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Jitsu is an open-source customer data platform designed to orchestrate event data pipelines. It captures, transforms, and routes behavioral data from web and server sources into data warehouses and analytics tools, providing a unified infrastructure for managing event streams. The platform distinguishes itself through its focus on self-hosted, containerized operations that grant users full control over their data security and privacy. It features a robust identity resolution engine that stitches disparate user identifiers into persistent profiles across sessions and devices, alongside program
Dag-factory is a framework for constructing and managing Apache Airflow data pipelines through declarative configuration files. By replacing manual procedural code with structured YAML definitions, it enables the programmatic generation of complex workflow structures, task dependencies, and execution schedules. The project distinguishes itself by mapping configuration keys directly to Python class constructors and operators, allowing for the dynamic instantiation of objects and custom logic. It supports hierarchical configuration inheritance to standardize settings across environments and pro
Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve
Arroyo is a high-performance stream processing platform built in Rust. It executes continuous SQL queries on streaming data with event-time semantics, enabling accurate windowed aggregations, joins, and stateful computations on unbounded event streams. The platform uses native Rust execution for high throughput and low latency, with periodic checkpointing for exactly-once fault tolerance and horizontal scaling across distributed workers. The system integrates deeply with Kafka for reading and writing topics with exactly-once delivery and supports change data capture (CDC) from MySQL and Postg
The database purpose-built for stream processing applications.
The main features of confluentinc/ksql are: Data Pipelines and Orchestration, Streaming SQL.
Projects with overlapping indexed features include: astronomer/dag-factory — Dag-factory is a framework for constructing and managing Apache Airflow data pipelines through declarative… jitsucom/jitsu — Jitsu is an open-source customer data platform designed to orchestrate event data pipelines. It captures, transforms,… apache/flink — Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite… arroyosystems/arroyo — Arroyo is a high-performance stream processing platform built in Rust. It executes continuous SQL queries on streaming… dagster-io/dagster — Dagster is a data orchestration platform designed to manage the entire lifecycle of data assets through declarative… alluxio/alluxio — Alluxio is a virtual distributed file system and data orchestration layer that serves as a high-performance caching…