Redefining Data Infrastructure: Web3 Analytics vs. Traditional Approaches

We’re entering an era where analytics isn’t just about optimization—it’s about trust, transparency, and decentralization. Traditional analytics thrives on control and ownership. Web3 analytics flips that model on its head. To understand this transition, we need to define what both paradigms represent, how they differ, and what’s changing at the infrastructure, tooling, and philosophical levels.

Defining Web3 Analytics

Web3 analytics marks a shift in how we think about data—not just how it’s queried, but how it’s owned, secured, and interpreted.

At its core, Web3 analytics is the practice of extracting insights from decentralized systems. That means pulling behavioral patterns, economic trends, or governance metrics directly from public blockchains, smart contract logs, and peer-to-peer protocols. Unlike traditional analytics—which runs on top of private databases—Web3 analytics is built into the infrastructure itself. The data is already there, publicly accessible, cryptographically verified, and tamper-proof.

What makes it different isn’t just the tech stack—it’s the philosophy behind it.

• Decentralization: There’s no central authority collecting and processing your data. Information lives across a distributed network of nodes, reducing the risk of breaches and eliminating single points of failure.

• User sovereignty: Instead of platforms owning your data trail, you own it. You decide which applications can read from your wallet, how you want to interact, and when you disconnect.

• Transparency: Every transaction, vote, or interaction is recorded on-chain. Anyone can audit the data. The system is open by default.

Instead of relying on a backend to report analytics, you analyze the chain directly. This unlocks a new kind of visibility—one that doesn’t depend on third-party logs or invasive tracking tools. It also means building analytics tools that work without cookies, sessions, or user IDs.

And because the data is both permanent and public, Web3 analytics isn't just about insight—it’s about accountability. DAOs, protocols, and NFT platforms are now expected to explain what’s happening in their ecosystems, with the data to back it up.

Defining Traditional Analytics

Traditional analytics, in contrast, operates in a closed system. Data is collected through web apps, mobile SDKs, or backend services, then funneled into centralized warehouses. These warehouses are controlled by the company or platform that owns the product—and, by extension, owns the data.

This model makes sense in many Web2 contexts. It allows for:

• Structured schemas and event logs

• Fast A/B testing

• Detailed customer segmentation

• Optimization based on cohort analysis and attribution

But it also comes with trade-offs.

• Centralization: All data lives in a few centralized services—like Google Analytics, Amplitude, or internal PostgreSQL clusters. If those are compromised, everything goes with them.

• Opacity: Users rarely know what’s being collected or how it’s being used. You might click “Accept Cookies,” but what happens next is rarely clear.

• Control: Once data is collected, the user loses visibility. It’s stored, enriched, and used—sometimes resold—without meaningful consent or transparency.

Traditional analytics is designed for performance and business optimization. It works well when the platform is the center of the universe. But in the world of decentralized applications, self-custodied wallets, and interoperable protocols, it falls short of providing insight without overreach.

Web3 Analytics vs. Traditional Analytics: A Shift in Power, Purpose, and Practice

If you’ve ever used Google Analytics, Mixpanel, or Snowflake, you’ve seen traditional analytics in action. It’s powerful, efficient, and built to scale. But it’s also deeply centralized—designed for a world where platforms control user identity, session state, and data flow.

Web3 analytics doesn’t just tweak that model. It rewrites it.

In Web3, you don’t “track users”—you observe on-chain behavior. You don’t “collect data”—you interpret what’s already public. And you don’t depend on centralized pipelines—you analyze decentralized systems in their native format.

Let’s walk through the key differences, from the way data is captured to how it’s governed.

Data Ownership and Access

• Traditional Analytics: The platform owns the data. Users interact with the product, but their behavior is captured, stored, and processed on the company’s servers—often without explicit visibility. You agree to vague terms and lose control at login.

• Web3 Analytics: The data is public and belongs to the system. It’s stored on blockchains, visible to anyone, and structured around wallet addresses. If you want to know how users behave, you read the ledger—not a private log file.

