资源list：Github上关于大数据的开源项目、论文等合集

Awesome Big Data

A curated list of awesome big data frameworks, resources and other awesomeness. Inspired byawesome-php, awesome-python, awesome-ruby, hadoopecosystemtable & big-data.

Your contributions are always welcome!

• Awesome Big Data
  • Frameworks
  • Distributed Programming
  • Distributed Filesystem
  • Key-Map Data Model
  • Document Data Model
  • Key-value Data Model
  • Graph Data Model
  • NewSQL Databases
  • Columnar Databases
  • Time-Series Databases
  • SQL-like processing
  • Integrated Development Environments
  • Data Ingestion
  • Service Programming
  • Scheduling
  • Machine Learning
  • Benchmarking
  • Security
  • System Deployment
  • Applications
  • Search engine and framework
  • MySQL forks and evolutions
  • PostgreSQL forks and evolutions
  • Memcached forks and evolutions
  • Embedded Databases
  • Business Intelligence
  • Data Visualization
  • Internet of things and sensor data
  • Interesting Readings
  • Interesting Papers
• Other Awesome Lists

Frameworks

• Apache Hadoop – framework for distributed processing. Integrates MapReduce (parallel processing)， YARN (job scheduling) and HDFS (distributed file system).

Distributed Programming

• AddThis Hydra – distributed data processing and storage system originally developed at AddThis.
• AMPLab SIMR – run Spark on Hadoop MapReduce v1.
• Apache Crunch – a simple Java API for tasks like joining and data aggregation that are tedious to implement on plain MapReduce.
• Apache DataFu – collection of user-defined functions for Hadoop and Pig developed by LinkedIn.
• Apache Flink – high-performance runtime, and automatic program optimization.
• Apache Gora – framework for in-memory data model and persistence.
• Apache Hama – BSP (Bulk Synchronous Parallel) computing framework.
• Apache MapReduce – programming model for processing large data sets with a parallel, distributed algorithm on a cluster.
• Apache Pig – high level language to express data analysis programs for Hadoop.
• Apache S4 – framework for stream processing, implementation of S4.
• Apache Spark – framework for in-memory cluster computing.
• Apache Spark Streaming – framework for stream processing, part of Spark.
• Apache Storm – framework for stream processing by Twitter also on YARN.
• Apache Tez – application framework for executing a complex DAG (directed acyclic graph) of tasks, built on YARN.
• Apache Twill – abstraction over YARN that reduces the complexity of developing distributed applications.
• Cascalog – data processing and querying library.
• Cheetah – High Performance, Custom Data Warehouse on Top of MapReduce.
• Concurrent Cascading – framework for data management/analytics on Hadoop.
• Damballa Parkour – MapReduce library for Clojure.
• Datasalt Pangool – alternative MapReduce paradigm.
• DataTorrent StrAM – real-time engine is designed to enable distributed, asynchronous, real time in-memory big-data computations in as unblocked a way as possible, with minimal overhead and impact on performance.
• Facebook Corona – Hadoop enhancement which removes single point of failure.
• Facebook Peregrine – Map Reduce framework.
• Facebook Scuba – distributed in-memory datastore.
• Google Dataflow – create data pipelines to help themæingest, transform and analyze data.
• Google MapReduce – map reduce framework.
• Google MillWheel – fault tolerant stream processing framework.
• JAQL – declarative programming language for working with structured, semi-structured and unstructured data.
• Kite – is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.
• Metamarkers Druid – framework for real-time analysis of large datasets.
• Netflix PigPen – map-reduce for Clojure whiche compiles to Apache Pig.
• Nokia Disco – MapReduce framework developed by Nokia.
• Pinterest Pinlater – asynchronous job execution system.
• Pydoop – Python MapReduce and HDFS API for Hadoop.
• Stratosphere – general purpose cluster computing framework.
• Streamdrill – usefull for counting activities of event streams over different time windows and finding the most active one.
• Twitter Scalding – Scala library for Map Reduce jobs, built on Cascading.
• Twitter Summingbird – Streaming MapReduce with Scalding and Storm, by Twitter.
• Twitter TSAR – TimeSeries AggregatoR by Twitter.

