Search Results for "flink"
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Showing 20 open source projects for "flink"
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Apache Flink
Stream processing framework with powerful stream
Apache Flink is a distributed engine for stateful computations over data streams and batches, designed for low-latency processing at scale. Its core runtime executes dataflow graphs with fine-grained backpressure and checkpointing, allowing applications to recover consistently from failures. Flink’s event-time model and watermarks enable accurate out-of-order processing, windowing, and complex time semantics that typical real-time systems struggle with. -
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Flink CDC
Flink CDC is a streaming data integration tool
Apache Flink CDC is a distributed data integration tool that captures data changes in real-time from various databases. It leverages Change Data Capture (CDC) technology to stream data changes into Apache Flink, enabling real-time analytics and data processing. Flink CDC simplifies data pipeline development with its declarative YAML configurations. -
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CDC Connectors for Apache Flink
CDC Connectors for Apache Flink
This project provides a set of source connectors for Apache Flink® directly ingesting changes coming from different databases using Change Data Capture(CDC). CDC Connectors for Apache Flink® is a set of source connectors for Apache Flink®, ingesting changes from different databases using change data capture (CDC). CDC Connectors for Apache Flink® integrates Debezium as the engine to capture data changes. -
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Apache Iceberg
Apache Iceberg
Iceberg is a high-performance format for huge analytic tables. Iceberg brings the reliability and simplicity of SQL tables to big data while making it possible for engines like Spark, Trino, Flink, Presto, Hive, and Impala to safely work with the same tables, at the same time. The core Java library that tracks table snapshots and metadata is complete, but still evolving. Current work is focused on adding row-level deletes and upserts, and integration work with new engines like Flink and Hive. The Iceberg format specification is being actively updated and is open for comment. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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XGBoost
Scalable and Flexible Gradient Boosting
...It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems. XGBoost can be used for Python, Java, Scala, R, C++ and more. It can run on a single machine, Hadoop, Spark, Dask, Flink and most other distributed environments, and is capable of solving problems beyond billions of examples. -
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Bytewax
Python Stream Processing
Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries. Bytewax is a Python framework and Rust distributed processing engine that uses a dataflow computational model to provide parallelizable stream processing and event processing capabilities similar to Flink, Spark, and Kafka Streams. ... -
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IoTDB
Apache IoTDB
Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Due to its light-weight architecture, high performance and rich feature set together with its deep integration with Apache Hadoop, Spark and Flink, Apache IoTDB can meet the requirements of massive data storage, high-speed data ingestion and complex data analysis in the IoT industrial fields. In the scene of factories, there are tens of devices under LAN network. IoTDB can be installed on a local controller server in the factory to receive data from those devices. The local controller server (normal PC or workstation) with IoTDB can provide the ability to persist data and query data with SQL-like interface. ... -
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Apache Polaris
Apache Polaris, the interoperable, open source catalog
...By implementing the Iceberg REST catalog API, Polaris enables distributed data platforms to access shared table metadata without tightly coupling storage systems and query engines. This design allows organizations to run queries on the same Iceberg tables using tools such as Apache Spark, Flink, Trino, and other analytics engines while maintaining consistency across platforms. Polaris also focuses on data governance, security, and interoperability within large-scale cloud data architectures. Because Iceberg tables often exist across many services in a distributed ecosystem, the catalog helps coordinate metadata, schemas, and access policies in a unified system. -
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Apache Beam
Unified programming model for Batch and Streaming
Apache Beam is an open source, unified programming model to define both batch and streaming data-parallel processing pipelines, as well as certain language-specific SDKs for constructing pipelines and Runners. These pipelines are executed on one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Beam is especially useful for Embarrassingly Parallel data processing tasks, and caters to the different needs and backgrounds of end users, SDK writers and runner writers. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference... -
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Dolphin Scheduler
A distributed and extensible workflow scheduler platform
...All process definition operations are visualized, Visualization process defines key information at a glance, One-click deployment. Support multi-tenant. Support many task types e.g., spark,flink,hive, mr, shell, python, sub_process. Support custom task types, Distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. -
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Apache Sedona
Cluster computing framework for processing large-scale geospatial data
Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive query workloads. ... -
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Speedb
A RocksDB compliant high performance scalable embedded key-value store
...Speedb is a compatible alternative for LevelDB and RocksDB users who would like to take their application to the next level. When using event streaming platforms like Kafka, Flink, Spark, Splunk, Elastic, or others, consider using Speedb to enhance its performance. The increase in metadata in modern data sets is causing significant performance issues for many applications. With Speedb you can keep costs low and ensure your applications continue to run smoothly even under heavy loads. When it comes to making a choice to upgrade or deploy a new key-value store with your platform, Speedb is up for the challenge. ... -
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Alink
Alink is the Machine Learning algorithm platform based on Flink
Alink is Alibaba’s scalable machine learning algorithm platform built on Apache Flink, designed for batch and stream data processing. It provides a wide variety of ready-to-use ML algorithms for tasks like classification, regression, clustering, recommendation, and more. Written in Java and Scala, Alink is suitable for enterprise-grade big data applications where performance and scalability are crucial. It supports model training, evaluation, and deployment in real-time environments and integrates seamlessly into Alibaba’s cloud ecosystem. -
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SRM
C library for the development of Linux OpenGL DRM/KMS apps
SRM is a C library that simplifies the development of Linux DRM/KMS API applications. With SRM, you can focus on the OpenGL ES 2.0 logic of your application. For each available display, you can start a rendering thread that triggers common events like initializeGL(), paintGL(), resizeGL(), pageFlipped() and uninitializeGL(). SRM allows you to use multiple GPUs simultaneously and automatically finds the most efficient configuration. It also offers functions for creating OpenGL textures,... -
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Amazon Kinesis Flink Connectors
Contains various Apache Flink connectors to connect to AWS data
This library contains various Apache Flink connectors to connect to AWS data sources and sinks. This repository contains various Apache Flink connectors to connect to AWS Kinesis data sources and sinks. Flink maintain backwards compatibility for the Sink interface used by the Firehose Producer. This project is compatible with Flink 1.x, there is no guarantee it will support Flink 2.x should it release in the future. -
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nussknacker
Real-time actions on data. A building block for low-code applications
Real-time actions on data. A building block for low-code real-time applications with event streaming and request-response capabilities. Nussknacker is a low-code visual tool for domain experts to define and run real-time decisioning algorithms instead of implementing them in the code. It serves where real-time actions on data have to be made: real-time marketing, fraud detection, Internet of Things, Customer 360, and Machine Learning inferring. An essential part of Nussknacker is a... -
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SZT-bigdata
SZT‑bigdata is an open source project
SZT‑bigdata is an open-source project analyzing real Shenzhen metro (subway) card usage data using big‑data frameworks like Spark, Hadoop, Hive, Kafka, Flink, ClickHouse, HBase, and Elasticsearch. Aimed at exploring transit passenger flow patterns and system optimization using a variety of Scala-based technologies. -
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ChunJun
A data integration framework
ChunJun is a distributed integration framework, and currently is based on Apache Flink. It was initially known as FlinkX and renamed ChunJun on February 22, 2022. It can realize data synchronization and calculation between various heterogeneous data sources. ChunJun has been deployed and running stably in thousands of companies so far. Based on the real-time computing engine--Flink, and supports JSON template and SQL script configuration tasks. -
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Flink is a email monitor applet for the gnome panel. It supports multiple accounts (infinite), of mbox,pop3 and imapv4 types. But more is to be supported. It also supports sound, themes, program invokation and more.
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