Proof, Not Promises: How the VAST DataBase Outperforms Iceberg and Parquet

The benchmarks behind the VAST DataBase Engine series: the head-to-head results against Iceberg and Parquet, the architecture behind them, and how to match real customer pain to the right VAST capability.

Earlier posts in this series set out why modern data architectures break and explained how the VAST DataBase engine and its row, column, and ACID design are built to solve the problem on a single flash-native foundation. This post does the part that earns trust: it puts the claims to the test. In any serious technical evaluation, performance benchmarks are more than numbers. They are the evidence that separates a marketing claim from a real-world result, and the thing a careful buyer asks for before committing. So this post looks at how VAST is benchmarked, what the results actually show against the leading lakehouse format, and how to translate those results into the language of a real customer’s problems.

The comparison throughout is against Iceberg on Parquet, the industry’s leading file and object based lakehouse format. That is a deliberate and demanding choice. Iceberg with Parquet is fast, transactional, and widely adopted, so using it as the baseline keeps the results meaningful for anyone evaluating a modern alternative. Beating a weak comparator proves little; outperforming the strongest open format is what demonstrates a genuine generational leap rather than an incremental gain.

Executive summary

Across the workloads that matter most to enterprises, the VAST DataBase posts large, consistent advantages over Iceberg and Parquet on equivalent hardware: roughly 3x faster ingest on a 30-billion-row dataset, over 5x faster transactional updates, flat millisecond-level query latency even as data and concurrency grow, and a 21x faster schema backfill that turns a 42-hour operation into about 2 hours. These are not isolated wins; they follow directly from the architecture described in Part 2.

The reason is that the VAST DataBase is file-free. It replaces the file and partition indirection of a lakehouse with metadata held in storage-class memory, sorted tables that act as a global index, and true cell-level updates. That eliminates the planning tax, the small-file problem, and the expensive rewrites that cause file-based systems to slow down precisely as they scale. This post walks through the methodology behind the numbers, the five lakehouse bottlenecks the architecture removes, the headline results, and a practical guide to positioning VAST against the pain a customer is actually feeling.

Why benchmarks matter, and how to read them

Customers want objective, scenario-driven proof that a platform can deliver on its promises, especially when they are evaluating a next-generation approach that asks them to rethink a familiar stack. A good benchmark does two things at once: it shows that the new system is faster, and it shows that the comparison was fair. A number without a method behind it is just a louder claim.

That is why credible benchmarking uses both synthetic and real-world tests. Synthetic benchmarks, such as the industry-standard TPC-DS suite, provide apples-to-apples comparisons against competitors using a workload everyone recognizes. Real-world benchmarks, drawn from actual customer scenarios, prove value in production-like conditions and highlight operational efficiency and business impact that a synthetic test can miss. VAST uses both, which is what lets the results stand up to scrutiny from technical buyers and third-party reviewers alike.

The hardware setup is just as important as the workload. VAST benchmarks run on robust, enterprise-grade clusters, both in private cloud and on AWS, with high-core CPUs, large memory, fast networking, and NVMe flash. Critically, both VAST and Iceberg on Parquet are tested on equivalent, carefully matched hardware: the same node counts, the same flash, the same network speed. That parity is the whole point, because it ensures that any performance difference reflects the architecture rather than a hardware advantage or a tuning trick. The measurements span four axes that map directly onto customer pain: ingest speed, transactional updates, query performance and concurrency, and schema evolution.

Why the architecture wins before the first query runs

The benchmark results are not a surprise once you understand the design, because the VAST DataBase removes the specific things that make a lakehouse slow. It is worth being precise about the four architectural advantages, since each one explains a category of result you will see later.

• A file-free foundation. VAST replaces the file and partition indirection of Iceberg and Parquet with a native tree-structured index whose metadata lives in ultra-low-latency storage-class memory. When that metadata is sorted, locating data becomes a fast, logarithmic-time operation, which eliminates the planning tax of scanning manifests that bogs down file-based systems. Combined with an ingest path that avoids commit delays and small-file backlogs, this makes the platform efficient for all data regardless of layout.
• Accelerated queries with sorted tables. By maintaining user-defined orderings, for example by symbol and time, VAST uses a log-structured merge design to combine high-speed ingest with background sorting. The resulting data chunks are cataloged in the memory-resident index, which acts as a global index and lets queries pinpoint the exact data they need in milliseconds. This delivers the scan avoidance of fine-grained partitioning without the operational rigidity or the costly rewrites, so query patterns can evolve as the business changes.
• Efficient updates and schema evolution. The architecture supports true cell-level updates directly in the columnar store. Because Parquet files are immutable, table formats like Iceberg must use copy-on-write or merge-on-read, both of which are inefficient for frequent changes. VAST touches only the affected data blocks, which makes updates and schema backfills dramatically faster.
• Simplified operations. Taken together, these advantages remove the need for manual, file-based partition design, make compaction fleets unnecessary, and collapse multiple storage tiers into one system. The result is a flatter operational footprint, so engineers spend their time on discovery rather than data plumbing.

