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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:

• Massive volumes across networks
• Sub-second speed for DeFi and automation
• The need to verify integrity without relying on trusted intermediaries

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.

The 3Vs Framework for Web3 Data

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.

Volume

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.

Velocity

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.

Veracity

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.

Volume: The Avalanche of On-Chain and Off-Chain Events

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.

The Drivers of Volume in Web3

• Rollups and modular architectures are fragmenting execution across dozens of chains, each producing independent state histories.
• DeFi protocols like Uniswap, GMX, or Aave generate high-frequency events: swaps, borrows, liquidations, LP movements.
• NFT marketplaces and gaming apps produce vast amounts of metadata and interactions tied to user identity and asset provenance.
• Validator ecosystems and oracles like Chainlink or RedStone contribute continuous feeds of staking, randomness, and pricing data.

The Infrastructure Challenge

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:

• Storage: Persisting terabytes or petabytes of historical and real-time data across chains
• Indexing: Parsing logs, traces, and states from heterogeneous runtimes (EVM, WASM, custom VMs)
• Querying: Enabling fast, reliable access to relevant slices of data without scanning entire ledgers

Emerging Patterns in Response

To manage this, leading teams are adopting:

• Modular data lakes with pre-processed event streams and deduplicated logs
• Chain-specific indexers like Subsquid for efficient WASM/EVM hybrid indexing
• Event-driven storage models, where only relevant contract events are retained, rather than full trace trees
• Stateful APIs that abstract multi-chain data stitching (e.g., ZettaBlock or Space & Time)

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.

Velocity: The Speed at Which Data Streams in Web3

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.

Where Velocity Becomes a Bottleneck

• Liquidation engines rely on up-to-date collateral ratios. Even a few seconds of delay can mean bad debt builds up before a position is closed.
• Automation tools like keepers and bots trigger smart contract actions. Without millisecond-level responsiveness, critical tasks can be missed or misfired.
• DEX aggregators need live data on pool liquidity and gas prices. Routing trades with outdated information leads to slippage or failed transactions.
• Bridges and intent-based protocols coordinate across chains. Any delay introduces risk; users may be front-run or funds may be locked longer than expected.

In these systems, data is not passive. It’s the fuel that drives autonomous, programmable logic; if it lags, the logic breaks.

The Infrastructure Challenge

Traditional systems batch data, buffer queues, and retry later. Web3 systems can’t afford that luxury.

Here, every delay compounds risk:

• Oracles push stale prices
• Bots miss liquidation windows
• DAOs misread governance outcomes
• Rollup bridges delay fund availability

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.

Architectural Responses to Web3 Velocity

To handle high-speed data flows, modern teams are shifting toward:

• Stream processors like Redpanda and WarpStream that offer Kafka-compatible performance with better horizontal scalability
• WASM-native edge handlers, allowing smart filtering and transformation closer to the ingestion point
• Event-level pipelines, where each on-chain event becomes a trigger for microservices or workflows, rather than waiting for full blocks or full traces
• Low-latency indexing layers, e.g., ZettaBlock or proprietary indexers that support sub-second query response times across multi-chain data

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.

Veracity: Trust, Quality, and Consistency in Decentralised Data

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.

Why It’s Hard

• Blockchain Reorgs: When chains temporarily fork, data can be reverted even if it was already indexed or acted upon.
• Oracle Inconsistencies: Price feeds and off-chain data can be delayed, manipulated, or sourced from unreliable inputs, leading to incorrect decisions.
• Indexing Errors and Gaps: Events can be missed or misinterpreted due to custom contract logic, proxy patterns, or non-standard emissions.
• Sybil Attacks and Data Spoofing: Fake accounts and manipulated activity can distort analytics, DAO metrics, or incentive programs if not filtered properly.

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.

How Leading Teams Ensure Veracity

• Cryptographic proofs: Some protocols now use ZK or STARK proofs to validate off-chain data claims before injecting them on-chain.
• Finality buffers: Automation and analytics systems wait for X blocks before acting to avoid reorg contamination.
• Subgraph verifiability: Teams use deterministic subgraph deployments with reproducible indexing pipelines (e.g., using The Graph or Subsquid).
• Data attestations: Newer APIs like Eigenlayer AVS or Avail are exploring ways to attach verifiable data commitments to execution flows.
• Custom integrity checkers: Builders implement their own guards that compare multiple sources (e.g., multiple RPCs) to detect inconsistencies.

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.

When the 3Vs Collide: The Real-World Complexity

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 Multi-Chain NFT Aggregator

A platform that aggregates NFT data across chains like Ethereum, Base, and Polygon faces all three Vs at once:

• Volume: It must collect massive amounts of data listings, trades, metadata, and user interactions from multiple blockchains.
• Velocity: Buyers expect live updates on prices and listings, especially during fast-moving mints or trending collections.
• Veracity: Metadata stored on IPFS or Arweave may load slowly or be outdated, and transactions on L2s may not be finalised when first displayed.

