2 مستودعات
Distributed processing connectors that exchange data between databases and Spark clusters.
Distinct from Big Data Processing: Distinct from Big Data Processing: specifically focuses on the connector layer for Apache Spark rather than the general processing framework.
Explore 2 awesome GitHub repositories matching data & databases · Apache Spark Connectors. Refine with filters or upvote what's useful.
Nebula is a distributed graph database designed for storing and querying massive volumes of interconnected vertices and edges across a horizontally scalable cluster. It functions as a Kubernetes-native database and a distributed graph analytics engine, utilizing a Raft-based distributed store to ensure strong consistency and high availability. The system features an OpenCypher query engine for performing complex graph traversals and pattern matching. It distinguishes itself with a decoupled compute-storage architecture and a shared-nothing distributed design, allowing query processing and dat
Provides a distributed processing connector for exchanging data between the database and Apache Spark clusters.
Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer
Processes files and converts them into segment files for database ingestion using Spark.