Bottom line: Traditional analytics gives power to platforms. Web3 analytics gives transparency to everyone.

Identity and Tracking

• Traditional Analytics: Identity is explicit. Users log in, sessions are tracked, cookies and device IDs follow you from screen to screen. Attribution is built into the system.

• Web3 Analytics: Identity is pseudonymous. One person might use multiple wallets—or one wallet might serve multiple purposes. There's no login, no session, no cookie trail. You work with behavioral signals, not declared identities.

Tools like wallet clustering, ENS resolution, or on-chain reputation systems help—but fundamentally, you're analyzing actions, not profiles.

Data Structure and Collection

• Traditional Analytics: Events are defined by the app. You decide what to track—clicks, conversions, scroll depth—and instrument it via SDKs or tags. The schema is clean, predictable, and optimized for your own backend.

• Web3 Analytics: The data is already there. You don’t choose what gets logged—the blockchain does. But it’s messy: smart contract events, calldata, opcodes, transaction logs. You have to decode, filter, and stitch together behavior across many contracts and protocols.

Tools like The Graph, Dune, and StarRocks help transform this data into usable insights—but it’s on you to interpret the meaning.

Transparency and Trust

• Traditional Analytics: Opaque by default. Users often don’t know what’s being collected or how it’s used. Third-party trackers follow them across platforms. Companies may share or sell data behind the scenes.

• Web3 Analytics: Transparent by default. Every transaction is public, timestamped, and verifiable. No hidden events. No data selling. Just open ledgers anyone can read.

But transparency cuts both ways. Anyone—including competitors or malicious actors—can access this data. So Web3 analytics requires ethical design, not just technical tooling.

Performance and Infrastructure

• Traditional Analytics: Optimized for central control. Warehouses like Snowflake and BigQuery deliver fast, scalable joins. Everything lives in a structured format, and ETL pipelines are stable and mature.

• Web3 Analytics: Decentralized data is harder to work with. You need to stream from RPC nodes, index contracts, and join messy logs across chains. Traditional warehouses often choke on this complexity.

That’s why many Web3 teams use StarRocks, which can:

• Query directly from object storage (e.g., Apache Iceberg)

• Perform fast, federated joins without denormalizing

• Power real-time dashboards at low latency

TRM Labs is a great example—processing petabytes of blockchain data across 30+ chains for fraud detection, powered by StarRocks under the hood.

Privacy and Consent

• Traditional Analytics: Consent is often performative. You click “Accept” on a cookie banner, and a dozen trackers light up behind the scenes. Data is collected in bulk, sometimes shared with third parties, and rarely deleted.

• Web3 Analytics: Users don’t “give” data—it’s already public. The question becomes: how do you interpret data ethically, without linking wallets to real identities unless absolutely necessary?

Modern Web3 analytics emphasizes:

• Cohort-level analysis over individual fingerprinting

• Zero-knowledge proofs for private stats

• Privacy-aware tooling that respects on-chain norms

Use Case Focus

Philosophy and Design Assumptions

• Traditional: The product owns the user. It tracks, optimizes, and monetizes behavior to drive growth.

• Web3: The user owns the experience. They opt into contracts, transactions, and voting—on their own terms.

Web3 analytics doesn’t assume consent—it earns it by being open, verifiable, and respectful.

Summary Table

Challenges in Evolving from Traditional to Web3 Analytics

So what happens when a team that’s used to Web2 tooling moves into the decentralized world?

The Loss of "Tracking" as You Know It

There’s no identify() function in Web3. No session cookies. No attribution pixel. You can’t just instrument a signup funnel with three click events.

You need to think in terms of event graphs, not user journeys. A transaction on-chain might represent “user staked 100 tokens”—but only if you decode the stake() function and know what pool it interacted with.

