Most people think of blockchains in terms of headlines: crypto prices soaring or crashing, high-profile hacks, or the latest NFT boom. But behind all the hype is something far more enduring and quietly transformative: the data.
Every blockchain—Bitcoin, Ethereum, Solana, Avalanche—is a live, append-only record of everything that happens on its network. That means every transaction, every smart contract call, every wallet interaction gets immutably written into public storage. In effect, blockchains are the most transparent financial databases ever created.
So the next logical question becomes: how do we read them? That’s where blockchain data analysis comes in.
At its core, blockchain data analysis is the practice of extracting meaning from decentralized transaction records. It’s part forensic accounting, part behavioral analysis, part infrastructure monitoring. Depending on your lens, it can help you:
Detect and investigate fraud, scams, or sanctions evasion.
Track asset flows across wallets, protocols, and blockchains.
Understand user behavior in NFT or DeFi ecosystems.
Power real-time dashboards for customers interacting with crypto products.
Build machine learning models on top of immutable data.
Unlike traditional databases, blockchain data is public but chaotic. Every byte is transparent, but little of it is labeled or enriched. Wallets are pseudonymous. Transaction payloads are often opaque. Smart contracts behave like black boxes unless decoded. So analyzing blockchain data is less about gaining access, and more about making sense of what’s already out in the open.
Back in 2011, blockchain analytics mostly meant using a block explorer to check wallet balances. If you were serious, maybe you wrote a script to parse Bitcoin transaction data by hand. It was crude, slow, and entirely manual.
That started to change with Ethereum.
The arrival of smart contracts in 2015—and the subsequent waves of ICOs, DeFi protocols, and NFTs—transformed blockchain data into something fundamentally different. A single block might now contain dozens of contract calls, token mints, flash loans, and governance actions, all layered together.
This complexity gave rise to a new generation of blockchain analytics companies like Chainalysis, TRM Labs, Elliptic, and Nansen. They moved past basic lookups and into real-time graph modeling, entity clustering, and on-chain–off-chain correlation. The systems they built could track fund flows across multiple hops and multiple chains—at a scale that demanded serious infrastructure.
As the need for insight outpaced what basic tools could deliver, the stack evolved too. Open table formats like Apache Iceberg brought order to chaotic chain data. Engines built for analytical speed—like StarRocks—made it possible to run sub-second queries across billions of rows, powering everything from compliance dashboards to customer-facing analytics in production environments.
Blockchain data isn’t like a clean Excel spreadsheet or a tidy relational database. It’s messy, fast-growing, and decentralized. Here’s what makes it tricky:
High Volume: Popular chains record thousands of transactions per second. Ethereum alone has surpassed 1 billion transactions.
Low Signal-to-Noise Ratio: For every meaningful transfer, you may have dozens of background transactions, spam contracts, or dusting attacks.
No Real Schema: Transaction data is encoded in hex, payloads vary by contract, and most addresses are unlabeled.
Cross-Chain Complexity: Behavior doesn’t live on a single chain. Smart traders move across Ethereum, Arbitrum, Solana, and back again in seconds.
As a result, blockchain analysis is part data engineering, part forensic investigation. You need infrastructure that can handle large volumes of raw data, modeling techniques that can cut through the noise, and workflows that can trace behavior across fragmented ecosystems.
Today, blockchain analytics isn’t just about auditing the past—it’s about reacting in the moment. NFT marketplaces need to surface trends as they emerge. Exchanges track wallet activity in real time to flag unusual behavior. Compliance systems trigger alerts mid-transaction, not post-facto.
That shift has redefined the performance bar.
Instead of batch jobs and offline reports, modern systems need to run live queries across massive datasets, serve multiple users simultaneously, and do it with latencies low enough to power actual product features.
This is where high-performance engines like StarRocks, paired with Iceberg or Delta Lake, have become foundational. These systems are designed for interactive workloads—supporting high concurrency and sub-second queries, even on petabytes of data.
A real-world example: TRM Labs, a leading provider of blockchain intelligence tools for law enforcement, regulators, and crypto businesses. Their platform analyzes blockchain activity across 30+ chains, processing petabytes of data and handling more than 500 customer queries per minute. Their architecture exemplifies what modern blockchain analytics looks like: open, scalable, and built for real-time decision-making.
This isn’t just a walkthrough of tools and APIs. It’s for anyone trying to make blockchain data useful, at any scale.