Distributed Filesystem

• Apache HDFS – a way to store large files across multiple machines.
• BeeGFS – formerly FhGFS, parallel distributed file system.
• Ceph Filesystem – software storage platform designed.
• Facebook Haystack – object storage system.
• Google Colossus – distributed filesystem (GFS2).
• Google GFS – distributed filesystem.
• Google Megastore – scalable, highly available storage.
• GridGain – GGFS, Hadoop compliant in-memory file system.
• Lustre file system – high-performance distributed filesystem.
• Quantcast File System QFS – open-source distributed file system.
• Red Hat GlusterFS – scale-out network-attached storage file system.
• Tachyon – reliable file sharing at memory speed across cluster frameworks.

Document Data Model

• Actian Versant – commercial object-oriented database management systems .
• Crate Data – is an open source massively scalable data store. It requires zero administration.
• Facebook Apollo – Facebook’s Paxos-like NoSQL database.
• jumboDB – document oriented datastore over Hadoop.
• LinkedIn Espresso – horizontally scalable document-oriented NoSQL data store.
• MarkLogic – Schema-agnostic Enterprise NoSQL database technology.
• MongoDB – Document-oriented database system.
• RavenDB – A transactional, open-source Document Database.
• RethinkDB – document database that supports queries like table joins and group by.

Key Map Data Model

Note: There is some term confusion in the industry， and two different things are called “Columnar Databases”. Some, listed here, are distributed, persistent databases built around the “key-map” data model: all data has a (possibly composite) key， with which a map of key-value pairs is associated. In some systems, multiple such value maps can be associated with a key， and these maps are referred to as “column families” (with value map keys being referred to as “columns”).

Another group of technologies that can also be called “columnar databases” is distinguished by how it stores data, on disk or in memory — rather than storing data the traditional way， where all column values for a given key are stored next to each other, “row by row”， these systems store all columnvalues next to each other. So more work is needed to get all columns for a given key， but less work is needed to get all values for a given column.

The former group is referred to as “key map data model” here. The line between these and the Key-value Data Model stores is fairly blurry.

The latter, being more about the storage format than about the data model, is listed under Columnar Databases.

You can read more about this distinction on Prof. Daniel Abadi’s blog: Distinguishing two major types of Column Stores.

• Apache Accumulo – distribuited key/value store, built on Hadoop.
• Apache Cassandra – column-oriented distribuited datastore, inspired by BigTable.
• Apache HBase – column-oriented distribuited datastore, inspired by BigTable.
• Facebook HydraBase – evolution of HBase made by Facebook.
• Google BigTable – column-oriented distributed datastore.
• Google Cloud Datastore – is a fully managed, schemaless database for storing non-relational data over BigTable.
• Hypertable – column-oriented distribuited datastore, inspired by BigTable.
• InfiniDB – is accessed through a MySQL interface and use massive parallel processing to parallelize queries.
• OhmData C5 – improved version of HBase.
• Tephra – Transactions for HBase.
• Twitter Manhattan – real-time, multi-tenant distributed database for Twitter scale.

Key-value Data Model

• Aerospike – NoSQL flash-optimized, in-memory. Open source and “Server code in ‘C’ (not Java or Erlang) precisely tuned to avoid context switching and memory copies.”
• Amazon DynamoDB – distributed key/value store, implementation of Dynamo paper.
• Edis – is a protocol-compatible Server replacement for Redis.
• ElephantDB – Distributed database specialized in exporting data from Hadoop.
• EventStore – distributed time series database.
• LinkedIn Krati – is a simple persistent data store with very low latency and high throughput.
• Linkedin Voldemort – distributed key/value storage system.
• Oracle NoSQL Database – distributed key-value database by Oracle Corporation.
• Redis – in memory key value datastore.
• Riak – a decentralized datastore.
• Storehaus – library to work with asynchronous key value stores, by Twitter.
• Tarantool – an efficient NoSQL database and a Lua application server.
• TreodeDB – key-value store that’s replicated and sharded and provides atomic multirow writes.

