a darker side to that comfort. Your data wasn’t yours in a practical sense. If you wanted to use another system, let’s say, a specialized analytics tool or a different database engine, you had to move it.
That meant extract-transform-load (ETL) jobs, complex migration scripts, and usually a lot of waiting around. The walled city kept things safe, but it also kept you trapped.
The First Crack in the Walls
Then along came a strange, almost humble-looking service: Amazon Simple Storage Service: we're talking about S3.
S3 wasn’t the first networked storage. Network-attached storage (NAS) and storage area networks (SANs) had existed for years in the enterprise world.
But S3 did something revolutionary: it stripped the concept of storage down to its most elemental form and made it accessible over the internet with an API so simple it could fit on a napkin.
It had four killer traits:
Practically infinite scalability.
You didn’t buy disks anymore. You didn’t even think about disks. You just put data in, and S3 swallowed it whole. You could store megabytes or petabytes; the interface didn’t change.Pay-as-you-go pricing.
No massive capital expense for racks of storage. No guesswork about future capacity. You paid for what you stored and what you moved. It turned storage from a painful up-front investment into a running utility bill.Eleven nines of durability.
99.999999999% durability was almost absurd to see written down. It meant that if you stored 10 million objects, you might expect to lose one every 10,000 years. For all practical purposes, your data was safer in S3 than in your own data center.A universal, language-agnostic API.
You didn’t need to be running Oracle, Teradata, or anything else to use it. If your program could make HTTP requests, it could store and retrieve data from S3.
At First, a Niche Curiosity
In the early days, S3 was used mostly by developers for static website hosting, media assets, and backups.
It wasn’t immediately obvious to the database world that this was the future. After all, databases had their own highly optimized storage formats and didn’t want to give up control.
But the economics and the extreme flexibility were irresistible. Slowly, cracks appeared in the old monoliths.
A new generation of cloud-native analytics engines emerged; Amazon Athena, Presto (born at Facebook), Google BigQuery: all built around the idea that the data doesn’t have to live in the same system that queries it.
Databricks went even further: betting everything on the premise that a Spark-based compute engine reading from cheap, reliable cloud object storage could outperform traditional warehouses on both cost and flexibility.
Snowflake, famously, doubled down on separating compute clusters from shared storage, making it trivial to spin up multiple independent workloads on the same underlying data.
The First Domino Falls
This architectural shift, separating storage from compute, was the first domino in a chain reaction that reshaped the entire data ecosystem.
It wasn’t just about cheaper storage. It was about freedom. You could store your data once, in a neutral, open-access layer, and let multiple tools work with it. You could use a warehouse for BI, Spark for machine learning, and custom code for niche transformations: all reading the same files.
And once data was free to live in object storage, the next question was inevitable: how should we actually store it there?
Because here’s the thing, dumping CSVs into S3 might be cheap and easy, but it’s not efficient for serious analytics. You needed columnar formats, schema evolution, and transactional guarantees on top of that cheap storage.
That’s when the next wave hit: the standardization of table formats for object storage: Parquet, ORC, Iceberg, Delta Lake, Hudi.
These formats would become the new “data warehouse file systems” in the cloud-native world.
Open Table Formats: The Quiet Enabler
We often hear about “formats” like Parquet or ORC and how they revolutionized big data by making columnar storage efficient and compressible.
But file formats alone don’t tell the whole story.
Parquet solved a critical piece of the puzzle, how to efficiently store and read columnar data, but it left unanswered the bigger question of how to organize, manage, and evolve that data over time.
Enter the open table formats (OTFs): Apache Iceberg, Delta Lake, and Apache Hudi. Each born out of real, painful challenges in handling data at scale, they form the quiet, invisible foundation beneath modern data lakes.
Apache Iceberg, conceived inside Netflix, tackled the “small files” problem.
At petabyte scale, data lakes become littered with millions of tiny files, killing query performance and frustrating users.
Iceberg brought a fresh approach to managing tables in object storage with atomic commits, snapshot isolation, and hidden partitioning; all designed to make lakes behave more like databases.
Delta Lake, championed by Databricks, built on the promise of adding ACID transactions and versioning to cloud data lakes.
Delta Lake turned data lakes into reliable, consistent stores, solving problems like partial writes and concurrent updates that had long plagued lakes.