If you’re a compliance analyst trying to spot illicit flows…
If you’re a data engineer wrangling on-chain and off-chain data into pipelines…
If you’re a product team building dashboards or customer-facing metrics on crypto data…
Or if you’re just starting out and trying to figure out what the data even means…
This guide is for you.
We’ll walk through how to approach blockchain analytics as an engineering problem—from framing the right question, to designing robust data models, to scaling into real-time pipelines that don’t fall over under pressure. You’ll learn how to handle ingestion, normalization, modeling, querying, and alerting.
Throughout, we’ll reference patterns and practices used by teams like TRM Labs—companies running blockchain analytics as a production system, not a side project.
Before you pull data, spin up infrastructure, or write a single line of SQL—slow down and ask:
What exactly am I trying to find out?
This might sound basic, but it’s the step most teams gloss over. Blockchain data is incredibly dense. Without a sharply defined objective, you’ll end up scrolling through hashes, not getting answers.
The key is to frame a precise, operational question. For example:
Behavioral
“How did user activity shift after a token airdrop?”
“Which wallets are flipping NFTs the fastest within a given collection?”
Investigative
“Where did funds from this flagged wallet go after the exploit?”
“Which addresses interacted with this Tornado Cash contract in the last 72 hours?”
Operational
“What’s the real-time transaction volume for this DeFi protocol?”
“How many unique users interacted with our staking contract this week?”
If you’re stuck, anchor the question in one of these lenses:
A behavioral event (e.g., flash loan, bridge withdrawal)
An entity (e.g., token, wallet cluster, exchange)
A time-bound pattern (e.g., pre/post exploit flows)
At TRM Labs, analysts don’t “explore Ethereum.” They start with targeted questions:
“Can we trace this laundering path across multiple chains?”
“How many hops connect this scam cluster to a known CEX wallet?”
Every decision—what data to load, how to normalize, what schema to use—starts there.
Once you have a question, the next step is to limit the blast radius.
Trying to analyze all chains, all contracts, all time is a recipe for wasted compute and half-finished pipelines.
Define your scope:
Which chain? Don’t treat Ethereum and Solana the same. Pick based on where the activity happens.
What time range? If you’re investigating an exploit from last Tuesday, you don’t need five years of history.
What event types? ERC-20 transfers? NFT mints? Contract calls? Be explicit.
TRM Labs doesn’t index everything equally. They decode mixers, bridges, and CEX wallets heavily; low-activity addresses get lighter treatment. It’s deliberate. Analysis is time- and cost-bounded.
A practical way to think about scope:
Start with one chain (e.g., Ethereum mainnet)
Focus on one or two event types (e.g., token transfers, contract calls)
Choose a defined time window (e.g., block 15M–18M, or the past 30 days)
You’ll know you scoped it well when your queries return quickly, your storage costs stay sane, and your dashboards don’t spin.
With scope set, it’s time to decide: where’s your data coming from?
Services like Etherscan, Alchemy, or TRM’s own API offer parsed data over REST or GraphQL.
Best for: Prototyping, lightweight exploration
Limitations: Rate limits, opaque logic, can’t customize parsing
Operate archive nodes (e.g., Geth or Erigon for Ethereum) to get full raw access.
Best for: Maximum fidelity (traces, internal calls)
Limitations: High storage, ops overhead, tough to scale across chains
What serious teams (like TRM Labs) do:
Ingest decoded chain data into a data lake (e.g., S3)
Store in Iceberg or Delta Lake
Query with StarRocks for sub-second performance at scale
TRM adopted this model after hitting scalability and latency ceilings with BigQuery and distributed Postgres. Their current stack lets them query petabytes across 30+ chains with predictable cost and response time.
Raw blockchain data is machine-readable, not analysis-ready. It needs work.
Start by:
Decoding logs with ABI definitions
Flattening nested fields into typed columns
Normalizing addresses and timestamps
Standardizing decimals, token symbols, and event types
Enriching with external labels (e.g., known entities, risk scores)
Store each stage separately:
raw_events
parsed_transfers
enriched_flows
TRM’s data is fully versioned—every field traceable, every enrichment logged. That’s not overkill. It’s what keeps their findings auditable and production-grade.
You don’t need 15 tools—you need a pipeline that can ingest, normalize, query, and serve blockchain data fast, reliably, and at scale. TRM Labs learned this the hard way: their platform went through multiple architecture phases before settling on a solution that could meet strict latency, concurrency, and deployment requirements.