Tooling Gaps

In Web2, the analytics stack is rich and battle-tested:

• Frontend: Segment, RudderStack

• Storage: Snowflake, Redshift

• Visualization: Looker, Metabase

In Web3, it’s still maturing. You'll juggle:

• Indexers like The Graph

• Public query platforms like Dune

• Storage lakes like Iceberg

• High-performance engines like StarRocks (used by teams like TRM Labs for real-time analytics across 30+ chains)

There’s no one-size-fits-all.

Skills Gap

Data analysts are often comfortable with SQL, dashboards, and clean schemas. But Web3 requires:

• Understanding of smart contracts and EVMs

• Ability to read transaction traces

• Comfort stitching on-chain and off-chain metadata

It’s less “drag-and-drop” and more “decode, enrich, normalize.”

Fragmented Data Across Chains

Each chain has its own quirks: Solana uses account data differently than Ethereum. Polygon might fork off behaviors mid-stream. You can’t assume uniformity.

You’ll often need to build a unified model across chains—and that takes effort.

Data Stack Differences: Traditional vs. Web3 Analytics

Let’s break down the components that make up the analytics stack in both paradigms.

A Real-World Benchmark: How TRM Labs Rebuilt Web3 Analytics with StarRocks + Iceberg

One of the most telling examples of next-generation Web3 analytics in action comes from TRM Labs, a leading blockchain intelligence company serving law enforcement agencies, financial institutions, and crypto compliance teams worldwide.

TRM’s platform ingests and analyzes petabytes of blockchain data across 30+ networks—including Bitcoin, Ethereum, Solana, and Binance Smart Chain—to track illicit finance, detect fraud, and trace funds in real time. The nature of their work demands sub-second insights into complex transaction flows, wallet behaviors, and smart contract interactions, all while maintaining forensic-grade auditability.

The Challenge: Traditional Warehousing Couldn’t Keep Up

TRM initially relied on Google BigQuery for analytical processing. But as their data volume exploded and query complexity increased—especially around multi-table joins, historical traceability, and high-concurrency investigations—performance became a bottleneck:

• Latency increased significantly for fraud detection dashboards

• Pre-aggregation requirements slowed down investigative workflows

• Joins across normalized wallet, transaction, and contract metadata became operationally expensive

The limitations of a warehouse designed for Web2-style event data were becoming clear. TRM needed a data stack purpose-built for deep, real-time analytics on semi-structured blockchain data.

The Solution: A Lakehouse Architecture with StarRocks + Apache Iceberg

TRM Labs rearchitected their core analytics stack around a modern lakehouse model:

• Apache Iceberg as the unified, append-friendly storage layer—capable of storing partitioned blockchain logs, decoded smart contract events, and off-chain metadata in open formats

• StarRocks as the high-performance analytical engine—optimized for complex, join-heavy workloads with columnar storage, vectorized execution, and cost-based optimization

Why this setup worked:

1. No Need for Denormalization

   • StarRocks can join across wallet tables, transaction logs, event traces, and entity metadata without flattening the data or materializing pre-joined views.

   • This was critical for forensic queries like: “Trace funds from address X, across bridges and swaps, until they exit to fiat.”

2. Sub-Second Query Latency

   • Even on billions of rows, StarRocks delivered <1s response times for most interactive queries—vital for internal dashboards used during active investigations or regulatory disclosures.

3. High Concurrency Without Bottlenecks

   • Dozens of analysts, investigators, and automated systems run thousands of queries per hour. StarRocks’ distributed execution model supports this without locking or degraded throughput.

4. Real-Time + Historical Hybrid Workloads

   • TRM combines live blockchain streams (for anomaly detection) with long-term ledger data (for historical analysis)—and queries both in one unified environment.

5. Auditability and Compliance

   • Because StarRocks queries Iceberg tables directly, there’s no need for multiple ETL hops or intermediate stores. That means a single source of truth—easier to govern, easier to explain in court, easier to trust.

