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Published On Jul 17, 2025
Updated On Jul 17, 2025
Web3 isn’t just generating more data; it’s generating fragmented, trustless, and time-sensitive data.
Every transaction, vote, or smart contract interaction is recorded publicly, but scattered across multiple chains, rollups, and runtimes.
The fragmentation of Web3 data challenges the foundational assumptions of traditional analytics stacks.
Unlike Web2, where data is centralised and controllable, Web3 demands real-time access, cross-chain coherence, and cryptographic trust.
This creates a new set of challenges, such as:
In this blog, we break down the three core challenges that are Volume, Velocity, and Veracity and explore what they mean for developers, analysts, and protocol teams in 2025.
Let's get started.
There are three core dimensions to understanding data challenges in Web3: Volume, Velocity, and Veracity.
Originally from the Big Data world, these terms take on new meaning in decentralised systems. Web3 isn’t just scaling data; it’s changing how it’s generated, transmitted, and trusted.
Before we explore how these factors impact system design, let’s break down what each one really means in a Web3 context.
Web3 generates massive volumes of data, not just in size, but in structure.
Every contract call, token transfer, vote, or oracle update adds to a growing on-chain state. And this doesn’t happen on one chain; it happens across Ethereum, L2s like Arbitrum and Base, and dozens of rollups.
For example, the Ethereum mainnet alone emits over 1.4 million logs per day. Add L2s, and you’re quickly dealing with tens of millions of daily events.
Unlike Web2, there’s no central backend to query. Web3 data must be fetched, filtered, and rebuilt from source, chain by chain, block by block.
As activity scales, managing this volume becomes a fundamental infrastructure challenge.
Web3 data isn’t just large, it’s fast.
Every block can trigger liquidations, price updates, governance actions, or cross-chain messages. DeFi protocols, bots, and real-time dashboards rely on data that updates in seconds or even less.
But latency in Web3 isn’t just inconvenient, it’s costly. A delay in processing can lead to failed trades, missed automations, or inaccurate decisions.
Unlike Web2, where systems can buffer and batch, Web3 often requires sub-second ingestion and reaction. Chains with different block times and finality models add further complexity.
Handling velocity means building for real-time execution, not just real-time visibility.
In decentralised systems, trust isn’t assumed; it has to be verified.
Data can be delayed, incomplete, or even manipulated. Blockchain reorgs, unreliable RPCs, or indexing errors can distort what’s actually happening on-chain.
Veracity in Web3 means ensuring that what you see reflects finalised, on-chain truth, across networks, under adversarial conditions.
That requires more than accuracy. It demands cryptographic proofs, multi-source validation, and indexing transparency.
Without it, analytics mislead, automations misfire, and protocol decisions go wrong.
Let’s start with the first V i.e. Volume, and see what it means for Web3 teams in practice.
In Web3, data volume isn’t just about size; it’s about duplication, fragmentation, and context overload.
A single user action can produce dozens of events like token transfers, contract calls, vault updates, or NFT metadata writes.
And when that action touches multiple chains, each with its own runtime and indexer assumptions, the volume problem becomes less about bytes and more about structure.
This scale breaks traditional data assumptions, i.e. in Web2, you query a database. But in Web3, you’re reconstructing state from low-level, append-only logs, which often have no guarantees of structure or consistency.
This creates three core challenges:
To manage this, leading teams are adopting:
Volume isn’t just a cost problem, it’s an architectural one. If you can’t handle the scale, everything else breaks downstream: dashboards lag, bots misfire, and key insights go missing.
In Web3, data doesn’t just move fast; it often needs to trigger action the moment it lands.
Every block can affect collateral ratios, trigger oracle updates, or finalise a DAO proposal.
In systems like DeFi, liquid staking, or cross-chain execution, delays aren't just inconvenient; they’re expensive or even dangerous.
The pressure isn’t just to consume data quickly; it’s to act on it faster than your competition, validator set, or market volatility window.
In these systems, data is not passive. It’s the fuel that drives autonomous, programmable logic; if it lags, the logic breaks.
Traditional systems batch data, buffer queues, and retry later. Web3 systems can’t afford that luxury.
Here, every delay compounds risk:
Complicating this further is the variation in block times and finality across chains.
Ethereum settles every ~12 seconds, Solana every ~400ms, and some rollups finalise with significant delay. When building across them, your data pipeline is only as fast as its slowest source.