Here’s what a modern, proven stack looks like—adapted from their implementation:
| Layer | Tools Used | Role |
|---|---|---|
| Ingestion | Kafka, Spark, Flink | Ingest block data, decode logs, extract and transform events |
| Storage | Apache Iceberg on S3 | Open table format for versioned, partitioned data lakes |
| Query Engine | StarRocks | High-concurrency SQL queries with sub-second latency |
| ETL/Modeling | PySpark, dbt | Normalize, enrich, cluster, and pre-aggregate |
| BI Layer | Superset, Grafana, Custom UIs | Dashboards and alerting for analysts, ops, and product teams |
TRM’s platform handles petabytes of blockchain data across 30+ chains, serving more than 500 customer queries per minute. Their requirements were not theoretical:
Multi-environment deployments: On-prem, cloud, and hybrid deployments across secure zones
Petabyte-scale ingestion: Continuous growth in on-chain volume (2–3% monthly)
Low-latency SLA: P95 query latency below 3 seconds, even under high concurrency
High-throughput analytics: Support for investigative queries and live product dashboards
TRM benchmarked Apache Iceberg, Delta Lake, and Apache Hudi for their data lake foundation. Here’s what tipped the scale toward Iceberg:
Schema evolution and support for hidden partitioning
Compatibility with multi-engine querying
Better performance on read-heavy workloads
Open standard, easier to deploy across environments
Delta Lake was ruled out for its limited flexibility in multi-environment setups, and Apache Hudi underperformed in TRM’s benchmarks for typical blockchain aggregation workloads.
TRM compared StarRocks, Trino, and DuckDB over a 2.5 TB test dataset with two core workloads:
Point lookups (e.g., wallet → transaction history)
Complex aggregations (e.g., group by token + time + category with multiple filters)
Their results:
StarRocks: Consistently fastest, especially when caching was enabled. Delivered sub-second latencies even on aggregation-heavy workloads. Also handled concurrency better than the others.
Trino: Performed well on larger clusters but hit a ceiling in latency under load. Lacked caching support at the time of testing (later added in Trino 439).
DuckDB: Fast for small workloads on a single machine, but lacked support for distributed querying and Iceberg predicate pushdown at the time.
They chose StarRocks because it offered:
Sub-second performance on Iceberg tables
Automatic materialized views for repeated queries
Vectorized execution
Better scaling under high concurrency
This gave TRM the performance needed for investigative workflows, real-time compliance alerts, and customer-facing dashboards—all from the same backend.
The Iceberg + StarRocks architecture let TRM:
Avoid data duplication: Query live data without preloading into a warehouse
Simplify ETL: Model directly on top of the lake
Scale cost-effectively: Object storage + decoupled compute = better cost control
Stay flexible: Swap or add engines without re-architecting the whole stack
StarRocks became the execution layer that ties everything together—serving both internal analytics and production-facing services with the same low-latency interface.
If you’re building anything close to real-time, multi-chain analytics, this stack is battle-tested. And if you’re not at TRM’s scale yet, you’ll benefit even more from its simplicity and modularity. Start with the same pieces. Scale them as your use cases grow.
Now the fun part.
You’re not pulling rows—you’re investigating.
Examples:
“Which wallets moved funds through this bridge last week?”
“What are the top 10 token pairs by wash trade likelihood?”
“How many ERC-20 approvals happened just before this rug pull?”
Core techniques:
Graph traversal
Clustering
Time series rollups
Anomaly detection
TRM runs these patterns daily. Their workflows are largely SQL-based now, powered by Iceberg tables and StarRocks views. That means faster answers, fewer one-off scripts.
Don’t wait for performance issues to hit. Build with scale in mind.
Partition by block time or chain ID
Pre-aggregate hot metrics (e.g., top flows, token volume)
Use StarRocks AutoMVs (Automatic Materialized View) to accelerate repeated queries
Bucket large joins by address hash or tx hash
Cache intelligently, isolate workloads
TRM shaved 50% off query latency and reduced timeouts by 54% by tuning clustering and caching. That’s not nice-to-have—it’s what lets the system stay real-time.
A good dashboard doesn’t just look clean. It tells a story.