The Bigger Picture: A New Standard for Web3 Analytics Infrastructure

TRM’s migration from BigQuery to StarRocks + Iceberg isn’t just about cost or speed (though both improved significantly). It reflects a deeper trend:

• Moving away from generalized warehouses toward OLAP engines optimized for semi-structured, multi-tenant, join-heavy workloads

• Designing analytics stacks that can operate natively on blockchain-style data, rather than force-fitting it into Web2 schemas

• Building for flexibility, interpretability, and zero compromise on transparency

In short, TRM Labs shows what it looks like when Web3 analytics is done right: scalable, real-time, and aligned with the forensic, regulatory, and operational needs of decentralized ecosystems.

Let me know if you’d like a diagram of this architecture or an expanded case study format.

Future Trends in Web3 Analytics

As decentralized systems mature, the expectations for analytics will evolve from “nice-to-have” dashboards to mission-critical infrastructure. We’re no longer just tracking transactions—we’re trying to understand how decentralized systems behave, how trust is established, and how incentives shape entire ecosystems.

Here are the trends that will define the next phase of Web3 analytics.

Real-Time, Cross-Chain Analytics Becomes the Baseline

Most analytics pipelines today operate on single-chain data (usually Ethereum) and are run in batch. But the reality of Web3 is multichain. Users bridge assets across chains, interact with L2 rollups, and switch ecosystems on the fly.

Expect to see:

• Unified query layers that abstract across Ethereum, Solana, Avalanche, BNB Chain, and others

• Streaming ingestion pipelines that let you monitor swap events, votes, or mints in real time

• Engines like StarRocks that can scan billions of rows from Iceberg tables and respond to fraud triggers in under a second

TRM Labs already operates at this level—processing 30+ chains for compliance and forensics at forensic-grade resolution.

Privacy-Preserving Analytics Will Go Mainstream

Web3 has a paradox: all data is public, but users are pseudonymous. As analytics becomes more advanced, so does the risk of deanonymizing wallets. This will force teams to rethink how they extract insight without compromising privacy.

Emerging solutions include:

• Zero-Knowledge Proofs (ZKPs) to aggregate metrics (e.g., TVL, turnout, churn) without revealing individual contributors

• On-chain ML models trained on anonymized data to detect risk or surface trends

• Cohort-based analytics that replace individual-level tracking with behavioral clustering

This isn't a fringe concern—any protocol claiming to be “trustless” will need analytics that preserve that trust model.

Decentralized Data Infrastructure Will Replace Closed ETL Pipelines

In traditional analytics, ETL pipelines are centralized black boxes. In Web3, we’re seeing the rise of composable data layers:

• Apache Iceberg as the de facto standard for large-scale, immutable storage across decentralized and off-chain metadata

• Lakehouse engines like StarRocks enabling federated joins without flattening or denormalizing

• Open query fabrics that bridge IPFS/Arweave data with contract logs, wallet graphs, and token metadata

This stack isn’t just about performance—it’s about auditability. Teams like TRM don’t just need to answer queries fast—they need to explain how they got the answer to regulators, investigators, or auditors.

Agentic and Autonomous Analytics

Just as Web3 apps are moving toward composable, autonomous systems (e.g., DAOs, bots, smart agents), so too will analytics. We’re starting to see:

• Analytics agents that monitor contracts, detect anomalies, and take on-chain actions (e.g., freezing wallets, raising proposals)

• Self-updating dashboards that react to real-time network state, not batch updates

• Auto-governing protocols that adjust incentives or upgrade contracts based on observed metrics

This ties analytics directly into protocol operations—less “reporting after the fact” and more “analytics as a feedback loop.”

AI + On-Chain Reasoning

Right now, Web3 analytics requires a lot of manual interpretation. You decode logs, map wallet behavior, and write queries by hand.

But with advances in large language models, vector databases, and on-chain indexing, we’ll see:

• AI copilots that translate plain English into on-chain SQL

• Conversational dashboards that let DAO members ask questions like “Which cohort dropped off after last proposal?” and get real-time answers

• Chain-aware LLMs that understand protocol mechanics and simulate future outcomes (“What happens to staking rewards if we cut inflation 20%?”)

