Open-source alternatives to Bruin

Hadoop Data Integration with various databases, ftp servers, salesforce. Incremental update, dedup, append, merge your data on Hadoop.

A probabilistic data structure service and storage

DEPRECATED: Data collection and processing made easy.

Secor is a service implementing Kafka log persistence

A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management for both streaming and batch data ecosystems.

Apache Pulsar is a cloud-native distributed pub-sub messaging system designed for high-performance data ingestion. It functions as a geo-replicated data streamer and a multi-tenant event streaming platform, providing a serverless stream processing engine and a tiered storage messaging broker. The system distinguishes itself by separating serving layers from storage layers to allow independent scaling of compute and data retention. It features native geo-replication to synchronize messages across different geographical regions and employs a multi-layered tenant isolation model using authentica

Dagster is a data orchestration platform designed to manage the entire lifecycle of data assets through declarative modeling and version-controlled code. It functions as a workflow engine that treats data assets as first-class primitives, allowing teams to define, schedule, and monitor complex pipelines while maintaining clear visibility into lineage, dependencies, and data quality. The platform distinguishes itself by using a code-as-configuration framework that enables standard software engineering practices, such as unit testing and local mocking, to be applied directly to data workflows.

Airflow is a platform for programmatically authoring, scheduling, and monitoring complex data pipelines. It functions as a workflow automation engine that manages the lifecycle of recurring business processes by executing code-defined task dependencies. By representing workflows as directed acyclic graphs, the system ensures that task execution order and data flow are explicitly defined and reliably maintained across distributed computing environments. The platform distinguishes itself through a highly modular, provider-based architecture that decouples core orchestration logic from external

🦎 A multi-protocol edge & service proxy. Seamlessly interface web apps, IoT clients, & microservices to Apache Kafka® via declaratively defined, stateless APIs.

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

Hadoop log aggregator and dashboard

Scribe is a distributed log aggregation system designed to collect and route real-time log data from numerous servers to centralized storage or analysis tools. It functions as a log data pipeline and scalable collector that gathers streaming data and writes it to local disks or remote endpoints. The system employs a log routing server model that organizes incoming streams into specific buckets based on predefined configuration mappings. It supports multi-hop log forwarding, allowing data to be routed through a chain of intermediate servers to centralize logs from diverse network segments. Re

DocId set compression and set operation library

Iggy is a distributed message streaming platform and multi-protocol message broker that functions as a persistent distributed log store. It provides infrastructure for publishing and consuming binary messages using an append-only log, ensuring high availability and data consistency across nodes through Viewstamped Replication. The platform is distinguished by its specialized LLM streaming infrastructure, which uses a server protocol to connect large language models to streaming data and system controls. This includes standardized protocols for context management and data bridging via HTTP or

Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

Mirror of Apache Oozie

Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded batch data and unbounded real-time streams. It provides a system for building scalable, data-parallel workflows that operate across compute clusters using a single programming model. The framework utilizes a cross-runner pipeline abstraction that decouples the data processing logic from the underlying execution backend, allowing the same pipeline to run on different distributed compute engines. It supports multi-language pipeline development by translating high-level code fro

Cocoindex is an incremental data processing engine that builds and maintains live indexes for AI agents, with a core focus on codebase indexing and knowledge graph extraction. The engine uses a function-graph execution model where user-defined Python functions are composed into a directed acyclic graph, and it processes data incrementally so only changed source records or code paths are re-computed, avoiding full recomputation at any scale. It supports automatic schema inference from transformation pipeline type annotations and provides full data lineage tracing, tagging every output record wi

Python task queue using Redis

Apache NiFi is a flow-based programming platform that enables the visual design, monitoring, and management of data pipelines. At its core, it provides a web-based visual dataflow designer where users build directed graphs of processors to route, transform, and mediate data movement between any source and destination without writing custom code. The system records fine-grained data provenance for every data item from ingestion to delivery, supporting audit, debugging, and replay of data lineage. The platform distinguishes itself through a zero-master cluster architecture that distributes proc

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Celery is an asynchronous job processor and distributed task queue designed to offload time-consuming operations to background worker nodes. By utilizing a message-passing architecture, it decouples task producers from consumers, allowing applications to maintain responsiveness while scaling workloads across multiple isolated environments. The system functions as a distributed workload orchestrator that manages the lifecycle of deferred operations through persistent queues. It distinguishes itself by providing a pluggable transport abstraction, which allows the core task logic to remain indep

This repository demonstrates a comprehensive CI/CD pipeline for dbt projects using GitHub Actions. The pipeline provides safe, efficient, and isolated testing of dbt changes while maintaining production data integrity.