Because VAST stores all metadata in persistent memory and avoids file indirection, the operations that punish a lakehouse, point queries, transactional updates, and schema evolution, are dramatically faster and more predictable, even at petabyte scale. That is the foundation the benchmark numbers rest on.

A needle in a haystack

One comparison captures the difference better than any other, and it comes back to the 32KB columnar objects introduced earlier in this series. In a lakehouse, data is stored in large Parquet row groups. To read even a tiny slice of data, the engine often has to scan an entire row group, because that is the unit it can address. Finding a few relevant rows means reading far more than you need.

A 32KB columnar object is roughly 4,000 times smaller than a typical Parquet row group. That single fact has outsized consequences. It allows VAST to retrieve just the data a query needs, instead of scanning huge files for a handful of rows, which keeps latency flat even as concurrency and dataset size grow. The needle is found without combing through the whole haystack, and the larger the haystack gets, the more that precision matters. This is why VAST’s query latency stays consistent at scales where file-based systems degrade.

A real-world test: why a lakehouse struggles with trading data

Abstract advantages become concrete under a demanding workload, and few are more demanding than quantitative trading. Modern trading firms generate billions of rows per day from Level 1 and Level 2 order book feeds, each event stamped at microsecond resolution with hundreds of attributes, and enrichments such as derived features and strategy metadata push the schema width into the tens of thousands of columns. A file-based lakehouse format like Iceberg on Parquet runs into five fundamental bottlenecks here, and the VAST architecture removes each one.

• Ingest. Writing individual messages as Parquet files creates a small-file problem, so pipelines must buffer records into large micro-batches before writing. Larger batches improve file sizes but add ingest-to-query latency that makes real-time analysis impossible; smaller batches improve freshness but create small-file sprawl. VAST ingests streams directly into the SCM write buffer, so each event is durable and queryable within microseconds, with no batching or compaction trade-off.
• Reads. In an unpartitioned lakehouse, finding a small amount of data means traversing a catalog, a metadata file, a manifest list, and one or more manifest files just to discover which data files to read. This metadata tax is brutal for selective queries like a single symbol over a 50-millisecond window, or a basket of hundreds of symbols. VAST replaces that tree of files with a single memory-resident index where lookups are direct and logarithmic-time, so selective queries are fast by default.
• Updates and deletes. Order book data is dominated by high-frequency updates and cancellations, but Parquet files are immutable, so Iceberg simulates changes with copy-on-write, which rewrites whole files, or merge-on-read, which piles up delta files the reader must merge on the fly and a background process must constantly compact. VAST applies true cell-level updates that touch only the affected blocks, with no rewrites and no compaction.
• Backfills. Adding a new feature column is instant as a definition, but a null column is not useful; the real work is backfilling it across history. Because Parquet is immutable, that backfill rewrites every affected file, which at petabyte scale can take days or weeks and freezes research velocity. VAST’s cell-level updates make a backfill an efficient operation that touches only affected blocks.

The fifth bottleneck is the sum of the other four: operational drag. Constant tuning, compaction, and partitioning consume the engineering time that should go into research. VAST’s flash-native, cell-level, similarity-reducing architecture does not just run faster, it rewrites the economics and the agility of the whole workload.

Head-to-head: the numbers

With the method and the architecture established, here are the results across the four axes. Each is based on benchmark data on equivalent hardware, and each maps to a customer requirement rather than an abstract score.

Workload	Iceberg / Parquet	VAST DataBase	Advantage
Ingest, 30-billion-row dataset	Baseline, slows as data grows	About 3x faster	~3x
Transactional updates (update-heavy)	Merge-on-read accumulates delete files and slows over time	Stable, low-latency cell-level updates	Over 5x
Point query, 10-billion-row table	Latency grows with data and concurrency	Under 100 milliseconds, flat	Flat vs exponential
Schema backfill, new column on 30-billion rows	42 hours	About 2 hours	21x

A few of these deserve a sentence of explanation. On ingest, VAST loads a 30-billion-row financial dataset roughly three times faster than Iceberg, because it eliminates client-side partitioning and file management and handles data layout efficiently on the back end. On updates, VAST is over five times faster in update-heavy scenarios, because Iceberg’s merge-on-read approach accumulates metadata and delete files that slow updates over time, while VAST’s cell-level updates hold steady regardless of workload size. On queries, point queries on a 10-billion-row table complete in under 100 milliseconds and stay flat as concurrency rises, whereas file-based latency grows exponentially with more clients or larger datasets. And on schema evolution, backfilling a new column on a 30-billion-row table takes 42 hours with Iceberg but about 2 hours with VAST, a 21x improvement, because the change does not require rewriting entire tables.

Taken together, these are not incremental gains in a single dimension. They show VAST representing a generational leap across ingest speed, transactional throughput, query latency, and operational agility at the same time, which is the combination that is hard to achieve and therefore worth proving.

