Blockchain analytics isn’t just about parsing on-chain data. It’s about making sense of one of the messiest, noisiest, yet most transparent datasets we’ve ever encountered. Imagine trying to reverse-engineer behavioral patterns from a decentralized, ever-growing torrent of pseudonymous transactions—written in hex, scattered across incompatible schemas, and often weaponized by bad actors. That’s the job.
Done right, blockchain analytics is a fusion of forensic investigation, real-time monitoring, and data architecture. It enables regulators to track illicit flows, lets product teams power customer-facing dashboards, and helps fraud teams surface anomalies before they become headlines.
Let’s break down how it works, where it matters, and why the underlying tech—like Iceberg and StarRocks at TRM Labs—is redefining how modern data systems handle transparency at scale
Blockchain analytics isn’t a single tool or script—it’s a full-stack data system designed to ingest, decode, enrich, and serve massive volumes of semi-structured, multi-chain data. Here’s what makes it all work under the hood:
Interfaces with both on-chain nodes (e.g., Geth, Solana RPC) and third-party APIs (e.g., Alchemy, QuickNode)
Capable of pulling full blocks, logs, internal traces, and real-time events from multiple protocols simultaneously
Requires durable ingestion layers—Kafka, Flink, or cloud-native equivalents—to ensure consistency and fault tolerance
Translators that convert raw blockchain logs into structured event models using verified ABIs and custom schema mappers
Must support a wide range of contract standards (ERC-20, 721, 1155, Solana programs, etc.)
Joins raw data with off-chain labels, risk scores, price feeds, and token metadata
Enables higher-order interpretation: e.g., labeling “0xabc…” as a centralized exchange wallet or identifying a flow as a stablecoin bridge exit
TRM’s entity graph is an example of this—merging blockchain activity with known identities, categories, and behaviors
Transforms enriched data into models that power actual use cases:
Behavioral clusters (e.g., mixer patterns, sybil wallets)
Token flow maps (in/out directionality, bridges)
Aggregate metrics (e.g., average TX volume, daily active wallets)
Implemented via batch tools (e.g., PySpark, dbt) or live systems (e.g., materialized views in StarRocks)
Enables fast, complex queries over large datasets—under tight latency and concurrency constraints
Needs to support:
Sub-second to low-second latencies
100s of concurrent users or API calls
Joins across multiple large tables (entities, tokens, chains)
At TRM Labs, StarRocks replaced BigQuery and Postgres to meet these demands—achieving a 50% P95 latency improvement and handling over 500 queries per minute
Typically object storage (e.g., S3 or GCS) structured via open table formats like Iceberg
Must support partitioning, clustering, time travel, and schema evolution—especially for petabyte-scale workloads
Iceberg's design allows data to be shared across multiple query engines, cloud regions, and deployment environments (e.g., on-prem or air-gapped)
Surfaces insights through APIs, dashboards, and alerts
Interfaces with tools like:
Grafana, Superset for dashboards
REST/GraphQL APIs for external access
Alerting systems for real-time risk monitoring
Backed by caching and pre-aggregation to keep latency down without sacrificing flexibility
Needs to run reliably across cloud, hybrid, or on-prem environments
Kubernetes-native infrastructure (e.g., what TRM uses for StarRocks clusters) supports multi-site deployment and resource isolation
CI/CD for schema and pipeline evolution without downtime
At first glance, blockchain data looks like a dream—every transaction, token transfer, and contract call, publicly recorded and immutable. But the reality is far less friendly. On-chain data isn’t built for analysis; it’s built for consensus. It’s raw, deeply nested, inconsistent across protocols, and often requires heavy decoding just to understand what’s happening.
Let’s walk through what makes blockchain data technically challenging—and how modern analytics platforms like TRM Labs turn it into something useful.
Working with blockchain data is like trying to extract clean signals from static. It’s technically rich but semantically poor:
Hex-encoded logs: Inputs, outputs, and event data are stored in raw hexadecimal. Decoding them requires ABIs—if you can get them.
No standard schema: ERC-20, ERC-721, ERC-1155… each contract type comes with its own structure. Add in chains like Solana and Tron, and schema variability explodes.
Opaque contract logic: Even with ABIs, behavior can vary wildly across implementations. Verified source code helps—but it’s not always available.
Pseudonymous actors: Addresses have no built-in labels. You can’t tell whether 0x1ab... is a user, an exchange, or a smart contract without additional context.
Despite this, analysts are expected to build clean tables, track money flows, and flag suspicious behavior—all at scale and in near real-time.
Before diving deeper into the mechanics, it’s worth stepping back to understand why blockchain analytics requires such a specialized stack in the first place.
