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Solr - Blazing-fast, open source enterprise search platform
Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, dynamic clustering, database integration, and rich document (e.g., Word, PDF) handling. Solr is highly scalable, providing distributed search and index replication, and it powers the search and navigation features of many of the world's largest internet sites.
search-engine searchengine full-text-search facet distributed analyticscrossfilter - Fast n-dimensional filtering and grouping of records.
Fast multidimensional filtering for coordinated views.
square analytics visualizationstats - A statistics package with common functions that are missing from the Golang standard library
A statistics package with many functions missing from the Golang standard library. See the CHANGELOG.md for API changes and tagged releases you can vendor into your projects.Protip: go get -u github.com/montanaflynn/stats updates stats to the latest version.
statistics math data analyticsKibana - Analytics and search dashboard for Elasticsearch
Kibana provides flexible analytics and visualization platform for Elasticsearch. It understands large volume of data and easily create bar charts, line and scatter plots, histograms, pie charts, and maps. It can provide real-time summary and charting of streaming data. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.
analytics dashboard visualization real-time-charts time-series graph chartsAutotrack - Automatic and enhanced Google Analytics tracking for common user interactions on the web
The default JavaScript tracking snippet for Google Analytics runs when a web page is first loaded and sends a pageview hit to Google Analytics. If you want to know about more than just pageviews (e.g. where the user clicked, how far they scroll, did they see certain elements, etc.), you have to write code to capture that information yourself.Since most website owners care about a lot of the same types of user interactions, web developers end up writing the same code over and over again for every new site they build.
analytics analyticsjs auto google click-tracking web-analytics user-behaviour google-analyticsdisc - :chart_with_upwards_trend: Visualise the module tree of browserify project bundles and track down bloat
Disc is a tool for analyzing the module tree of browserify project bundles. It's especially handy for catching large and/or duplicate modules which might be either bloating up your bundle or slowing down the build process.The demo included on disc's github page is the end result of running the tool on browserify's own code base.
analyze analytics directory file modules tree tool browserify size structure visualizeangulartics - Analytics for AngularJS applications.
**Note: we are dropping support for NuGet.You can also use $analyticsProvider.withBase(true) instead of $analyticsProvider.withAutoBase(true) if you are using a <base> HTML tag.
angular analytics google-analytics pageview-tracking event-tracking tracking google-tag-manager woopra splunk piwik page-tracking scroll-tracking inspectlet angularjs angular-componentdazzle - :rocket: Dashboards made easy in React JS.
Dazzle is a library for building dashboards with React JS. Dazzle does not depend on any front-end libraries but it makes it easier to integrate with them.Dazzle's goal is to be flexible and simple. Even though there are some UI components readily available out of the box, you have the complete control to override them as you wish with your own styles and layout.
dashboard react-js visualization analytics dashboard-widget drag-and-drop react react-component widgets react-dazzle analytics-dashboardTimescaledb - An open-source time-series database optimized for fast ingest and complex queries
TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), as well as full SQL support. TimescaleDB is packaged as a PostgreSQL extension and released under the Apache 2 open-source license.
time-series-database postgresql time-series sql postgres tsdb iot financial-analysis analytics database postgresql-extensionHypertable - A high performance, scalable, distributed storage and processing system for structured
Hypertable is based on Google's Bigtable Design, which is a proven scalable design that powers hundreds of Google services. Many of the current scalable NoSQL database offerings are based on a hash table design which means that the data they manage is not kept physically ordered. Hypertable keeps data physically sorted by a primary key and it is well suited for Analytics.
no-sql distributed-database column-store analytics database distributed scalable cloud-databaseElasticSearch - Distributed, RESTful search and analytics engine
Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected.
searchengine search-engine full-text-search document-oriented percolator analytics nosql time-series aggregationaws-amplify - A declarative JavaScript library for application development using cloud services.