Expect analytics to become more accessible—not just to data teams, but to DAO voters, governance stewards, and builders.

From “Looker Dashboards” to Ecosystem Intelligence

Web3 analytics won’t stop at product metrics. It will evolve into ecosystem-level intelligence—a layer that informs governance, risk management, and protocol design.

You’ll be able to:

• Monitor ecosystem health: how liquidity, usage, and governance are trending across protocols

• Forecast token economics: how supply/demand dynamics evolve under different rule sets

• Detect systemic risk: which contracts or bridges are chokepoints in multi-chain flows

In short: analytics moves from being a reporting tool to a coordination tool.

Analytics as Public Good

In Web2, analytics is proprietary. But in Web3, data is already public—so we’ll see more projects publishing open dashboards, live metrics, and subgraphs.

This shift will:

• Empower researchers and contributors

• Raise the transparency bar for DAOs and DeFi protocols

• Encourage shared tooling and composability

Tools like Dune, The Graph, and StarRocks-based open dashboards will lead the way in powering public insights.

Final Thoughts

Web3 analytics asks us to rethink everything we thought we knew about data. In the Web2 world, analytics meant control—platforms collected what they wanted, stored it behind closed doors, and used it to optimize whatever metric mattered most.

But Web3 flips that. The data’s already out there—open, permanent, and verifiable. The job now isn’t to capture behavior, but to make sense of it without overstepping. That’s a much harder task, but also a more honest one.

In this new world, analytics isn’t about tracking people. It’s about observing patterns in a system where users are pseudonymous, behavior is transparent, and no one’s handing you clean event logs. You’re not just running funnels—you’re decoding smart contract calls, clustering wallets, and piecing together how a protocol is being used in the wild.

That takes new tools. It takes engines like StarRocks that can scan billions of blockchain events without flattening the data. It takes open formats like Iceberg, built for scale and auditability. And it takes a different mindset—one rooted in respect for user sovereignty and a willingness to work with messy, decentralized systems.

TRM Labs didn’t move away from BigQuery because it was trendy—they did it because the old model couldn’t keep up. Their new stack wasn’t just faster. It was fairer. More flexible. More transparent. And that’s the direction the whole ecosystem is headed.

Web3 analytics isn’t a dashboard on the side—it’s becoming the heartbeat of how decentralized systems run. From real-time fraud detection to tokenomics to governance, insight is no longer optional. It’s the only way to steer the ship.

And if we do it right—if we build analytics that are fast, ethical, and built for this new reality—then maybe we don’t just understand the data. We understand the systems we’re all helping to build.

FAQ

What is the difference between blockchain analytics and Web3 analytics?

Blockchain analytics focuses on raw on-chain data—token transfers, wallet activity, smart contract calls. It’s commonly used for compliance, forensics, and fraud tracing.

Web3 analytics builds on that by interpreting how users interact with dApps, DAOs, games, or NFTs. It adds behavioral context and product-level insights—without requiring user identity.

Do I need denormalization for Web3 analytics?

Not with modern engines. Systems like StarRocks eliminate the need to flatten data by supporting real-time, high-performance joins across large, normalized datasets.

How does StarRocks help with Web3 analytics?

It’s optimized for analytical workloads with:

• Sub-second query latency

• Complex joins across Iceberg tables

• Real-time + batch hybrid workloads

• No denormalization needed

TRM Labs uses it to analyze data across 30+ chains at scale.

Can I run traditional analytics tools on blockchain data?

Technically yes—but you’ll hit limits fast. Tools like Snowflake or BigQuery weren’t built to handle hex-encoded calldata, smart contract logs, or wallet clustering at scale.

Is Web3 analytics ethical?

It can be. Done right, Web3 analytics:

• Respects pseudonymity

• Avoids invasive fingerprinting

• Uses cohort- or behavior-based models

• Employs ZKPs to preserve privacy while still extracting insight

Ethics has to be built into the system—not retrofitted later.