Pain Points
Unstructured by Design: Blockchain data wasn’t made for analytics. It’s optimized for consensus, not clarity. You’re dealing with deeply nested logs, arbitrary contract logic, and zero enforced schema.
No Labels, Just Hashes: Everything is pseudonymous. A wallet could belong to a user, an exchange, a bridge, or a DAO treasury—and you have no idea unless you enrich it.
Inconsistent Across Chains: There’s no shared schema across protocols. ERC-20 events don’t behave like ERC-721s. Solana, Ethereum, and Tron are entirely different beasts. Each chain is its own data dialect.
High Velocity and Volume: New blocks arrive every few seconds, carrying thousands of events. Multiply that by dozens of chains, and you’ve got petabyte-scale data before you even blink.
Heavy Compute Demands: Even basic questions like “Who moved USDC through Tornado Cash last week?” require multi-table joins, filters, and aggregations—across billions of records.
Concurrency Is Non-Negotiable: It’s not just about running queries—it’s about running 500+ simultaneous queries with low latency, as TRM Labs does. Dashboards, alerts, and API services all depend on it.
What the Platform Needs to Handle
Schema evolution across many blockchains (e.g., Iceberg)
Join efficiency and vectorized execution for real-time analytics (e.g., StarRocks)
Open table formats for interoperability and cost-efficient storage
Streaming-friendly ingestion to handle live blocks and logs
Flexible enrichment layers that support both manual curation and automated labeling
Kubernetes-native deployments for multi-site or on-prem architecture
Materialized views or pre-aggregations for low-latency metrics
That’s why off-the-shelf warehouse solutions like BigQuery or Snowflake start to buckle under these requirements—especially if you need to run them outside the cloud or embed analytics into user-facing products.
A proper blockchain analytics pipeline isn’t just a glorified ETL job. It’s a complex, high-performance system that deals with messy inputs, joins on fuzzy semantics, and serves mission-critical queries to analysts and customers alike.
The pipeline starts with ingestion—pulling in blocks, transactions, logs, and traces from sources like:
Self-hosted nodes (e.g., Geth, Erigon, Solana RPC)
API gateways (e.g., Alchemy, QuickNode)
Internal Kafka streams or Flink jobs
You’re not just grabbing transaction data—you need full receipts, logs, and traces to reconstruct complex flows like internal calls or contract-generated events.
This step transforms unreadable blobs into structured, queryable records:
Decode logs using verified ABIs from sources like Etherscan
Flatten nested data into typed columns: addresses, token values, timestamps
Normalize token units and precision (especially for stablecoins and LP tokens)
Standardize event types across protocols
At TRM Labs, schema evolution is handled via Apache Iceberg, which lets them gradually align inconsistent structures across 30+ chains without full rewrites—essential for long-term maintainability.
Raw blockchain data has no context. That’s why enrichment is critical:
Wallet labeling (e.g., “Binance Hot Wallet,” “Bridge Contract,” “Scam Cluster”)
Token metadata (e.g., name, type, price, launch date)
Exchange rates (to price transactions in USD or fiat equivalents)
Entity graphs (to track relationships between addresses or wallets)
TRM’s own enrichment engine is backed by an entity graph that correlates on-chain activity with known actors—critical for fraud detection and regulatory reporting.
Once enriched, the data is shaped into models that serve real-world use cases:
Flow models: Trace token transfers across chains or through DeFi protocols
Behavioral models: Identify mixers, sybil clusters, or coordinated actors
Aggregations: Compute time-series metrics like protocol volume, active wallets, or suspicious flows
These are built via batch tools like dbt or PySpark, and increasingly via real-time materialized views powered by StarRocks—especially for dashboards and customer queries.
This is where many analytics systems break. Blockchain workloads aren’t just large—they’re wide (lots of joins), deep (across chains and history), and high concurrency.
That’s why TRM Labs replaced BigQuery and distributed Postgres with a StarRocks + Iceberg architecture:
Petabyte-scale datasets stored in Iceberg (on object storage)
Real-time queries executed via StarRocks
Latencies under 3 seconds, even with 500+ queries per minute
50% improvement in P95 latency, 54% fewer timeouts
Where traditional OLAP tools struggled, StarRocks’ vectorized execution and caching mechanisms proved essential for live dashboards, compliance alerting, and API responses.
Let’s look at TRM Labs—a blockchain intelligence company that helps governments and financial institutions detect crypto fraud. They started with BigQuery and distributed Postgres to power customer queries, but hit major limits when they needed multi-environment support (e.g., on-prem) and petabyte-scale ingestion.
Here’s how they rebuilt their stack:
Distributed Postgres (Citus): For fast lookups and aggregates.
BigQuery: For larger aggregations.
BigQuery couldn’t run in secure/on-prem environments.
Postgres struggled with storage scale and complex joins.
