Overview: Jitsu is an open-source data ingestion platform designed for modern data teams seeking full control over their event data pipelines. It serves as a self-hosted alternative to Segment, enabling organizations to collect user events from websites and apps and stream them in real time to data warehouses such as ClickHouse, BigQuery, Redshift, Snowflake, and PostgreSQL. Built with TypeScript and Go, Jitsu eliminates vendor lock-in while offering scriptable transformation logic and flexible destination configurations. It is ideal for engineering teams that need to own their data infrastructure without sacrificing ease of setup or real-time performance.
Jitsu 2.0 introduces a redesigned architecture based on Bulker, an open-source ingestion engine that handles high-volume event streaming with low latency. The platform supports both cloud-hosted and self-hosted deployments, making it suitable for startups experimenting with data pipelines as well as enterprises requiring compliance and customization. With native integrations, SDKs for multiple frameworks, and a proxy mode compatible with Segment’s API, Jitsu reduces migration friction while offering full transparency over data flow and transformation.
What You Get
- Real-time event ingestion - Collect user events from web apps, mobile apps, and server-side Node.js environments using HTML snippets, React SDKs, NPM packages, or direct HTTP API calls with sub-second latency.
- Multi-destination support - Stream events directly to ClickHouse, BigQuery, Snowflake, Redshift, and PostgreSQL with built-in connectors that handle schema mapping and batch optimization.
- Segment API proxy - Replace Segment without changing existing code; Jitsu accepts the same HTTP payloads and authentication, enabling seamless migration with zero client-side changes.
- Self-hosted deployment - Deploy Jitsu on your own infrastructure using Docker Compose or Kubernetes, with full control over data residency, security, and scaling.
- Scriptable transformations - Write JavaScript-based transformation rules to clean, enrich, or filter events before they reach your data warehouse, with live preview and validation in the UI.
- Bulker ingestion engine - Underlying open-source engine (https://github.com/jitsucom/bulker) that handles high-throughput data ingestion with built-in deduplication and retry logic.
Common Use Cases
- Building a privacy-first analytics stack - Companies subject to GDPR or CCPA use Jitsu to self-host event collection and avoid sending sensitive user data to third-party SaaS platforms like Segment.
- Migrating from Segment with minimal disruption - Engineering teams replace Segment by configuring Jitsu’s Segment Proxy API, keeping existing event tracking code intact while gaining cost savings and data ownership.
- Real-time analytics for high-volume SaaS apps - Platforms processing 10M+ monthly events use Jitsu to ingest user actions into ClickHouse for low-latency dashboards, reducing dependency on expensive cloud data tools.
- DevOps teams managing multi-cloud data pipelines - Teams deploying across AWS, GCP, and Azure use Jitsu’s unified ingestion layer to route events to region-specific data warehouses without maintaining multiple SDKs.
Under The Hood
Jitsu is a data ingestion and processing platform tailored for high-volume, real-time event streaming. It provides a modular architecture that enables flexible integration of various data sources and destinations, supporting both batch and streaming workflows with robust error handling and reprocessing capabilities.
Architecture
Jitsu adopts a modular, multi-language architecture that balances monorepo structure with microservices for scalable data processing.
- The system is organized into distinct services for ingestion, processing, and destination handling, ensuring clear separation of concerns
- A unified bulker system enables pluggable implementations across SQL, file, and cloud storage destinations
- Kubernetes-native deployment patterns support seamless orchestration and failover in cloud environments
Tech Stack
Jitsu is built as a polyglot monorepo leveraging TypeScript and Go, with modern frontend and backend frameworks.
- The platform uses React, Next.js, and Node.js for frontend and CLI components, alongside Go for core backend services
- Extensive use of libraries such as Zod, Lodash, and Ant Design enhances type safety and UI consistency
- Development tools include esbuild, Turbo, and pnpm for efficient builds and monorepo management
- Testing is handled through Jest, Vitest, and custom test containers for end-to-end validation
Code Quality
Code quality in Jitsu reflects a balance between strong testing practices and some inconsistencies in style and configuration.
- Comprehensive test coverage and automated validation strategies ensure reliability across modules
- Error handling is consistently implemented with widespread use of try/catch blocks and structured logging
- Type safety is enforced through TypeScript, contributing to maintainable and scalable code
- Some variation in configuration patterns and module organization introduces minor complexity
What Makes It Unique
Jitsu stands out through its innovative approach to event processing and flexible deployment models.
- The modular bulker architecture allows for unified batch and streaming support across diverse data destinations
- Built-in reprocessing and dead letter queue handling provide resilient error recovery without external dependencies
- Native Kubernetes integration enables seamless orchestration and runtime adjustments via configuration sources
- Support for multiple configuration methods (HTTP, Redis, Postgres) enables dynamic and runtime adaptable systems