Graph Data Model

• Apache Giraph – implementation of Pregel, based on Hadoop.
• Apache Spark Bagel – implementation of Pregel, part of Spark.
• ArangoDB – multi model distribuited database.
• Facebook TAO – TAO is the distributed data store that is widely used at facebook to store and serve the social graph.
• Google Cayley – open-source graph database.
• Google Pregel – graph processing framework.
• GraphLab PowerGraph – a core C++ GraphLab API and a collection of high-performance machine learning and data mining toolkits built on top of the GraphLab API.
• GraphX – resilient Distributed Graph System on Spark.
• Gremlin – graph traversal Language.
• Infovore – RDF-centric Map/Reduce framework.
• Intel GraphBuilder – tools to construct large-scale graphs on top of Hadoop.
• MapGraph – Massively Parallel Graph processing on GPUs.
• Neo4j – graph database writting entirely in Java.
• OrientDB – document and graph database.
• Phoebus – framework for large scale graph processing.
• Titan – distributed graph database, built over Cassandra.
• Twitter FlockDB – distribuited graph database.

Columnar Databases

Note please read the note on Key-Map Data Model section.

• Columnar Storage – an explanation of what columnar storage is and when you might want it.
• Actian Vector – column-oriented analytic database.
• C-Store – column oriented DBMS.
• MonetDB – column store database.
• Parquet – columnar storage format for Hadoop.
• Pivotal Greenplum – purpose-built, dedicated analytic data warehouse that offers a columnar engine as well as a traditional row-based one.
• Vertica – is designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses.
• Google BigQuery Google’s cloud offering backed by their pioneering work on Dremel.
• Amazon Redshift Amazon’s cloud offering, also based on a columnar datastore backend.

NewSQL Databases

• Actian Ingres – commercially supported, open-source SQL relational database management system.
• Amazon RedShift – data warehouse service, based on PostgreSQL.
• BayesDB – statistic oriented SQL database.
• Cockroach – Scalable, Geo-Replicated, Transactional Datastore.
• Datomic – distributed database designed to enable scalable, flexible and intelligent applications.
• FoundationDB – distributed database, inspired by F1.
• Google F1 – distributed SQL database built on Spanner.
• Google Spanner – globally distributed semi-relational database.
• H-Store – is an experimental main-memory， parallel database management system that is optimized for on-line transaction processing (OLTP) applications.
• Haeinsa – linearly scalable multi-row， multi-table transaction library for HBase based on Percolator.
• HandlerSocket – NoSQL plugin for MySQL/MariaDB.
• InfiniSQL – infinity scalable RDBMS.
• MemSQL – in memory SQL database witho optimized columnar storage on flash.
• NuoDB – SQL/ACID compliant distributed database.
• Oracle Database – object-relational database management system.
• Oracle TimesTen in-Memory Database – in-memory， relational database management system with persistence and recoverability.
• Pivotal GemFire XD – Low-latency， in-memory， distributed SQL data store. Provides SQL interface to in-memory table data, persistable in HDFS.
• SAP HANA – is an in-memory， column-oriented, relational database management system.
• SenseiDB – distributed, realtime, semi-structured database.
• Sky – database used for flexible, high performance analysis of behavioral data.
• SymmetricDS – open source software for both file and database synchronization.

Time-Series Databases

• Cube – uses MongoDB to store time series data.
• InfluxDB – distributed time series database.
• Kairosdb – similar to OpenTSDB but allows for Cassandra.
• OpenTSDB – distributed time series database on top of HBase.