Storage: Apache Iceberg on object storage (schema evolution + time travel).
Query Engine: StarRocks for high-concurrency, sub-second analytics.
ETL: PySpark + dbt for transformations.
Infra: Kubernetes-native deployment across cloud and on-prem.
Why it worked:
Iceberg gave schema flexibility across 30+ chains.
StarRocks handled complex queries (joins, filters, aggregates) with latencies under 3 seconds—even at >500 queries/minute.
Result: 50% improvement in P95 query response, 54% drop in timeout errors.
This isn’t your average data pipeline. Blockchain analytics requires a real-time, high-concurrency system that can turn cryptographic event logs into reliable behavioral insight—at petabyte scale, and often under regulatory scrutiny.
TRM’s journey from Postgres + BigQuery to Iceberg + StarRocks shows what it takes: open formats, vectorized execution, and careful schema design. If you’re building serious blockchain infrastructure, don’t underestimate the data side—it’s where most of the complexity lives, and where the real differentiation starts.
Let me know if you want this turned into a standalone blog post or visualized with diagrams.
Blockchain analytics isn’t a nice-to-have—it’s foundational to how trust, compliance, and visibility function in a permissionless world.
In a traditional financial system, you have banks, intermediaries, and centralized reporting mechanisms. In crypto? You have public ledgers and pseudonymous activity. That’s a double-edged sword: everything is recorded, but very little is labeled or interpretable without context.
This is where blockchain analytics steps in—not as an optional overlay, but as critical infrastructure. Here’s what’s at stake:
Regulators need cross-chain visibility to monitor capital flows, detect systemic risks, and enforce evolving AML and sanctions frameworks. Without analytics, they’re flying blind.
Law enforcement uses on-chain analysis to trace ransomware payments, uncover terrorist financing routes, and unmask large-scale fraud networks. The blockchain might be immutable, but without entity resolution and graph analysis, the evidence stays buried.
Financial institutions now include crypto in their portfolios. Whether issuing loans, onboarding wallets, or monitoring treasury exposure, they depend on blockchain analytics to quantify counterparty risk and maintain compliance.
Startups and dApps embed analytics directly into user-facing products. NFT creators monitor floor price movements and holder distribution. DeFi protocols visualize staking activity and identify usage trends. Wallet apps send real-time alerts on suspicious contract interactions.
None of this is possible without robust blockchain analytics pipelines. It’s not just about querying data—it’s about translating entropy into clarity. And the cost of not having that clarity? Reputational risk, regulatory penalties, and potential exposure to illicit activity.
Forget the hype cycles, token pumps, and viral NFTs. The real utility of blockchain analytics lies in its ability to answer the hard questions—the ones that affect market integrity, protocol health, and user safety.
DEXes: Which trading pairs show signs of manipulation or wash trading?
Bridges: Are cross-chain transfers being used to obfuscate fund origins?
NFTs: Are those high-volume traders real collectors or self-dealing actors?
DAOs: How decentralized is governance in practice—what does voting power distribution really look like?
These questions aren’t abstract. At TRM Labs, they’re part of daily operational workloads. Their platform processes petabytes of blockchain data across 30+ chains and handles more than 500 customer queries per minute—ranging from forensic investigations to high-frequency fintech dashboards.
And critically, they’re doing this with service-level guarantees: sub-second queries at p95 latency, even for complex, multi-table joins across massive datasets.
That level of performance isn’t possible with legacy stacks. TRM moved from BigQuery and distributed Postgres to a data lakehouse architecture powered by Apache Iceberg and StarRocks—a move that enabled schema evolution across chains and vectorized execution at scale. With it, they achieved:
50% reduction in query latency (p95)
54% fewer timeout errors
Flexible deployment across cloud and on-prem environments
It’s a reminder that blockchain analytics isn’t just about insights. It’s about architecture. It’s about building the technical muscle to observe, model, and act on the financial infrastructure of the future—without breaking under load.
So where is blockchain analytics headed? The fundamentals—decode, normalize, enrich, query—are already tough. But as the crypto stack evolves, so do the expectations. The frontier isn’t just about faster queries or cleaner schemas. It’s about fundamentally rethinking who can analyze, how they analyze, and what’s possible once analytics becomes composable.
Here’s what’s next:
We’re already seeing early prototypes that combine large language models with blockchain datasets to generate human-readable insights. But the real promise goes deeper:
Vector embeddings of wallet behavior, contract patterns, or transaction flows can feed into models that classify actors with no known labels.
LLM agents can be built to explore anomalous clusters or simulate investigative workflows—turning tedious forensic work into partially automated pipelines.
Natural language interfaces on top of StarRocks or Iceberg tables could let non-technical analysts ask things like, “Which wallets used Tornado Cash within 3 hops of Binance in the last 48 hours?”
