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Ingestion and Transformation Engine

NextPipe

A pipeline engine for logs, events, files, APIs, CDC, enrichment, routing, and delivery.

NextPipe replaces brittle Logstash and Vector estates with a governed visual pipeline layer for ingestion, transformation, quality checks, routing, and observability.

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Positioned to replace

Logstash, Vector

NextPipe product interface

1600+

Connectors and source patterns

Rules

Transform, enrich, route, validate

Traceable

Pipeline observability and replay

What Makes It Different

A productized NextBrick operating layer built around real enterprise workflows, not a thin wrapper around one vendor.

Connector Framework

Ingest from SaaS, databases, APIs, files, logs, and message queues.

Visual Pipeline Rules

Transform fields, normalize schemas, enrich records, and route payloads without code sprawl.

Failure Handling

Dead-letter, replay, retry, and quality-check bad records without losing traceability.

Delivery Targets

Send clean data to search indexes, warehouses, streams, vector stores, and AI platforms.

ETL and Data Pipeline Battlecard 1.0

32x more events per second with 11x less CPU.

Kafka-native pipelines with NextDB and BigQuery sinks, tested on the same input and target tables.

Battlecard

Versus Logstash

32x more events/sec

11x lower CPU footprint

6.8x faster BigQuery sink

0% errors on the locked 5,000-event test

MetricNextPipe 1.0Logstash 9.3.3Delta
Throughput7,872 events/s246 events/s32x
CPU avg / p950.22 / 0.22 cores2.46 / 3.06 cores11x less
RAM avg / p950.18 / 0.18 GB0.38 / 0.64 GB2x less
BigQuery sink498 rows/s73 rows/s6.8x
BigQuery API errors0%Partial fails-
Failed inserts (NextDB sink)0 / 5,0000 / 5,000tie
Locked benchmark used the same 5K input and identical sink targets.
0.22-core footprint leaves CPU headroom where Logstash consumed multiple cores.
Drop-in sinks include NextDB, BigQuery, Snowflake, Elasticsearch, and Solr.
Backpressure-aware and retry-safe pipelines produced clean BigQuery commits.
NextPipe proof screen

Use Cases

  • Log ingestion
  • Search indexing
  • Data sync
  • API-to-warehouse pipelines

Why Teams Choose It

  • Make pipelines visible
  • Reduce fragile config files
  • Standardize ingestion
  • Recover from failures faster

Product Modules

The product is packaged into clear capabilities so teams can adopt incrementally and expand as the platform matures.

Plan rollout
Sources
Transforms
Rules
DLQ
Replay
Destinations