Apache Hudi focuses on streaming ingestion and incremental processing, allowing lakes to ingest data continuously with minimal latency, while supporting rollback and time travel.
What unites these formats isn’t just features, it’s that they are protocols: agreed-upon contracts specifying how data files and metadata coexist, evolve, and are accessed.
They define:
Where the data files live — the physical Parquet or ORC files stored on object storage.
How to store and manage metadata snapshots — the manifests and logs that track what files belong to which table state.
How to track schema changes and partition evolution — letting tables grow and adapt without breaking queries.
How to support atomic transactions, rollbacks, and snapshots — ensuring consistent reads even amidst concurrent writes.
Thanks to these protocols, object storage tables suddenly gained the structural integrity and transactional guarantees of databases, without locking users into a single compute engine.
This alone is a remarkable achievement.
But beneath the surface of these metadata layers lies a transformative idea that’s only starting to gain attention: table virtualization.
From Shared Tables to Table Virtualization
When people discuss open table formats, the conversation often centers on “shared tables”; the idea that multiple engines can access the same physical data without the inefficiency of copying or moving it around.
That is powerful, but it only scratches the surface.
To unlock the full potential of open table formats, we need to think in terms of virtualization.
Virtualization, in computing, is the art of creating a logical abstraction that hides the messy complexity of the physical world underneath.
Consider:
Storage virtualization lets a cluster of physical disks appear as a single flexible volume.
Network virtualization creates overlay networks that mask the actual topology of physical switches.
Compute virtualization, via hypervisors, makes one physical server seem like many isolated virtual machines.
Virtualization is freedom, because it decouples how resources are used from where and what they physically are.
Open table formats do this for tables. They virtualize tables by separating:
The physical data (the Parquet files on object storage),
From the metadata layer that organizes, catalogs, and presents that data as a coherent logical entity.
The result is that:
A single physical table can appear in multiple catalogs, in multiple platforms, simultaneously.
The ownership of data can be distinct from its presentation and access, letting different platforms expose the table differently without duplicating data.
Multiple compute engines can query the same table natively, without ingesting it into their own storage or making copies.
This form of table virtualization goes beyond the old “data virtualization” concepts championed by engines like Presto or Trino, which worked at the query federation layer and required users to commit to a single engine.
Instead, table virtualization lives at the storage and metadata layer, persisting independently of any single compute engine, and making data truly interoperable.
The Invisible Force Shaping Our Data Ecosystem
Imagine for a second you’re standing near a massive planet in space: the pull it exerts is immense, unavoidable. Everything nearby is drawn to it, stuck in its orbit.
That’s data gravity. It’s an invisible force, but one that governs the behavior of data in any system, especially large-scale data platforms.
Unlike streams or ephemeral events that flow and dissipate, tables are fundamentally different beasts.
They’re not just transient snapshots: they are carefully curated, long-term stores of information, painstakingly built by teams or platforms to serve specific purposes.
Tables accumulate history, structure, and context. Over time, as new rows get added, new columns introduced, and joins created, these datasets become increasingly rich, complex, and valuable.
This accumulation isn’t just about volume: it’s about value concentration.
The more data you gather, the more other datasets you want to combine it with, creating a network effect that pulls even more data in.
This is the essence of data gravity: a growing mass of data that attracts other data, tools, and users, creating a powerful ecosystem centered around that dataset.
Now, this force of gravity works very differently depending on your vantage point.
Gravity as a Fortress
For vendors building data platforms, data gravity is a fortress, a moat: a powerful strategic asset.
The more data that lives inside your platform, the harder it is for customers to leave. Your platform becomes the central hub of their data ecosystem.
Moving or sharing that data out becomes expensive, risky, and complicated, creating natural friction that reduces churn.
Deep integration points (connectors, UIs, governance tools) become tied to the data, increasing customer stickiness.
This is why so many vendors build their platforms to maximize data gravity; it’s a way of growing defensibility in a crowded market.
Gravity as Chains
But what about the people who own data, who need to make business decisions or build products?
From a customer’s perspective, data gravity is often a constraint, sometimes a straightjacket.
They want to leverage the best tools available: the top ingestion pipeline here, the most scalable warehouse there, the best BI tool for dashboards, the most powerful machine learning platform for predictions, and so on.
They dream of a best-of-breed architecture where they mix and match the best vendor for each task.
