Queryable event streaming for blockchain data.
Powered by Continuum, Data Feeds is the simplest way to consume decoded, reorg-safe blockchain data - across 100+ chains, without running the infrastructure yourself.
Raw bytes to structured events
ABI-decoded, decimal-applied, USD-priced at event time. No bolt-on decoders. No off-chain enrichment pipeline.
100+ chains, one schema
EVM, Solana, Bitcoin, Hyperliquid, Tron, custom L1s and L2s into 18 unified topics. Same shape across every chain you index.
Atomic correction handling
Single resetPosition call rewinds every consumer. No DIY reconciliation. No silent corruption from out-of-sequence corrections.
Three problems general-purpose streaming tools cannot solve.
Blockchain data has a shape Kafka and its successors weren't designed for. Reorgs aren't edge cases. Cross-partition ordering matters. Decoded data needs a single canonical schema. Continuum solves all three at the storage layer.
Reorgs aren't edge cases
Blockchain history rewrites in real time. Standard streaming tools assume immutability. Every team hits this and writes its own reconciliation logic, duplicated across consumers, silently corrupting data when corrections arrive out of sequence.
Continuum's answer
Validates blocks against parent hashes before visibility. Atomic resetPosition at the storage layer rolls back, invalidates affected data, and propagates the correction to every downstream consumer automatically.
Cross-partition ordering
Partition-based brokers only guarantee ordering per partition, not globally. The natural unit of blockchain data is the block - across all transactions, traces, and events. Forcing partitioned ordering forces single-partition pipelines or breaks the chain.
Continuum's answer
Single globally-ordered streams with session-based parallelism. Multiple concurrent writers and readers operating against the same sequence - structurally not possible in Kafka's partition model.
Decoded data distribution
Each consumer team maintains duplicate ABI decoders and chain-specific parsing. Schema changes ripple through every pipeline. The team that publishes the data and the team that consumes it diverge. Adding a chain means rebuilding decoders everywhere.
Continuum's answer
Server-side conversion to Apache Arrow with pre-decoded, consistent schemas across EVM, Solana, Bitcoin, Hyperliquid, Tron, and custom chains. 18 normalized topic types. Stop maintaining decoder logic in every consumer.
Any protocol in. Any protocol out. Arrow in the middle.
The difference is where conversion happens. Regardless of ingestion protocol or input schema, Continuum converts server-side to Apache Arrow and stores as columnar Parquet on S3. Pre-decoded across every chain.
- Kafka
- AMQP / RabbitMQ
- Amazon SQS
- REST
- Arrow Flight
Protobuf · Avro · Apache Arrow · JSON
- Kafka consumers
- AMQP / RabbitMQ
- Amazon SQS
- REST / HTTP
- Arrow Flight (gRPC)
ClickHouse · DuckDB · Spark · Snowflake · BigQuery · Pandas · dbt
Data published via any protocol is immediately readable via all others. No ETL. No connectors. No secondary copies.
18 topic types. Identical across all chains.
EVM, Solana, Bitcoin, Hyperliquid, Tron, and custom chains - same field names, same guarantees. Stop building one decoder per chain.
- native_transfer
- token_transfer
- nft_transfer
- token_approval
- nft_approval
- token_swap
- aggregate_swap
- liquidity_change
- deployed_pair
- migrated_pair
- trade_transaction
- supply_change
- nft_supply_change
- locked_amount
- price_update
- block_data
- deployed_contract
- internal_transaction
100+ chains. One ingestion engine.
EVM, UTXO, account-model, and custom chains - same engine, same primitives, same Arrow schema on the way out. Add a chain via session API in days, not weeks.
10.6× faster than Envio. On their own benchmark.
Rocket Pool ERC-20 Transfer Events on Ethereum Mainnet, blocks 18.6M to latest. Standardized indexer benchmark used across the category.
Test methodology and replication scripts available on request
Honest comparison against the category.
Each of these has real strengths. Continuum's architectural choices land differently - particularly around storage ownership, reorg semantics, compression, and schema evolution.
Decentralized indexing pioneer
Their strengths
Largest SubGraph ecosystem, decentralized indexing, battle-tested since 2020.
Where Continuum lands differently
- SubGraph schema changes trigger full reindex (days at scale); Continuum stores raw positions with no RPC re-fetch
- PostgreSQL compresses 3-5×; Parquet achieves 17× compounding monthly
- SubGraph data only accessible through their query layer; Continuum partitions are standard Parquet files in your S3
- Reorg handling is handler concern in graph-node; Continuum's
resetPositionis atomic at storage layer
Managed SubGraph + Mirror
Their strengths
Managed SubGraph hosting, Mirror streaming pipelines with visual builder, responsive support.