SQL-like processing

• Actian SQL for Hadoop – high performance interactive SQL access to all Hadoop data.
• AMPLAB Shark – data warehouse system for Spark.
• Apache Drill – framework for interactive analysis, inspired by Dremel.
• Apache HCatalog – table and storage management layer for Hadoop.
• Apache Hive – SQL-like data warehouse system for Hadoop.
• Apache Optiq – framework that allows efficient translation of queries involving heterogeneous and federated data.
• Apache Phoenix – SQL skin over HBase.
• BlinkDB – massively parallel, approximate query engine.
• Cloudera Impala – framework for interactive analysis, Inspired by Dremel.
• Concurrent Lingual – SQL-like query language for Cascading.
• Datasalt Splout SQL – full SQL query engine for big datasets.
• Facebook PrestoDB – distributed SQL query engine.
• Google BigQuery – framework for interactive analysis, implementation of Dremel.
• Pivotal HAWQ – SQL-like data warehouse system for Hadoop.
• RainstorDB – database for storing petabyte-scale volumes of structured and semi-structured data.
• Spark Catalyst – is a Query Optimization Framework for Spark and Shark.
• SparkSQL – Manipulating Structured Data Using Spark.
• Splice Machine – a full-featured SQL-on-Hadoop RDBMS with ACID transactions.
• Stinger – interactive query for Hive.
• Tajo – distributed data warehouse system on Hadoop.
• Trafodion – enterprise-class SQL-on-HBase solution targeting big data transactional or operational workloads.

Integrated Development Environments

• R-Studio – IDE for R.

Data Ingestion

• Amazon Kinesis – real-time processing of streaming data at massive scale.
• Apache Chukwa – data collection system.
• Apache Flume – service to manage large amount of log data.
• Apache Kafka – distributed publish-subscribe messaging system.
• Apache Samza – stream processing framework, based on Kafla and YARN.
• Apache Sqoop – tool to transfer data between Hadoop and a structured datastore.
• Cloudera Morphlines – framework that help ETL to Solr, HBase and HDFS.
• Facebook Scribe – streamed log data aggregator.
• Fluentd – tool to collect events and logs.
• Google Photon – geographically distributed system for joining multiple continuously flowing streams of data in real-time with high scalability and low latency.
• Heka – open source stream processing software system.
• HIHO – framework for connecting disparate data sources with Hadoop.
• Kestrel – distributed message queue system.
• LinkedIn Databus – stream of change capture events for a database.
• LinkedIn Kamikaze – utility package for compressing sorted integer arrays.
• LinkedIn White Elephant – log aggregator and dashboard.
• Logstash – a tool for managing events and logs.
• Netflix Suro – log agregattor like Storm and Samza based on Chukwa.
• Pinterest Secor – is a service implementing Kafka log persistance.

Service Programming

• Akka Toolkit – runtime for distributed, and fault tolerant event-driven applications on the JVM.
• Apache Avro – data serialization system.
• Apache Curator – Java libaries for Apache ZooKeeper.
• Apache Karaf – OSGi runtime that runs on top of any OSGi framework.
• Apache Thrift – framework to build binary protocols.
• Apache Zookeeper – centralized service for process management.
• Google Chubby – a lock service for loosely-coupled distributed systems.
• Linkedin Norbert – cluster manager.
• OpenMPI – message passing framework.
• Serf – decentralized solution for service discovery and orchestration.
• Spotify Luigi – a Python package for building complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more.
• Spring XD – distributed and extensible system for data ingestion, real time analytics, batch processing, and data export.
• Twitter Elephant Bird – libraries for working with LZOP-compressed data.
• Twitter Finagle – asynchronous network stack for the JVM.

Scheduling

• Apache Aurora – is a service scheduler that runs on top of Apache Mesos.
• Apache Falcon – data management framework.
• Apache Oozie – workflow job scheduler.
• Chronos – distributed and fault-tolerant scheduler.
• Linkedin Azkaban – batch workflow job scheduler.
• Sparrow – scheduling platform.