From benchmark to business value

Numbers persuade engineers, but decisions get made on what the numbers mean for the business. Each result translates into a concrete outcome that a non-technical stakeholder can recognize:

• Faster ingest means streaming and IoT data can be loaded and analyzed in near real time, which supports rapid decision-making instead of next-day reporting.
• Low-latency cell-level updates let an operational data store handle frequent, small changes efficiently, with no batch rewrites and no downtime windows.
• Flat, millisecond query latency keeps interactive dashboards and AI workloads responsive even as data and user counts grow, so the system gets more valuable as adoption rises rather than slower.
• Schema agility, with backfills up to 21x faster, lets data science teams deliver new features and signals without waiting days for an ETL job, which directly accelerates the pace of innovation.

Matching the pain to the capability

Technical superiority alone does not win anyone over. What matters is connecting a specific customer pain to the specific VAST capability that resolves it. Most enterprise customers are living with a familiar set of pain points, and each one maps cleanly to something in the VAST architecture covered earlier in this series.

• Siloed systems and fragmentation map to the unified, DASE-based platform that runs transactional, analytical, and AI workloads in one place and replaces multiple systems with one.
• Performance bottlenecks at scale map to the flash-native, file-free design that delivers consistent low latency by removing file and partition overhead.
• High operational overhead maps to a self-managing system with no manual sharding, partitioning, or compaction, so teams scale up or down without re-architecting.
• The inability to support real-time and AI workloads maps to hybrid transactional and analytical processing with a native vector store, so analytics and AI run on live data with no ETL wait.
• Painful schema evolution maps to cell-level updates and instant schema evolution, so new columns and features arrive in hours rather than days.

The same mapping drives real positioning conversations. A global bank making daily risk-model updates across billions of rows is a story about cell-level updates and instant schema evolution without downtime. A SaaS provider trying to unify analytics and transactions is a story about the unified DASE architecture on one simplified platform. A security company with a slow threat-analytics pipeline is a story about real-time ingest, flash-native storage, and native vector search enabling instant, AI-driven detection. And a quant trading team stuck on multi-day backfills is the 21x story made personal.

Answering the hard questions

A credible benchmark invites scrutiny, and the common objections have clear answers worth having ready:

• Is this a fair comparison? Yes. Benchmarks run on equivalent hardware using industry-standard workloads and transparent methodology, with both platforms tuned to best practices, so the results reflect architecture rather than configuration bias.
• What about real-world workloads versus synthetic tests? Both are used. TPC-DS provides standardized comparison and customer scenarios prove value in production-like conditions, so the results are credible and actionable.
• Does it hold at true enterprise scale? Yes. The architecture is designed for petabyte-scale workloads, benchmarks run on multi-node clusters with billions of rows, and performance stays consistent as data and concurrency grow, unlike file-based systems that degrade under scale.
• Does all-flash mean higher cost? No. Similarity-based reduction and efficient flash management deliver archive-level economics with all-flash performance, which lowers total cost of ownership for both hot and cold data.
• Will it fit my existing pipelines? Yes. VAST integrates natively with Trino, Spark, Python, and Kafka, so you keep your current tools and workflows with no rip-and-replace.

What this means for you

To close the series the way it began, here is the proof framed for four readers:

• If you run storage and infrastructure: the same flash that delivers the performance also delivers archive-level economics through similarity-based reduction, so you stop choosing between fast and affordable.
• If you run the database or data platform: the benchmarks confirm that one platform can carry ingest, updates, queries, and schema change without the pipelines and compaction fleets you maintain today.
• If you build analytics or AI: flat millisecond latency and 21x faster backfills mean your dashboards stay responsive and your feature delivery stops waiting on multi-day ETL.
• If you own the business outcome: the results turn into faster decisions, lower total cost of ownership, and the confidence that the platform speeds up rather than slows down as you grow.

Key takeaways

• On equivalent hardware, the VAST DataBase shows about 3x faster ingest, over 5x faster updates, flat sub-100ms queries on 10-billion-row tables, and a 21x faster schema backfill (42 hours to about 2 hours) versus Iceberg and Parquet.
• The advantages are architectural: a file-free foundation with metadata in storage-class memory, sorted tables as a global index, and true cell-level updates remove the small-file problem, the metadata tax, and expensive rewrites.
• A 32KB columnar object is roughly 4,000 times smaller than a Parquet row group, which is why VAST retrieves only what a query needs and keeps latency flat as data and concurrency scale.
• Benchmarks are run with both TPC-DS and real-world workloads on matched hardware, so the results reflect architecture rather than configuration, and they hold at petabyte scale.
• Winning the conversation means mapping pain to capability: siloed systems to the unified DASE platform, bottlenecks to flash-native design, overhead to operational simplicity, real-time AI to HTAP and native vectors, and slow schema change to cell-level updates.

Read the rest of the series

This post is the proof behind the VAST DataBase Engine series. If you arrived here first, the rest of the series builds the full picture:

• Part 1: Why Modern Data Architectures Break, and What the VAST DataBase Fixes
• Part 2: Inside the VAST DataBase Engine, One System for Tables, Files, and Streams
• Part 3: Under the Hood, Row and Column, ACID, and Exabyte Scale
• Part 4: From Lakehouse to AI, Analytics, Catalogs, and RAG on the VAST DataBase