Where Continuum lands differently
- Data lives on Goldsky (export job to leave); Continuum writes to your own S3 bucket
- Mirror reorg handling is best-effort eventual consistency;
resetPositionis atomic - Commodity S3 pricing after columnar compression is structurally cheaper at every volume
- Mirror doesn't expose raw Parquet; Continuum partitions are standard Arrow files
HyperSync EVM backfill
Their strengths
HyperSync fast EVM backfill, TypeScript handler model, publishes open-source benchmarks.
Where Continuum lands differently
- HyperSync is EVM-only; Continuum indexes EVM, Solana, Bitcoin, Hyperliquid, Tron, custom chains with same engine
- Envio is opinionated framework; Continuum is storage and streaming layer
- Envio schema changes require full RPC reindex; Continuum stores raw positions
- Reorgs in Envio require handler-level logic;
resetPositionis atomic at storage layer
TypeScript-native indexer
Their strengths
Outstanding developer experience, TypeScript with hot reload, type-safe schema, direct SQL access, minimal boilerplate.
Where Continuum lands differently
- Single Node.js process (instance ceiling); Continuum scales horizontally without downtime
- Postgres 3-5× compression vs Parquet 17× (permanent, compounding savings)
- Ponder is EVM-only; Continuum supports EVM, Solana, Bitcoin, Hyperliquid, Tron, custom chains
- Schema changes require full RPC reindex; Continuum adds derived topics from any historical position
Sync speed: 10-22× faster than peers.
Test: fetching 3.2M ERC20 transfer events on Ethereum (blocks 24,917,200 → 24,924,599 · Apr 19-21, 2026). All four providers, same query, same window.
Continuum sync time. Same dataset, same query, no warm cache.
10-22× faster than competitors
(400-800s range)
Methodology: cold-start sync of 3.2M ERC20 Transfer events, Ethereum blocks 24,917,200 → 24,924,599, single-region query node, Apr 19-21 2026. Replication scripts available on request.
Full capability comparison.
All dimensions side-by-side. Continuum delivers every capability competitors have - and several none of them do.
"Decoded" = ABI-decoded events. "Enriched" = decimals applied (raw uint256 → human-readable amount). "USD pricing" = price-at-event-time joined to the event. "Replay" = backfill from any historical block without re-fetching from RPC.
Speed vs data enrichment.
Where every provider sits on the two dimensions that matter most. Continuum is the only solution in the top-right: fast and fully enriched.
The top-right quadrant is empty except for Continuum. That's the position we want, and the position we have.
Drop-in Kafka. Same configs, different bytes.
If your stack already speaks Kafka, the migration is a hostname change. Same producer/consumer config. Same SASL/PLAIN auth. Same Schema Registry. Different storage backend, different correction semantics, different cost curve.
# consumer.properties
bootstrap.servers=broker-1.kafka:9092
security.protocol=SASL_SSL
sasl.mechanism=PLAIN
group.id=blockchain-pipeline
auto.offset.reset=earliest
key.deserializer=StringDeserializer
value.deserializer=ArrowDeserializer
# consumer.properties
bootstrap.servers=continuum.moralis.com:9092
security.protocol=SASL_SSL
sasl.mechanism=PLAIN
group.id=blockchain-pipeline
auto.offset.reset=earliest
key.deserializer=StringDeserializer
value.deserializer=ArrowDeserializer
Identical consumer config. Same SASL/PLAIN, same Schema Registry support, same consumer groups. Add Continuum as a parallel Kafka cluster, drain over weeks, deprecate the old broker fleet when ready.
Honest about scope.
Continuum is a streaming and storage substrate, not every solution. There are workloads where alternatives are better - and we'll tell you straight.
If you need stream topology processing, Continuum speaks Kafka protocol but doesn't run streaming topology or ksqlDB. Use Continuum for storage and access; run Kafka Streams or Flink alongside.
Continuum is at-least-once delivery, not exactly-once across producer-consumer-sink. If your workload is hard-dependent on exactly-once across the full chain, Kafka transactional producers may fit better.
If you want hosted SubGraphs without owning infrastructure, The Graph or Goldsky are better. Continuum writes to your own S3 bucket - you own the data and the operational surface.
S3-native model has different characteristics than in-memory brokers for HFT. If you need single-digit-ms end-to-end latency for trading workloads, an in-memory broker fleet may serve you better.
Memory and replay for autonomous machines.
Continuum is the same substrate, applied to AI agents, humanoid foundation labs, AV foundation models, and drone fleets. Persistent memory, episode replay, sensor recalibration, fleet-wide retraining - the autonomous-systems substrate.
Migrate from Kafka. Or start fresh.
A 15-minute walkthrough covers live reorg handling and atomic resetPosition, real-time protocol connection, migration timeline for your current stack, and storage and throughput modeling.