Stream Live Chain Data into Your Analytics Stack with Amp

Reliable analytics starts with accurate, queryable blockchain event data. But contract events break the moment reorgs happen.

Most teams end up building custom indexers and ingestion pipelines to transform logs into SQL tables. That means handling reorgs, managing schemas, running backfills, and maintaining infrastructure.

Amp provides a structured pipeline for smart contract events. When a contract emits events, Amp indexes them, reconciles reorgs, and exposes the results as SQL tables. The data stays consistent without custom parsers, reorg logic, or ingestion pipelines. You write contracts and queries. Amp maintains the data.

Real-Time User Engagement Metrics on a DEX

Analysts often ask questions like: where do our new users come from? Which liquidity pools are growing fastest? How many unique addresses interacted with token X this week vs last? These aren't hard questions, but getting reliable answers requires a pipeline most teams don't have:

• Real-time ingestion
• Guaranteed correctness through reorgs
• Queryable SQL tables with semantic meaning (events, entities)
• A pipeline where engineers don't write ABI parsers for every contract

Why Traditional Pipelines Fail

Polling an RPC means writing code to fetch blocks, decode logs using local ABIs, and manage shards. But devs spend more time fixing parsers than building BI, reorgs cause duplicates and missed reversions, and every new contract means fresh parsing work.

Indexing everything into raw tables means analysts write complex SQL for every metric: hard to maintain, slow queries, and no schema guarantees. Analysts end up reverse-engineering the data instead of using it.

Amp is the Solution

Teams choose Amp for three big reasons:

• Automatic event → SQL table conversion: Amp introspects ABIs and auto-generates table schemas via eventTables(abi)
• Reorg handling built-in: Amp's reorg awareness lets you ingest data while still trusting the results
• Derived tables for transformations: push transformations inside Amp, not in messy Airflow tasks

Reorg Handling Out of the Box

Blockchains sometimes undergo chain reorganizations, where recently confirmed blocks are replaced by a different canonical chain. With batch pipelines, you have to manually track and reconcile reorgs, pause ingestion, and run reconciliation jobs. This increases operational complexity and risks double-counting or missing events.

Amp handles chain reorgs automatically through block range organization and revision-based execution. It organizes data into immutable Parquet files and serves queries from consistent revisions. Reorgs are processed in parallel, and a single metadata update automatically switches the active view when the corrected version is ready. This delivers continuous ingestion, consistent queries, and built-in reorg correctness without operational overhead.

Results: What Teams Can Expect

• Time to get fresh data: 24+ hours before Amp → under 3 minutes after Amp
• Reorg-related dashboard errors: frequent before Amp → zero after Amp
• Engineering hours spent on pipeline: high before Amp → minimal after Amp
• Trust in data correctness: low before Amp → high after Amp

Analysts spend their time on insights, not debugging parsers.

Conclusion

Amp turns what used to be a messy, brittle, and slow ETL project into a robust, low-maintenance, real-time analytics pipeline: automatic event → SQL tables, built-in reorg correctness, derived tables for streaming-compatible views, content-addressable dataset versioning, and dual query APIs.

If your next product needs live blockchain analytics you can trust, Amp is worth a close look.