Replace hex with human-readable labels
Show movement over time, not just totals
Highlight deviations, not just averages
Build drilldowns that connect metrics to events
StarRocks is designed for serving low-latency dashboards. Whether internal (risk teams, analysts) or external (customers), it lets you query billions of rows in real time—without precomputing everything manually.
If your work touches compliance or fraud detection, batch isn’t good enough. You need real-time signals.
That means:
Streaming ingestion
Materialized views that update within seconds
Threshold- or rule-based alerts
Dashboards that reflect the last block, not last night’s ETL
TRM’s system tracks high-risk flows as they happen—mixer exits, cross-chain jumps, sudden fund consolidations. It’s not just fast—it’s accountable.
A good analytics system doesn’t just deliver answers. It evolves.
That means:
Version-controlled transformations
Logged queries and schema changes
Proper testing and observability
Auditability for every number you show
Once you've mastered basic token transfers and contract logs, the real complexity—and value—lies in multi-chain analysis, liquidity behavior, and market manipulation detection. These topics demand not only scale but also flexible modeling, evolving schemas, and domain-specific context.
Serious investigations today rarely stay confined to one chain. Funds move from Ethereum to Arbitrum, then through Tron or BNB Chain before landing on a centralized exchange. Mixers, bridges, and swaps fragment the traceability of assets—on purpose.
But analyzing these flows across chains is non-trivial:
Each chain has its own data format (UTXO vs. account-based)
Different field structures, timestamp formats, and hash schemes
Varying support for contract logs and event standards
How to handle it:
Normalize schemas using Iceberg
Apache Iceberg supports schema evolution, so you can define unified structures (e.g., tx_hash, from_address, to_address, value, timestamp, chain) and enforce them across per-chain tables. This allows you to query cross-chain behavior without hardcoding every new format.
Partition and bucket by chain + time
Partitioning by chain and block_date lets you scan selectively. Bucketing by wallet_hash helps with performance when tracing multi-hop flows.
Leverage StarRocks for multi-table JOINs
Use JOINs between normalized tables to reconstruct flow paths: e.g., ETH withdrawal → bridge transaction on Polygon → swap on Solana. StarRocks’ vectorized JOIN engine and cost-based optimizer handle these complex paths well—even at scale.
Optional: Integrate cross-chain mapping services (e.g., Chainabuse, LayerZero or Wormhole metadata) for enriched context on bridge activity or cross-chain entity resolution.
Liquidity pools are foundational to DeFi, and they generate a massive stream of event data—mint/burn of LP tokens, swaps, impermanent loss, volatility spikes, and yield farming cycles.
Use cases include:
Detecting sudden liquidity exits (a common pre-rug signal)
Monitoring farming strategies for suspicious behavior
Tracking high-APR pools that may be vulnerable to exploits
How to handle it:
Track LP token mints/burns over time
Decode Mint, Burn, and Swap events from DEX protocols like Uniswap, Sushiswap, PancakeSwap, or Curve. Normalize these into structured tables.
Use clustering and bucketing
Bucket LP positions by token pair or pool ID for efficient aggregations. Cluster user behavior over time to detect cyclical or suspicious farming strategies.
Integrate off-chain pricing data
Use DEX APIs or oracles (e.g., CoinGecko, Chainlink) to enrich LP events with price and volume context. This is essential for APR estimation and slippage analysis.
Compute custom metrics
Calculate time-weighted APR, liquidity concentration, protocol-level TVL, and volatility windows—all as materialized views in StarRocks for quick refresh and low-latency dashboards.
NFT activity isn’t just about art—it’s a rich surface for financial patterns: flipping, bot bidding, wash trading, and whale concentration.
How to handle it:
Track transfers, listings, and bids
Parse events from platforms like OpenSea (Wyvern protocol), LooksRare, and Blur. These events may not follow standard ERC-721/1155 transfer semantics—so custom ABIs or off-chain APIs are often needed.
Join on metadata
Merge on-chain transaction data with metadata (collection name, rarity traits, creator address) to analyze value behavior over time. Services like Reservoir, OpenSea API, or LooksRare GraphQL can help here.
Model wash trading
Detect repeated back-and-forth transfers between the same wallets or within a tight cluster. Look for zero-fee transfers, bid-sniping patterns, or sales just above floor price.