AWS Amplify provides a declarative and easy-to-use interface across different categories of cloud operations. AWS Amplify goes well with any JavaScript based frontend workflow, and React Native for mobile developers. Our default implementation works with Amazon Web Services (AWS), but AWS Amplify is designed to be open and pluggable for any custom backend or service.
react cloud-service metrics react-native aws aws-apigateway aws-cognito pinpoint aws-mobile aws-mobilehub aws-s3 mobile-analytics pwa analytics storage cognito amazon-cognitopachyderm - Reproducible Data Science at Scale!
Pachyderm is a tool for production data pipelines. If you need to chain together data scraping, ingestion, cleaning, munging, wrangling, processing, modeling, and analysis in a sane way, then Pachyderm is for you. If you have an existing set of scripts which do this in an ad-hoc fashion and you're looking for a way to "productionize" them, Pachyderm can make this easy for you. Install Pachyderm locally or deploy on AWS/GCE/Azure in about 5 minutes.
pachyderm docker analytics big-data containers distributed-systems kubernetes data-science data-analysisjquery-scrolldepth - Google Analytics plugin to track visitor scroll depth
Scroll Depth is a Google Analytics plugin that tracks how far users are scrolling. The plugin provides native support for Universal Analytics, Classic Google Analytics, and Google Tag Manager. It can also be used with any analytics service that supports events. Bug reports and code contributions are welcome. Please see contributing.md.
scroll-depth analytics google-analytics scrollingappmetrics
A small (803 bytes gzipped) library for measuring things in your web app, annotating the DevTools timeline, and reporting the results to Google Analytics. If you chose to send metrics to Google Analytics, values will show up its UI. See below.
metrics appmetrics google-analytics user-timing lib analytics reporting measure measurement instrumentMapD - The MapD Core database
MapD Core is an in-memory, column store, SQL relational database that was designed from the ground up to run on GPUs. MapD Core is the foundational element of a larger data exploration platform that emphasizes speed at scale. By taking advantage of the parallel processing power of the hardware, MapD Core can query billions of rows in milliseconds. Furthermore, by using the graphics pipelines of GPUs, MapD Core can render graphics directly from the server.
gpu database olap visualization sql machine-learning analytics column-store columnar-databasedask - Parallel computing with task scheduling
Dask is a flexible parallel computing library for analytics. See documentation for more information. New BSD. See License File.
analyticsClickHouse - Columnar DBMS and Real Time Analytics
ClickHouse is an open source column-oriented database management system capable of real time generation of analytical data reports using SQL queries. It is Linearly Scalable, Blazing Fast, Highly Reliable, Fault Tolerant, Data compression, Real time query processing, Web analytics, Vectorized query execution, Local and distributed joins. It can process hundreds of millions to more than a billion rows and tens of gigabytes of data per single server per second.
database columnar-database column-oriented columnar analytics real-time big-databoba.js - Need those links tracked? "As you wish."
Boba.js is a small, easily extensible JavaScript library that makes working with Google Analytics easier. It supports the old ga.js library as well as the new analytics.js library. It has one out of the box function, trackLinks, and makes tracking everything else child's play. Requires jQuery.
analytics trackingsnappydata - SnappyData - The Spark Database. Stream, Transact, Analyze, Predict in one cluster
Apache Spark is a general purpose parallel computational engine for analytics at scale. At its core, it has a batch design center and is capable of working with disparate data sources. While this provides rich unified access to data, this can also be quite inefficient and expensive. Analytic processing requires massive data sets to be repeatedly copied and data to be reformatted to suit Spark. In many cases, it ultimately fails to deliver the promise of interactive analytic performance. For instance, each time an aggregation is run on a large Cassandra table, it necessitates streaming the entire table into Spark to do the aggregation. Caching within Spark is immutable and results in stale insight. At SnappyData, we take a very different approach. SnappyData fuses a low latency, highly available in-memory transactional database (GemFireXD) into Spark with shared memory management and optimizations. Data in the highly available in-memory store is laid out using the same columnar format as Spark (Tungsten). All query engine operators are significantly more optimized through better vectorization and code generation. The net effect is, an order of magnitude performance improvement when compared to native Spark caching, and more than two orders of magnitude better Spark performance when working with external data sources.
snappydata spark memory-database analytics stream transaction scale
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