But gravity means they’re stuck with data trapped in one platform or another, making it painfully hard to break free or collaborate across tools.
The costs pile up:
Duplication: Copying large datasets between systems wastes storage and bandwidth.
Latency: Moving data introduces delays that hamper real-time insights.
Operational Overhead: Managing 10+ vendor contracts, APIs, and data pipelines becomes a full-time job.
This tension, the one between the vendor’s desire for lock-in and the customer’s desire for freedom, has been the Achilles’ heel of the Modern Data Stack (MDS).
The Modern Data Stack: Promise vs Reality
When the Modern Data Stack first emerged, it was heralded as a new dawn of composability.
The idea was actually quite compelling:
Pick the best tools for each part of the data lifecycle.
Connect them loosely through APIs and integrations.
Gain agility, flexibility, and innovation.
Yet, in practice, many organizations found the opposite. Instead of greater simplification, they encountered:
Fragmentation: Ten or more vendors with overlapping or incompatible capabilities.
Complexity: Ten different integrations to maintain, debug, and upgrade.
Cost: Ten support contracts, ten billing cycles, and the risk of vendor fatigue.
What was supposed to be a nimble, composable stack became a brittle, sprawling mess that limited agility rather than enhancing it.
The core problem? Data gravity: data remained trapped, duplicated, or siloed, making true interoperability elusive.
The Middle Ground Between Lock-In and Chaos
It’s now the time for table virtualization, a concept that offers a pragmatic middle path, reconciling the tensions of data gravity with the real needs of customers.
Instead of forcing customers to uproot data from their gravity wells, the trusted platforms where data lives, is curated, and governed, table virtualization proposes to:
Keep the gravity where it is: the data stays safe and authoritative inside the platform that owns it.
Open standardized “windows” into that data, exposing it to other platforms without copying or migrating.
Think of it like a house with thick walls (data gravity), but with well-designed, standardized windows (table virtualization) through which neighbors can see, collaborate, and share without breaking down walls or building new fences.
How Table Virtualization Changes the Game
Thanks to open table formats (Iceberg, Delta Lake, Hudi) and shared object storage, we can now:
Present the same physical table simultaneously in multiple platforms,
Allow each platform to query, enrich, and visualize the data as if it were native,
Avoid the performance and governance risks of data duplication.
This is not just technical elegance: it’s operational simplicity.
Customers can build composable data estates where multiple best-of-breed platforms coexist, interoperating fluidly via shared virtual tables instead of brittle ETL pipelines.
A More Human Story
Beyond the technology and business cases, think about the humans who work with data every day:
Data engineers no longer spend countless hours fighting to synchronize copies of the same table across systems.
Data analysts gain consistent, real-time access to trusted data regardless of platform.
Architects can design future-proof systems that balance agility with control.
Table virtualization gives these people the tools and freedom to innovate, without the headache and risk of brittle, duplicated data silos.
What’s Next?
But, as you may have imagined, table virtualization is not a silver bullet.
It raises new questions and challenges:
How do we manage permissions and governance consistently across platforms?
How do we prevent metadata drift when multiple catalogs show the same table?
What are the limits to multi-platform writes? Can that ever be safe and practical?
Yet, in the face of these challenges, table virtualization offers a realistic path forward: a way to embrace data gravity without becoming captive to it.
It invites us to build data architectures that are modular, flexible, and aligned with the real-world politics of data ownership and trust.
Closing Reflection
The story of data architecture isn’t about flashy breakthroughs alone; it’s about the slow, relentless shifts beneath the surface.
From the quiet arrival of object storage to the rise of open table formats and now table virtualization, we’ve seen a tectonic realignment in how data lives, moves, and gets used.
Data gravity remains a force to reckon with: both a fortress vendors build and chains customers want to break free from.
Table virtualization doesn’t try to defy gravity; it works with it, creating transparent windows instead of walls, enabling data to stay where it belongs while being truly accessible and interoperable.
This middle ground is not just a technical innovation; it’s an invitation to rethink how we build, share, and govern data.
It promises agility without chaos, freedom without fragmentation, and collaboration without compromise.
As the ecosystem evolves, the platforms that embrace openness, respect ownership, and foster interoperability will lead the way.
The quiet revolution of table virtualization is not just reshaping technology: it’s reshaping our relationship with data itself.
And that’s a future worth watching closely.