Search Results for "jpk data processing"
Sort By:
Showing 23 open source projects for "jpk data processing"
View related business solutions-
Build on Google Cloud with $300 in Free CreditStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query exabytes in BigQuery, or build AI apps with Vertex AI and Gemini. Once your credits are used, keep building with 20+ products with free monthly usage, including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. Sign up to start building right away.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
HPCC Systems
End-to-end big data in a massively scalable supercomputing platform.
HPCC Systems® (www.hpccsystems.com) from LexisNexis® Risk Solutions is a proven, open source solution for Big Data insights that can be implemented by businesses of all sizes. With HPCC Systems, developers can design applications with Big Data at their core, enabling businesses to better analyze and understand data at scale, improving business time to results and decisions. HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. -
2
CloudI is an open-source private cloud computing framework for efficient, secure, and internal data processing. CloudI provides scaling for previously unscalable source code with efficient fault-tolerant execution of ATS, C/C++, Erlang/Elixir, Go, Haskell, Java, JavaScript/node.js, OCaml, Perl, PHP, Python, Ruby, or Rust services. The bare essentials for efficient fault-tolerant processing on a cloud!
-
3
dispy
Distributed and Parallel Computing with/for Python.
dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently. dispy supports public / private / hybrid cloud computing, fog / edge computing. -
4
Open Event Machine
An event driven processing runtime for multicore
Deprecated - see new repository in GitHub instead: https://github.com/openeventmachine/em-odp Open Event Machine (OpenEM or EM) is an architectural abstraction and framework of an event driven multicore optimized processing concept originally developed for networking. It offers an easy programming concept for scalable and dynamically load balanced multicore data plane applications with a very low overhead run-to-completion principle. EM can run on bare metal for best performance or under an operating system with special arrangements. This release of the Open Event Machine contains the EM API as well as an example implementation for Intel multicore CPUs. !!! ... -
99.99% Uptime for MySQL and PostgreSQL on Google CloudCloud SQL Enterprise Plus gives you a 99.99% availability SLA with near-zero downtime maintenance—typically under 10 seconds. Get 2x better read/write performance, intelligent data caching, and 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server with built-in vector search for gen AI apps. New customers get $300 in free credit.
-
5
Vappio is a framework for building virtual appliances that supports distributed data processing in cloud computing environments using Sun Grid Engine or Hadoop. The primary target application of Vappio is bioinformatics.
-
6
TeleScope
XML Data Stream Broker/Replicator
TeleScope is the efficient intensive-load XML data stream broker, replicator and simple event processing platform (SEP) written in C for the Fedora 17-18, Slackware 13-14, Red Hat Enterprise Linux 6 (RHEL-6) Linux distributions. The platform is intended to be operated upon the single number/word values and is not meant to be deployed for full-text XML stream analysis. TeleScope has internal query language with a set of standard logical operators that allows to construct relatively complex query expressions. ... -
7
Gpf4Med
Graph Processing Framework for Medical Information
Gpf4Med is a graph processing framework for clinical data, which can leverage from previously existing medical database and Cloud computing facilities to address large-scale studies. -
8
FlowVR is an open source middleware tailored for high performance in situ data processing and analytics running on large parallel machines
-
9
SECTOR: A Distributed Data Storage and Processing Platform
-
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
-
10
Syoncloud
Hadoop, Hbase, HBase Web Client, Flume based log analytics system
Syoncloud Logs enables you to process log files from various applications using Hadoop, Flume and HBase. It has an easy installation and configurations interface. It has Syoncloud HBase web client. It displays tree of HBase tables and column families linked to paginated grid of data. -
11
distributedPHP client
A simple script for distributed computing through PHP:
distributedPHP client is a simple PHP script that can simultaneously activate/send data to as many web scripts as you want. You must open and configure the distributedPHP .php file prior to running it. ditributedPHP client supports activating scripts without data, sending the same data to all scripts, sending unique data to each script or sending user input to each script. Examples of use include: distributed math computation, encryption breaking, SETI@home/folding@home (well, if they made the projects in php..) distributed bruteforce attacks, ddos attacks, distributed processing, etc.. ... -
12
Microbase is a Grid infrastructure for deploying massively parallel applications. Our notification-driven task scheduler supports the enactment of complex data processing workflows, such as those commonly used in bioinformatics.
-
13
Meerkat is a distributed programming environment. It consists of a virtual machine which is suited to parallel processing. The data model is based on the concept of actors, although it is much more permissive than the traditional description.
-
14
The Simple, Scalable, Script-based Science Processor for Measurements (S4PM) is a system for highly automated processing of science data, scalable up to large processing systems and scalable down to small, special-purpose processing strings.
-
15
This project aims to create a method able to determine the most frequent word phrases in a large source of text data (>5 Gb) using the computational power of multiple processors.
-
16
Open Source Remote Sensing OSSIM project, pronounced "awesome", will leverage existing algorithms/tools/packages from the open source community in construction of the ultimate Remote Sensing/Image Processing/GIS package. osgPlanet extends OSSIM and Op
-
17
-
18
Collection of Java tools and packages related to server-side (web application-oriented) XML processing, such as Adaptive XML Inclusions system with demo. Tools for personalization of XML content, Java-XML data binding and others will come later.
-
19
-
20
The DataTime Process Framework is intended to support the processing of time-based data in a modular, concurrent, distributed and extensible manner. C++, using YARP, ACE, Qt and MUSCLE on Linux, OSX, Windows and Solaris.
-
21
Allows creation of a distributed data processing system. Provided is a pluggable Daemon and a framework for writing custom plugins. Plugins can be changed and configured remotely and on the fly. Configurations with example plugins are provided.
-
22
StreamMine is a distributed event processing (streaming) infrastructure. You can create low-latency, fault-tolerant stream processing functionality with any stream-oriented operators that can be implemented in Python.
-
23
An implementation of a high performance distributed storage and processing system for large amounts of structured data.
- Previous
- You're on page 1
- Next
