Displaying 1 to 20 from 58 results

Vespa - Yahoo's big data serving engine


Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time. Vespa is serving platform for Yahoo.com, Yahoo News, Yahoo Sports, Yahoo Finance, Yahoo Gemini, Flickr.

genie - Distributed Big Data Orchestration Service


Genie is a federated job orchestration engine developed by Netflix. Genie provides REST-ful APIs to run a variety of big data jobs like Hadoop, Pig, Hive, Spark, Presto, Sqoop and more. It also provides APIs for managing the metadata of many distributed processing clusters and the commands and applications which run on them.See the official website to find documentation about Genie and specific documentation for various releases.

DataflowJavaSDK - Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines


Google Cloud Dataflow SDK for Java is a distribution of Apache Beam designed to simplify usage of Apache Beam on Google Cloud Dataflow service. This artifact includes the parent POM for other Dataflow SDK artifacts.




pachyderm - 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.

spark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks


This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

Spark - Fast Cluster Computing


Apache Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write. To run programs faster, Spark offers a general execution model that can optimize arbitrary operator graphs, and supports in-memory computing, which lets it query data faster than disk-based engines like Hadoop.


Presto - Distributed SQL query engine for big data


Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. It allows querying data from relational / nosql databases. A single Presto query can combine data from multiple sources, allowing for analytics across your entire organization. It is developed by Facebook.

Facets - Visualizations for machine learning datasets


The facets project contains two visualizations for understanding and analyzing machine learning datasets: Facets Overview and Facets Dive. The visualizations are implemented as Polymer web components, backed by Typescript code and can be easily embedded into Jupyter notebooks or webpages.

Apache REEF - a stdlib for Big Data


Apache REEF (Retainable Evaluator Execution Framework) is a library for developing portable applications for cluster resource managers such as Apache Hadoop YARN or Apache Mesos. For example, Microsoft Azure Stream Analytics is built on REEF and Hadoop.

Apache Tez - A Framework for YARN-based, Data Processing Applications In Hadoop


Apache Tez is an extensible framework for building high performance batch and interactive data processing applications, coordinated by YARN in Apache Hadoop. Tez improves the MapReduce paradigm by dramatically improving its speed, while maintaining MapReduce’s ability to scale to petabytes of data. Important Hadoop ecosystem projects like Apache Hive and Apache Pig use Apache Tez, as do a growing number of third party data access applications developed for the broader Hadoop ecosystem.

Hue - The open source Apache Hadoop UI


Hue is a Web application for interacting with Apache Hadoop. It supports a FileBrowser for accessing HDFS, JobBrowser for accessing MapReduce jobs (MR1/MR2-YARN), Job Designer for creating MapReduce/Streaming/Java jobs, HBase Browser for exploring and modifying HBase tables and data, Oozie App for submitting and scheduling workflows and bundles, A Pig/HBase/Sqoop2 shell, Beeswax application for executing Hive queries, Search app for querying Solr and Solr Cloud.

Cascalog - Data processing on Hadoop


Cascalog is a fully-featured data processing and querying library for Clojure or Java. The main use cases for Cascalog are processing "Big Data" on top of Hadoop or doing analysis on your local computer. Cascalog is a replacement for tools like Pig, Hive, and Cascading and operates at a significantly higher level of abstraction than those tools.

spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset


This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens dataset to build a movie recommender using collaborative filtering with Spark's Alternating Least Saqures implementation. It is organised in two parts. The first one is about getting and parsing movies and ratings data into Spark RDDs. The second is about building and using the recommender and persisting it for later use in our on-line recommender system. This tutorial can be used independently to build a movie recommender model based on the MovieLens dataset. Most of the code in the first part, about how to use ALS with the public MovieLens dataset, comes from my solution to one of the exercises proposed in the CS100.1x Introduction to Big Data with Apache Spark by Anthony D. Joseph on edX, that is also publicly available since 2014 at Spark Summit. Starting from there, I've added with minor modifications to use a larger dataset, then code about how to store and reload the model for later use, and finally a web service using Flask.

Apache Metron - Real-time Big Data Security


Metron integrates a variety of open source big data technologies in order to offer a centralized tool for security monitoring and analysis. Metron provides capabilities for log aggregation, full packet capture indexing, storage, advanced behavioral analytics and data enrichment, while applying the most current threat intelligence information to security telemetry within a single platform.

Shark - Hive on Spark


Shark is an open source distributed SQL query engine for Hadoop data. It brings state-of-the-art performance and advanced analytics to Hive users. It runs Hive queries up to 100x faster in memory, or 10x on disk. it is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive.

Postgres-XL - Scalable Open Source PostgreSQL-based Database Cluster


Postgres-XL is a horizontally scalable open source SQL database cluster, flexible enough to handle varying database workloads like OLTP, Business Intelligence requiring MPP parallelism, Key value store, GIS Geospatial and lot more.

Fluo - Make incremental updates to large data sets stored in Apache Accumulo


Apache Fluo (incubating) is an open source implementation of Percolator (which populates Google's search index) for Apache Accumulo. Fluo makes it possible to update the results of a large-scale computation, index, or analytic as new data is discovered. When combining new data with existing data, Fluo offers reduced latency when compared to batch processing frameworks (e.g Spark, MapReduce).