11 repository-uri
Systems that orchestrate the movement and routing of data streams between diverse sources and sinks.
Distinct from Data Source Routing: None of the candidates describe general data pipeline routing; they focus on database query routing [f0_mt1], workflow branching [f0_mt2], or web URL routing [f0_mt4, f0_mt5].
Explore 11 awesome GitHub repositories matching data & databases · Data Integration Pipelines. Refine with filters or upvote what's useful.
DataX is a distributed data integration framework and plugin-based ETL tool designed for synchronizing large datasets between heterogeneous sources and destinations. It functions as a JDBC data migration engine and offline synchronization tool, enabling the movement of data between relational databases, NoSQL stores, and object storage. The system utilizes a plugin-based connector architecture that decouples reader and writer logic, allowing it to map and transform data types across different storage engines using a standardized internal representation. This design supports heterogeneous data
Orchestrates the movement and routing of data between object storage, graph databases, and analytical warehouses via a plugin architecture.
Azure Docs is the official technical documentation repository for Microsoft Azure, the cloud computing platform. It provides comprehensive guidance on the full spectrum of Azure services, covering everything from core infrastructure components like virtual machines, Kubernetes clusters, and serverless computing to platform services for AI, machine learning, data analytics, and storage. The documentation details how to provision, manage, and govern cloud resources at scale, including policy enforcement, identity management, and cost optimization. The documentation distinguishes Azure through i
Documents Azure Data Factory for orchestrating hybrid data pipelines between on-premises and cloud.
Benthos is a stream processing engine and data integration pipeline used for routing, transforming, and connecting data streams between diverse sources and sinks. It functions as event routing middleware and a change data capture tool, streaming real-time database modifications as discrete events for downstream processing. The system utilizes a declarative pipeline configuration, where data flow and processing logic are defined in a single static file. It features a specialized domain-specific language for mapping, filtering, and enriching data payloads, allowing for complex transformations w
The product enables moving data between various sources and sinks using a declarative pipeline configuration defined within a single file.
Connect is a Kafka data integration platform and stream processing engine used to build declarative pipelines that move and transform messages between Kafka topics and external sources. It functions as a Kafka Connect framework and a change data capture tool, streaming real-time database modifications to synchronize data across distributed environments. The project differentiates itself through a dedicated mapping language for mutating and reshaping message payloads and the ability to execute custom processing logic within a sandboxed WebAssembly runtime. It also provides an observability pip
Provides systems that orchestrate the movement and routing of data streams between diverse sources and sinks.
Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis
Uses in-memory lists as data sources or destinations for batch processing jobs within a larger pipeline.
This project is a streaming data integration framework that captures real-time database changes and synchronizes them with downstream systems. It operates as a distributed streaming ETL and database synchronizer, reading database logs and snapshots to propagate row-level modifications to target sinks. The system supports declarative data integration, allowing users to define source-to-sink data flows using SQL or YAML configurations. It distinguishes itself by automating schema evolution to maintain synchronization when source structures change and ensuring exactly-once delivery and processin
Provides a pipeline system for orchestrating the movement and routing of data streams between database sources and target sinks.
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
Transfers data from any source to any destination using a flow-based programming model with configurable processors.
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Normalizes data from multiple upstream providers into a consistent schema before publishing unified tables.
Briefer is an interactive data notebook platform and business intelligence dashboard tool used for collaborative data analysis and reporting. It provides a containerized environment for building reports that combine SQL, Python, and Markdown with native visualizations. The platform features an integrated code assistant that uses large language models to generate SQL and Python snippets from natural language prompts. It is designed as a Kubernetes data application, deploying via Helm charts to manage isolated compute environments and ensure separate resources per page through pod-based isolati
Facilitates the construction of ad-hoc data workflows that move and transform data between different sources.
Console is a web-based management tool for monitoring and administering Kafka clusters. It provides a graphical interface for managing topics, consumer groups, and real-time data flows. The project includes dedicated managers for Kafka Connect and the Schema Registry, allowing for the deployment of data connectors and the governance of data schemas. It features a cluster dashboard for tracking broker health, storage usage, and rack status, alongside an interface for role-based access control and audit log inspection. The platform covers data browsing and filtering, consumer group coordinatio
Provides tools to construct and route data pipelines between disparate systems using Kafka connectors.
RavenDB is a multi-model NoSQL document database designed for high-performance, ACID-compliant data storage. It persists structured information as schema-flexible JSON documents and utilizes a unit-of-work session pattern to track entity changes and batch modifications into atomic transactions. The platform is built on a distributed architecture that supports horizontal scaling through sharding and ensures high availability via multi-node, master-to-master cluster replication. The database distinguishes itself through a self-optimizing query engine that automatically creates and maintains ind
Orchestrates data movement and transformation between the database and external systems like SQL and search engines.