TRM Labs is already halfway there. Their enriched entity graph and structured data pipelines form the perfect training ground for future AI-native investigations.
The analytics stack today is still off-chain: it ingests public data but runs on centralized infrastructure. But protocols like The Graph (indexing) and Eigenlayer (re-staking economic security) hint at a different model:
Query markets where nodes execute analytics tasks in decentralized fashion.
Verifiable compute, where results can be audited on-chain.
End users or smart contracts paying for insights directly, without going through off-chain APIs.
It’s early—but if composable data + compute layers evolve, analytics could become not just about blockchains, but of them.
In a privacy-conscious world, revealing raw data isn’t always acceptable—especially in enterprise, law enforcement, or DeFi risk contexts. That’s where zero-knowledge proofs come in:
Imagine proving a wallet met AML criteria without revealing its contents.
Or building dashboards that show compliance metrics without leaking specific transaction details.
Projects like ZKML, zkOracle, and Succinct Labs are laying the groundwork for this. The implication: blockchain analytics could soon operate under cryptographic guarantees, not just architectural best practices.
Blockchain analytics isn’t just a tooling question—it’s a visibility question. In a world where value moves at the speed of block confirmation and malicious actors operate behind pseudonyms, being able to see what’s happening is power. It’s compliance. It’s defense. It’s strategy.
But seeing clearly at scale takes infrastructure. You’re not parsing logs on a laptop—you’re decoding terabytes of hex-encoded data across 30+ chains, enriching it with dynamic context, and delivering insights to regulators, fintechs, fraud teams, and end users—often in under three seconds.
That’s why the stack matters. TRM Labs didn’t move to StarRocks and Iceberg for fun—they did it because their old stack couldn’t keep up. They needed multi-environment flexibility, real-time performance, schema evolution, and cost efficiency. And they built it.
If you’re working in blockchain—whether on-chain compliance, NFT analytics, DAO tooling, or DeFi dashboards—your infrastructure choices today will define what you can see tomorrow. Blockchain analytics isn’t a niche anymore. It’s core infrastructure for an open, programmable financial system.
Blockchain analytics is the process of extracting meaning from on-chain activity—transactions, contracts, tokens, and more. It involves ingesting data from public blockchains, decoding it into usable formats, enriching it with off-chain context, and serving it up through queries, dashboards, or alerts. It's used in compliance, fraud detection, market intelligence, and real-time product features.
A few things:
It’s deeply nested and hex-encoded.
There’s no universal schema—even on the same chain.
Entities are pseudonymous (wallet addresses are just hashes).
Event structures vary widely across contract types and chains.
New blocks are constant, and the data grows endlessly.
You can—until you can’t. Traditional warehouses struggle with:
High ingestion velocity from dozens of chains.
Real-time schema evolution and query flexibility.
Multi-environment or on-prem deployments.
High-concurrency, sub-second response SLAs.
That’s why companies like TRM Labs built lakehouses with Iceberg and StarRocks.
Apache Iceberg is an open table format designed for data lakes. It supports schema evolution, partitioning, time travel, and decouples storage from compute. In blockchain analytics, it allows you to maintain massive multi-chain datasets with evolving schemas, without rewriting everything downstream.
StarRocks is a high-performance OLAP query engine built for real-time analytics. It features:
Vectorized execution (SIMD) for fast processing.
Advanced caching for hot queries.
Native support for Iceberg tables.
High concurrency handling—500+ QPM with sub-second latency.
TRM Labs chose StarRocks after benchmarking it against Trino and DuckDB, where it outperformed both for complex joins and aggregates.
In brief:
Ingest raw data from nodes and APIs.
Decode and normalize events using ABIs.
Enrich with off-chain metadata and risk labels.
Store it in Iceberg tables on object storage.
Query it in real time with StarRocks.
Surface insights via APIs, dashboards, and alerts.
This enables 500+ queries per minute at sub-three-second latency, powering everything from law enforcement dashboards to fintech user metrics.
AML and compliance screening
Ransomware and fraud investigations
Whale tracking and wash trade detection
NFT and DAO analytics
DeFi protocol usage metrics
Risk scoring and wallet attribution
It’s no longer just about research—it’s embedded into business and regulatory infrastructure.
Three big directions:
AI for On-Chain Forensics: Autonomous agents that detect anomalies or simulate investigations using vector embeddings and LLMs.
Decentralized Analytics: On-chain query markets (e.g., The Graph) and verifiable compute using protocols like Eigenlayer.
Zero-Knowledge Analytics: Generating insights without exposing raw data—e.g., proving compliance without revealing wallet histories.
All of this depends on robust, scalable data pipelines underneath. The future’s wide open, but only if you can see clearly.