To handle high-speed data flows, modern teams are shifting toward:
Velocity in Web3 isn’t about speed in isolation; it’s about timing, trust, and execution sensitivity.
But without trust, speed breaks things.
Which brings us to the third challenge, i.e. Veracity.
In Web2, data integrity relies on trusted sources. In Web3, there are no trusted sources, only verifiable ones.
That’s what makes veracity difficult in decentralised systems.
You’re not just trying to confirm if the data is accurate. You’re trying to ensure it reflects on-chain truth, across networks where finality can be delayed, forks can happen, and off-chain dependencies can fail.
Veracity is further complicated by multi-chain ecosystems, where different chains have different levels of finality, different standards of emitting events, and varying availability of reliable RPCs or archive nodes.
Veracity isn't a layer you can add later; it has to be designed into the system from the start. Without it, analytics become misleading, automations become risky, and users lose confidence in protocol behaviour.
But the next challenge is understanding what happens when they intersect in real-world systems.
Individually, volume, velocity, and veracity each present tough engineering problems. But in real-world systems, these challenges rarely show up in isolation as they collide and are often unpredictable.
The result? Analytics pipelines break under load, dashboards show inconsistent results, automation scripts misfire, and cross-chain coordination becomes brittle.
Let’s look at how these failures play out in practice.
A platform that aggregates NFT data across chains like Ethereum, Base, and Polygon faces all three Vs at once:
Failure to get any of these right means:
A DEX or intent-based trading system operating across Arbitrum, Optimism, and zkSync has to handle:
If any part lags or fails:
A DAO dashboard that tracks proposals and voting across chains faces:
If not handled correctly:
When the 3Vs converge, teams face not just performance bottlenecks but systemic risk. Misaligned data across chains, delayed execution, and unverifiable sources can create:
Handling one V is hard. Handling all three at once is what separates high-resilience systems from everything else.
To meet these demands, teams are turning to a new generation of tools and architectural patterns built specifically for the scale, speed, and trust requirements of Web3 data.
The challenges of Web3 data volume, velocity, and veracity are infrastructure problems that teams face daily.
There’s no one-size-fits-all solution, but a new generation of tools and architectural patterns is emerging and is designed not as upgrades to Web2 analytics, but as blockchain-native primitives.
These systems are built to handle public, fragmented, event-driven data, and they’re changing how leading teams approach analytics at scale.
Let's see how.
Traditional indexers were built for monolithic chains and simple contracts. In today’s multi-chain environment, teams need indexing layers that are customizable, runtime-aware, and scalable.
These frameworks let teams move beyond centralised subgraphs and build indexers that match the scale and specificity of their own protocols.
Real-time systems like DEXs, bots, and liquidation engines don’t just need accurate data; they need it now. That’s driving adoption of stream-first architectures.
These tools support millisecond-level event ingestion, enabling responsive automations, near real-time dashboards, and event-driven workflows across chains.
As more analytics move off-chain, teams must ensure that the data they consume and act on is provably correct, not just assumed to be.
This is a critical shift from trust-based reads to proof-based pipelines, reducing attack surfaces and downstream failures.
Instead of writing SQL queries in dashboards, analysts and engineers increasingly need programmable, API-first access to blockchain data.
These platforms accelerate iteration, remove the need to manage infrastructure, and unlock insights from large-scale on-chain datasets without waiting for engineering support.
Modern data stacks are becoming modular, structured into clear layers that mirror the lifecycle of on-chain data:
This modularity doesn’t just make stacks easier to maintain; it lets teams scale individual layers independently as needs evolve.
Web3’s data landscape is too fragmented, too fast-moving, and too high-stakes for traditional tools.
Teams that are solving are rethinking architecture from the ground up and need to take key strategic decisions to stay ahead.
Here are five principles that matter in 2025 for anyone designing resilient, high-performance Web3 data systems:
In high-frequency environments like DeFi, it's common to prioritise low latency. But blockchains don’t offer instant finality, especially L2s and modular chains with longer settlement windows.
Acting on unfinalized data introduces risk. Reorgs can invalidate trades, liquidations, or governance actions that are already in motion.
What to do: Introduce configurable finality buffers in your pipeline, especially for execution-critical workflows.
Indexers are production-critical infrastructure. If your indexer lags, your app’s data is stale. If it fails, automations or dashboards break.
This is especially true for contracts with complex event structures or dynamic logic.
