A curated list of awesome big data frameworks, ressources and other awesomeness.
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Updated
Aug 25, 2018
A curated list of awesome big data frameworks, ressources and other awesomeness.
A service that aggregates amateur radio data worldwide in real time for public use
Azure Stream Analytics processes ATM transaction data streams, employing Event Hub, Storage, and Stream Analytics Job. Queries include total amounts and alerts. The setup and query execution process are documented with screenshots.
Finding missing k numbers in data stream using symm functions
A generator for synthetic, multivariate & heterogeneous datasteams with probabilistically repeating patterns.
Process data flow from different sources and destinations using DBI classes
An small android module to display a data stream in realtime
Algorithms proposed in the following master dissertation: OLIVEIRA, Gustavo Henrique Ferreira de Miranda. Previsão de séries temporais na presença de mudança de conceito: uma abordagem baseada em PSO. 2018. Dissertação de Mestrado. Universidade Federal de Pernambuco.
Flink exercises for UPM's Master in Data Science's Cloud Computing course
Persistor is a product used for permanent data storage. It provides support for multiple cloud platforms, more specifically the Google Cloud Platform and Microsoft Azure, as well as for various broker types (GCP PubSub, Azure Service Bus and Kafka) and different kinds of storage services (Google Cloud Storage and Azure Blob Storage).
This repository contains the implementation of Distributed File Server, Concurrent Echo Client-Server Application in Java using Socket Programming.
XGBoost model on a data stream to predict stock prices
lib-brokers is a Go library which contains the interfaces used to interact with messaging systems without relying on a specific technology or client library. This library attempts to solve the issue of properly abstracting away the interaction between applications and messaging systems.
This repository use some SSL characteristics to perform the classification in Non-Stationary Data Streams.
Implementation of streaming algorithms (Misra-Gries & Lossy Counting) for getting frequent items from data streams.
Исследование 1. ООП и ФП -> поиск баланса / Case Study 1. OOP & FP -> Finding Balance
The system simulates the VISA-transaction data management for analytics. It must receive data from an input data stream (Main.py) that simulates the real time data arrival. The data must be distributed to two types of clients with different needs
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