Failure to get any of these right means:

• Mismatched floor prices
• Inaccurate collection stats
• Missed trading opportunities

A Cross-Rollup DEX or Intent Protocol

A DEX or intent-based trading system operating across Arbitrum, Optimism, and zkSync has to handle:

• Volume: Constant streams of pool states, trade events, and bridge updates across multiple chains
• Velocity: Trades must be routed within milliseconds to avoid slippage or MEV frontrunning
• Veracity: Execution depends on timely, accurate oracle data and cross-chain messages, both of which can be delayed or inconsistent

If any part lags or fails:

• Trades may be routed inefficiently or executed at the wrong price
• Users can lose funds from mispriced or failed transactions
• The protocol becomes exploitable via arbitrage or stale state attacks

A DAO Governance Dashboard

A DAO dashboard that tracks proposals and voting across chains faces:

• Volume: Multiple proposals, token balances, and delegation flows across strategies like Snapshot, Tally, or on-chain voting
• Velocity: Users expect live vote counts, especially during close or high-stakes governance phases
• Veracity: Delegation chains, proxy contracts, and inconsistent RPCs can lead to incorrect vote tracking

If not handled correctly:

• Vote counts may be inaccurate
• Proposal outcomes may not reflect true participation
• Delegates and voters lose trust in the governance process

Why It Matters

When the 3Vs converge, teams face not just performance bottlenecks but systemic risk. Misaligned data across chains, delayed execution, and unverifiable sources can create:

• Financial loss in DeFi
• Governance errors in DAOs
• User frustration in consumer apps
• Protocol exploits due to bad assumptions

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.

Modern Tools and Approaches for Web3 Data Analytics (2025)

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.

Modular Indexing Frameworks: Decoupling What You Index from 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.

• Subsquid: A modular framework supporting EVM and WASM chains, with customizable pipelines and decentralised indexing nodes
• ZettaBlock: Offers low-latency APIs and abstracts multi-chain complexity for developers building real-time apps and analytics layers

These frameworks let teams move beyond centralised subgraphs and build indexers that match the scale and specificity of their own protocols.

Stream-Native Data Ingestion: Speed at the Core

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.

• Redpanda and WarpStream: Kafka-compatible engines with higher throughput and lower ops overhead
• Use cases: streaming validator rewards, DEX trade flows, oracle feeds, or governance triggers

These tools support millisecond-level event ingestion, enabling responsive automations, near real-time dashboards, and event-driven workflows across chains.

Verifiable APIs and Data Attestation: Making Data Trustworthy by Default

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.

• Avail and Eigenlayer AVS: Build infrastructure for verifiable data access, offering cryptographic attestations tied to on-chain state
• Ideal for bridge protocols, DeFi risk engines, and automation layers that can’t afford incorrect input

This is a critical shift from trust-based reads to proof-based pipelines, reducing attack surfaces and downstream failures.

Programmable Query Layers: Moving from Dashboards to Data Apps

Instead of writing SQL queries in dashboards, analysts and engineers increasingly need programmable, API-first access to blockchain data.

• ShroomDK (Flipside): Provides curated, indexed datasets with API access and SQL-style control
• Dune v2: Adds support for private datasets, scheduled queries, and internal dashboards

These platforms accelerate iteration, remove the need to manage infrastructure, and unlock insights from large-scale on-chain datasets without waiting for engineering support.

Event-Driven, Layered Architectures: Building for Composability and Scale

Modern data stacks are becoming modular, structured into clear layers that mirror the lifecycle of on-chain data:

• Ingestion Layer: Filters, deduplicates, and enriches incoming events
• Transformation Layer: Maps raw logs into structured, labelled data
• Query Layer: Powers APIs and analytics through dynamic schemas
• Application Layer: Feeds dashboards, bots, scoring systems, and alerts

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.

Strategic Considerations for Builders and Analysts

Here are five principles that matter in 2025 for anyone designing resilient, high-performance Web3 data systems:

Design for Finality, Not Just Freshness

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.

Treat Indexers as First-Class Infra

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.

Align Data Cost with What Actually Matters

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.

Plan for Chain Diversity from Day One

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.

Build for Verifiable Trust, Not Just Data Access

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.

The Road Ahead: Modular, Verifiable, and Built for the Long Term

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:

Modular by Design

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.

Verifiable by Default

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.

Application-Aware Analytics

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.

Conclusion

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.

• Volume pushes storage, indexing, and querying to their limits
• Velocity demands real-time performance without compromising safety
• Veracity calls for trustless, verifiable data in adversarial environments

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.

Astha Baheti

Growth Lead

Astha Baheti is the Growth Lead at Lampros Tech, a Blockchain development company helping businesses thrive in the decentralised ecosystem. With an MBA in Marketing and hands-on experience in digital marketing and content strategy, she brings expertise in crafting clear, impactful communication that aligns business goals with audience needs. At Lampros, Astha focuses on translating complex Web3 concepts into accessible narratives that drive engagement and awareness.

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