What to do: Self-host mission-critical indexers. Use public ones only for basic analytics or early-stage dev work.
Blockchain data is scattered. Storing and querying everything is expensive and rarely needed.
Optimising it by storing only high-value events, compressing archival data, and filtering early in the pipeline to cut compute and storage costs.
What to do: Apply event-level filters and TTL (time-to-live) rules for different datasets based on usage patterns.
Most protocols are already multi-chain. That means different runtimes, event formats, finality assumptions, and indexing requirements.
If your architecture assumes a single-chain model, it will break as you expand.
What to do: Use modular ETL pipelines and chain-agnostic schemas so you can add or replace chains without refactoring your entire stack.
As data becomes a dependency for automation, funding, and governance, trust assumptions must be explicit and provable.
Teams are increasingly using cryptographic proofs to validate data reads, detect manipulation, and defend against Sybil or oracle-based exploits.
What to do: Explore ZK-attested data feeds, Eigenlayer AVS-based validations, and multi-source cross-checking to reduce reliance on any single input.
These aren't just engineering tactics. They're resilience strategies.
Getting them right not just reduces risk, but will build systems that scale, adapt, and earn long-term trust in a modular, multi-chain world.
So, what does the future of Web3 data infrastructure look like? Let’s take a closer look.
Web3 is forcing a rethinking of data architecture from the ground up. In traditional systems, data pipelines are built around control, access, and aggregation. In Web3, they’re built around openness, coordination, and proof.
Today, building a good Web3 data pipeline means more than just moving data efficiently; it requires designing for fragmented sources, verifiable trust, and execution-aware timing across chains.
As chains multiply and on-chain logic grows more complex, the old ways of managing data are quickly becoming obsolete.
What’s emerging instead is a modular Web3 data stack, grounded in three design principles:
Teams are moving toward composable, chain-agnostic architectures.
Ingestion, indexing, transformation, and querying are no longer bundled; they’re decoupled, allowing each layer to evolve independently.
This modularity is essential in a world where protocols operate across Ethereum L1, Arbitrum, Base, zkSync, and Solana-like chains.
It enables selective scaling, faster debugging, and long-term maintainability.
Trust assumptions are shifting. It’s no longer enough to read data; you need to prove it.
Whether it's price feeds, governance votes, or automation triggers, teams are building pipelines that verify correctness before execution.
From zk-based attestations to AVS-backed validations, the move from “trust the source” to “verify the outcome” is accelerating.
Data in Web3 doesn’t just inform, it acts.
Modern systems don’t stop at dashboards. They power real-time automation, dynamic governance, and incentive distribution.
Teams are designing event-driven analytics pipelines where insights drive execution, whether it’s a smart contract trigger, DAO vote, or token payout.
This transformation brings challenges but also opportunities.
Protocols that understand and solve for volume, velocity, and veracity will not just operate more efficiently, they’ll unlock new forms of coordination, new ways of governing, and new classes of user experiences.
Web3 data isn’t just bigger or faster, it’s structurally different.
As protocols become more modular, users become more active, and systems more interconnected, the demands on data infrastructure are rising sharply.
The challenges of volume, velocity, and veracity aren’t edge cases. They’re central to how modern decentralised systems operate.
Solving these challenges requires more than upgraded tools. It takes a mindset shift from managing data as a byproduct to engineering it as a core system layer.
Teams that build with this in mind will not only move faster and more reliably but also set the foundation for smarter automation, better governance, and more trustworthy user experiences.
Growth Lead
FAQs
Web3 data is decentralized, trustless, and fragmented across multiple chains. Unlike Web2’s centralized databases, Web3 requires reconstructing state from raw on-chain logs, often in real time.
Web3 generates massive volumes from smart contracts, token transfers, NFTs, and validator logs across chains. Traditional systems can't efficiently store, index, or query this scale of event-driven data.
High-speed data is critical for DeFi, bots, oracles, and automation. Even a few seconds of delay can result in missed trades, incorrect liquidations, or failed governance actions.
Veracity ensures that on-chain data is accurate, final, and verifiable without relying on centralized sources. It prevents errors from RPC failures, reorgs, or manipulated oracle feeds.
Modern teams use modular indexers like Subsquid, streaming engines like Redpanda, verifiable APIs via Eigenlayer AVS, and event-driven architectures to handle volume, velocity, and veracity effectively.
