Blockchain Data Streaming Explained: Real-Time Onchain Feeds | Quicknode
//Answers>Learn about blockchain data streaming>What is blockchain data streaming?
What is blockchain data streaming?
// Tags
blockchain data streamingreal-time blockchain data
TL;DR: Blockchain data streaming is a push-based approach to accessing blockchain data where new blocks, transactions, and events are delivered to your application or database automatically as they are produced onchain. Instead of your application repeatedly asking the node "anything new yet?" (polling), a streaming service sends data to you the moment it is available. Streaming reduces latency, eliminates missed events, simplifies your backend architecture, and scales far more efficiently than traditional request-response patterns.
The Simple Explanation
There are two fundamental ways to get data from any system: you can ask for it (pull), or it can be sent to you (push). For most of blockchain's history, developers have used the pull model. Your application sends an RPC request to a node, the node sends back a response, and your application decides when to ask again. This is the polling pattern. It is simple to implement but inefficient, especially when your application needs to stay in sync with the blockchain's latest state.
Streaming flips this model. Instead of your application asking "what happened in the latest block?" thousands of times per day, you establish a data pipeline once and the streaming service delivers every new block's data to your application or database as it is produced. Your application becomes a consumer of a continuous data feed rather than a requester of discrete data snapshots.
The analogy is the difference between refreshing a news website every five minutes versus subscribing to a push notification that alerts you the moment a story breaks. Both get you the same information eventually, but the push model is faster, less wasteful, and more reliable.
How Blockchain Data Streaming Works
A streaming service sits between blockchain nodes and your application or data infrastructure. On the ingestion side, the service connects to nodes across supported networks and processes each new block as it is finalized. It extracts the raw block data, including headers, transactions, transaction receipts (with event logs), and optionally execution traces. This raw data is the complete record of everything that happened in that block.
On the processing side, the streaming service applies any filters or transformations you have configured. Perhaps you only care about ERC-20 transfer events from a specific set of contracts. A filter written in JavaScript or another supported language runs against each block's data and passes through only the records that match your criteria. This server-side filtering is critical for efficiency because it means your destination only receives and stores the data you actually need, not the entire block.
On the delivery side, the filtered data is sent to your configured destination. This could be a webhook URL that your application listens on, a PostgreSQL database, a Snowflake data warehouse, an Amazon S3 bucket, Azure Blob Storage, or another supported endpoint. The streaming service handles delivery guarantees, including retries on failure, deduplication, and correct ordering, so your destination receives a gapless, sequential feed of blockchain data.
Streaming vs Traditional ETL Approaches
Traditional blockchain ETL (Extract, Transform, Load) pipelines require developers to build and maintain significant infrastructure. A typical custom ETL setup involves a polling worker that calls RPC endpoints on a schedule to fetch new blocks, a processing layer that decodes and transforms the raw data, a database writer that inserts records, an error handler that manages retries and failures, a reorg detector that identifies and rolls back data from abandoned forks, and monitoring to alert when any of these components break. Each piece adds complexity, and each is a potential point of failure.
Streaming services consolidate all of this into a single managed pipeline. The extraction, filtering, transformation, delivery, retry logic, ordering, and reorg handling all happen on the provider's infrastructure. Your responsibility is limited to configuring what data you want, where you want it sent, and how you want it shaped. This dramatically reduces the engineering effort required to build and maintain blockchain data infrastructure.
The performance difference is also substantial. A polling-based pipeline inherently has latency equal to the polling interval. If your worker checks for new blocks every 5 seconds, you could be up to 5 seconds behind the chain tip even when everything is working perfectly. Missed polls (due to errors, rate limits, or worker restarts) increase this gap. Streaming eliminates polling latency because data is pushed as soon as it is available. On high-throughput chains where blocks are produced every few hundred milliseconds, this difference is meaningful for latency-sensitive applications.
Key Features of Production Streaming
Several features distinguish production-grade streaming from a simple WebSocket subscription. Guaranteed delivery ensures that every block's data reaches your destination exactly once, even if the connection drops temporarily or your destination experiences a brief outage. The streaming service buffers and retries until delivery is confirmed, so you never have gaps in your data.
Finality-order delivery means data arrives in the order the blockchain considers canonical. On chains with variable finality times, the streaming service waits until a block is sufficiently confirmed before delivering it, preventing your application from processing data that might later be invalidated by a reorg. When reorgs do occur, the service detects the fork, identifies which blocks are no longer canonical, and sends correction payloads so your destination can update accordingly.
Historical backfilling allows you to use the same streaming pipeline for past data, not just new blocks. Instead of building a separate ETL process for historical data and a different one for real-time data, you configure a single stream with a starting block in the past and let it work forward through history, then seamlessly transition to real-time once it catches up to the chain tip. This unified pipeline simplifies your architecture and ensures consistency between historical and real-time data.
Server-side filtering and transformation reduce the volume of data that reaches your destination, lowering bandwidth costs, storage costs, and processing overhead. Filters can match on addresses, event signatures, function selectors, transaction values, or any other property of the block data. Transformations can reshape payloads, decode ABI-encoded data, compute derived values, and output records in a schema that matches your database tables.
How Quicknode Streams Delivers Blockchain Data Streaming
Quicknode Streams is a purpose-built blockchain data streaming and ETL service that delivers real-time and historical data across 80+ chains with built-in filtering, transformation, and guaranteed delivery. Streams supports multiple dataset types (blocks, transactions, receipts, event logs, traces) and multiple destinations (webhooks, PostgreSQL, Snowflake, Amazon S3, Azure Storage, and more).
Streams processes data in finality order with exactly-once delivery guarantees, automatically handles chain reorganizations by sending correction payloads, and supports configurable batching and compression for optimal throughput during historical backfills. JavaScript filters run on Quicknode's infrastructure, allowing you to decode events, match patterns, reference external key-value stores, and shape output payloads before data reaches your destination. One-click backfill templates provide pre-configured pipelines for common datasets across 20+ chains, with transparent cost and completion time estimates shown before you start.
For even more sophisticated workflows, Streams integrates with Quicknode Functions to enable serverless automation on top of streaming data. Functions can enrich records with additional onchain data, call external APIs, trigger notifications, or execute arbitrary business logic in response to streaming events. Together, Streams and Functions provide a complete blockchain data platform that replaces custom ETL infrastructure with a managed, scalable solution.