Apply graph analytics
Use address graphs and time-windowed activity clustering to identify:
Wallet flipping patterns (e.g., buy-sell within 12 hrs)
Wallets operating as coordinated trading rings
Whale wallets concentrating holdings in certain collections
Serve visualizations via StarRocks dashboards
Fast query performance allows real-time heatmaps of sales volume, wallet activity timelines, or rarity-adjusted price movement—without flattening or denormalizing datasets.
Blockchain analytics isn’t just a matter of SQL or schema design—it’s about turning noisy, decentralized transaction logs into insight that drives action. The most powerful tools won’t help if you’re chasing vague questions or trying to boil the ocean.
What sets mature teams apart is this: they treat analytics as infrastructure. They version their pipelines, test their queries, track lineage, and design for real-time iteration—not one-off reports.
TRM Labs is a case in point. Their analytics system isn’t a dashboard tacked onto a database—it’s a core product capability built on scalable architecture. Apache Iceberg gives them a flexible, schema-evolving foundation. StarRocks gives them the speed and concurrency to serve both internal analysts and external customers without compromise.
If you’re starting your own journey, don’t aim to replicate petabyte-scale from day one. Aim for clarity. Define your question. Scope your data. Pick the right building blocks—and make sure your stack can grow with you.
In a world where blockchain is permanent but its patterns are ephemeral, the real challenge isn’t access—it’s interpretation. And the real opportunity? Turning transparency into an advantage.
Blockchain data is public, decentralized, and pseudonymous. Unlike structured business databases, it lacks consistent schema, meaningful labels, or clear semantics. Transactions are stored as raw hex strings; logs vary by contract; and most addresses have no known identity. This means analysis requires extensive normalization, enrichment, and context.
Not always. For exploration and prototyping, APIs from Alchemy, Etherscan, or TRM are enough. For maximum fidelity—like internal calls, traces, or full contract state—you’ll need archive nodes. That said, most teams skip this and instead build a lakehouse pipeline by ingesting already-parsed data from reliable sources into Iceberg or Delta Lake.
Apache Iceberg is an open table format that supports schema evolution, partitioning, and multi-engine querying. It’s ideal for blockchain data because it helps normalize messy, high-volume inputs without sacrificing future flexibility. TRM Labs chose Iceberg over Delta Lake and Hudi due to better performance on read-heavy workloads and easier deployment across secure environments.
Normalize data across chains using Iceberg schemas. Partition by chain ID and time, bucket by wallet hashes, and use StarRocks joins to trace flows. Cross-chain investigations often require enrichment via bridge metadata or clustering techniques to link entities. TRM’s system does this with enriched schema layers and Iceberg-backed transformations.
TRM Labs uses a lakehouse architecture: Kafka and Spark for ingestion, Apache Iceberg on S3 for storage, and StarRocks as the execution engine. ETL is managed via PySpark and dbt; dashboards and alerting are handled through Superset and internal UI systems. This stack supports hundreds of real-time queries per minute and petabyte-scale workloads.
Yes, but ML requires clean input. You’ll need structured, labeled, enriched tables—ideally stored in Iceberg and processed via Spark or dbt. Common ML applications include anomaly detection, clustering wallet behaviors, or flagging risky flows. TRM uses ML sparingly and often prefers deterministic heuristics for auditability.
Start with one chain (e.g., Ethereum). Choose a specific question (e.g., wallet activity post-airdrop). Use public APIs or small datasets. Parse the data, load it into a structured format (DuckDB or SQLite), and write exploratory SQL. Once you need scale or real-time capability, move to Iceberg + StarRocks.
Popular use cases include:
Market trend dashboards (NFT or DeFi)
Portfolio attribution and user behavior analysis
Liquidity pool monitoring and APR prediction
Wash trading and MEV detection
Internal risk scoring and alerting systems
These use cases are increasingly embedded directly into consumer-facing crypto apps.
StarRocks is a high-performance analytical engine designed for sub-second, high-concurrency SQL workloads. TRM Labs adopted it after benchmarking against Trino and DuckDB and found that StarRocks delivered consistently lower latency—especially on large joins, filtered aggregations, and workloads requiring materialized views. It pairs well with Iceberg and supports production-grade real-time analytics.
Favor open standards (Iceberg, Parquet), scalable compute engines (StarRocks, Spark), and object storage (S3, GCS). Version everything, document schemas, monitor performance. Keep your pipelines modular so you can swap tools without rearchitecting. And most importantly: design for iteration, not permanence. Blockchain moves fast—your analytics should too.