Building a crypto market data pipeline is mostly an engineering problem, and the design depends heavily on whether you need real-time or historical/batch data. Here's the mental model I'd start with.
1. Decide the data sources first
Crypto data comes from three fundamentally different layers, and you usually need more than one:
- Centralized exchanges (CEX) — Binance, Coinbase, etc. Best source for order-book depth, trades, funding rates, and anything related to trading activity. REST for snapshots, WebSocket for streaming.
- On-chain (raw blockchain) — the source of truth for wallet balances, transfers, token contract events. You don't "subscribe" to this; you index it. Either run your own node, or use a provider (Alchemy, QuickNode, Infura) plus an indexer (The Graph, Dune, DefiLlama's APIs).
- Aggregators — CoinGecko, CoinMarketCap, DexScreener, DefiLlama. These already curate and normalize data across many sources, so they're the fastest way to get market cap, historical OHLCV, and protocol TVL without building every integration yourself.
A common trap: people try to get everything from one CEX feed and then are surprised the "price" differs from what a DEX shows. Aggregating multiple sources and reconciling them is the actual hard part, not fetching.
2. Architecture layers
A typical pipeline looks like this:
- Ingestion — collectors that poll REST endpoints and maintain persistent WebSocket connections. Each exchange has its own message format, rate limits, and auth, so this is where a lot of engineering time goes.
- Validation & normalization — strip the noise, convert to a unified schema (you will need a canonical
(exchange, symbol, ts) key, since the same asset trades as BTCUSDT, BTC/USD, BTC-USD depending on venue). Drop obviously-bad ticks (zero/negative prices, timestamps in the future).
- Storage — the choice is driven by query pattern:
- Time-series DB (TimescaleDB, InfluxDB, ClickHouse) for candles, trades, and anything you query by time range.
- Object store + columnar files (S3 + Parquet) for cold historical archives you might want for backtesting.
- Streaming layer (Kafka/Redis) if downstream consumers need live events rather than polling a database.
- Serving/API — a read API so downstream apps (dashboards, trading bots, your own UI) never touch the raw ingestion path.
3. The two hardest problems
- Latency vs. completeness trade-off. A WebSocket trade feed is near-instant but lossy if your consumer crashes — you miss the ticks during downtime. Reliable systems replay from REST or a database after a disconnect to fill gaps. Decide what "correct enough" means for your use case: a dashboard can tolerate a few missing ticks; a trading strategy usually can't.
- Data integrity / reconciliation. Cross-venue arbitrage signals only work if the data is clean. It's worth having a periodic reconciliation job that compares your stored candles against an authoritative source and flags drift.
4. Don't overbuild for v1
For a first version, a solid pattern is: pull OHLCV + market cap from an aggregator API on a scheduled basis → store in a time-series DB → expose a small read API. That gets you a working, queryable pipeline in days. Add streaming WebSocket ingestion only when you actually have a consumer that needs sub-second data — which is usually trading bots, not analytics dashboards.
A couple of practical notes given how this app is set up: if you're building around specific market data for tokens you care about, I can pull live figures for anything — price, market cap, 24h volume, trending lists, even on-chain liquidity-lock and security status for a token contract. If you tell me what data you're trying to collect (e.g. "live prices for these 20 tokens"), I can fetch the current snapshot for you while you work on the plumbing.