aggregations

ParadeDB is a modern Elasticsearch alternative built on Postgres. Built for real-time, update-heavy workloads.

Extremely fast faceted search engine in JavaScript - lightweight, flexible, and simple to use

Control the quality of your labeled data with the Python tools you already know.

Exercises to help improve SQL skills

json-aggregate is a javascript utility to perform aggregations on JSON-formatted data.

JS/Typescript DSL for Elasticsearch

Alternative ActiveRecord composition

Data analytics helpers for Ecto and PostgreSQL

mongodb-pipeline-builder is a pipeline builder for the db.collection.aggregate method and db.aggregate method. It will simplify pipelines by making them more readable and much easier to edit. It also allows you to test your pipelines on a dataset in order to verify them. Pipeline stages appear in an array. Documents pass through the stages in sequence.

Course with udacity that cover SQL for data Scientists, this is my solution for the lessons and the project

👊 0-downtime elasticsearch reindex functional utility. Automatically detect ES mapping modification and reindex modified records. Use namespaces in your indexes. Create aggregated filtering for shops.

Chat bots aggregator of different social networks

Learn to answer questions with data using SQL

[Software Development] Este é um repositório de um projeto acadêmico para prática dos fundamentos do paradigma orientado objeto (POO) com a linguagem de programação TypeScript. Utilizado em sala de aula para implementação dos conceitos estruturais, relacionais e organizacionais do POO.

gonna build the Frontend

This repository contains all the source codes of consensual aggregation methods implemented in R programming. The explanations of each function and algorithm are available in my webpage: https://hassothea.github.io/publications/

Blacked is a high-performance blacklist aggregator and query service built in Go.

Tariff is a list of expenses that incur while transporting the goods from one distance to another distance. Tariff is also dependent on seasonal and non-seasonal factors also. This project is aimed at predicting the tariff ratesfor truck load by using the different machine learning algorithms like lasso regression, elastic net regression, ridge regression and linear regression. Tariffisa combination of lot ofthings and tariff rate is dependent on some ofthe factorslikeYear, Road, SeasonalImpact, Fuel Cost,Distance, Weight, Toll charge, Demand, labour cost, travel expenses etc. Using some ofthese factors and by employing the above-mentioned machine learning regression algorithms we will be trying to predict the tariff rates on the trucks. By doing this we can help the industriesto estimate the tariffratesso that they can take the necessary actions and they can make their business run inprofitable way. This model helps small- and large-scale firms to control and manage the cost on transport.

This repository executes tasks in a periodic sequence to precompute computationally intensive jobs within the scope of the explorer project