Machine Learning

• Apache Mahout – machine learning library for Hadoop.
• brain – Neural networks in JavaScript.
• Cloudera Oryx – real-time large-scale machine learning.
• Concurrent Pattern – machine learning library for Cascading.
• convnetjs – Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
• Decider – Flexible and Extensible Machine Learning in Ruby.
• etcML – text classification with machine learning.
• Etsy Conjecture – scalable Machine Learning in Scalding.
• Google Sibyl – System for Large Scale Machine Learning at Google.
• H2O – statistical, machine learning and math runtime for Hadoop.
• MLbase – distributed machine learning libraries for the BDAS stack.
• MLPNeuralNet – Fast multilayer perceptron neural network library for iOS and Mac OS X.
• nupic – Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.
• PredictionIO – machine learning server buit on Hadoop, Mahout and Cascading.
• scikit-learn – scikit-learn: machine learning in Python.
• Spark MLlib – a Spark implementation of some common machine learning (ML) functionality.
• Vowpal Wabbit – learning system sponsored by Microsoft and Yahoo!.
• WEKA – suite of machine learning software.

Benchmarking

• Apache Hadoop Benchmarking – micro-benchmarks for testing Hadoop performances.
• Berkeley SWIM Benchmark – real-world big data workload benchmark.
• Intel HiBench – a Hadoop benchmark suite.
• PUMA Benchmarking – benchmark suite for MapReduce applications.
• Yahoo Gridmix3 – Hadoop cluster benchmarking from Yahoo engineer team.

Security

• Apache Knox Gateway – single point of secure access for Hadoop clusters.
• Apache Sentry – security module for data stored in Hadoop.

System Deployment

• Apache Ambari – operational framework for Hadoop mangement.
• Apache Bigtop – system deployment framework for the Hadoop ecosystem.
• Apache Helix – cluster management framework.
• Apache Mesos – cluster manager.
• Apache Slider – is a YARN application to deploy existing distributed applications on YARN.
• Apache Whirr – set of libraries for running cloud services.
• Apache YARN – Cluster manager.
• Brooklyn – library that simplifies application deployment and management.
• Buildoop – Similar to Apache BigTop based on Groovy language.
• Cloudera HUE – web application for interacting with Hadoop.
• Facebook Prism – multi datacenters replication system.
• Google Borg – job scheduling and monitoring system.
• Google Omega – job scheduling and monitoring system.
• Hortonworks HOYA – application that can deploy HBase cluster on YARN.
• Marathon – Mesos framework for long-running services.

Applications

• Apache Kiji – framework to collect and analyze data in real-time, based on HBase.
• Apache Nutch – open source web crawler.
• Apache OODT – capturing, processing and sharing of data for NASA’s scientific archives.
• Apache Tika – content analysis toolkit.
• Domino – Run, scale, share, and deploy models — without any infrastructure.
• Eclipse BIRT – Eclipse-based reporting system.
• Eventhub – open source event analytics platform.
• HIPI Library – API for performing image processing tasks on Hadoop’s MapReduce.
• Hunk – Splunk analytics for Hadoop.
• MADlib – data-processing library of an RDBMS to analyze data.
• PivotalR – R on Pivotal HD / HAWQ and PostgreSQL.
• Qubole – auto-scaling Hadoop cluster, built-in data connectors.
• Sense – Cloud Platform for Data Science and Big Data Analytics.
• Snowplow – enterprise-strength web and event analytics, powered by Hadoop, Kinesis, Redshift and Postgres.
• SparkR – R frontend for Spark.
• Splunk – analyzer for machine-generated date.
• Talend – unified open source environment for YARN, Hadoop, HBASE, Hive, HCatalog & Pig.

Search engine and framework

• Apache Lucene – Search engine library.
• Apache Solr – Search platform for Apache Lucene.
• ElasticSearch – Search and analytics engine based on Apache Lucene.
• Enigma.io – Freemium robust web application for exploring, filtering, analyzing, searching and exporting massive datasets scraped from across the Web.
• Facebook Unicorn – social graph search platform.
• Google Caffeine – continuous indexing system.
• Google Percolator – continuous indexing system.
• TeraGoogle – large search index.
• HBase Coprocessor – implementation of Percolator, part of HBase.
• Lily HBase Indexer – quickly and easily search for any content stored in HBase.
• LinkedIn Bobo – is a Faceted Search implementation written purely in Java, an extension to Apache Lucene.
• LinkedIn Cleo – is a flexible software library for enabling rapid development of partial, out-of-order and real-time typeahead search.
• LinkedIn Galene – search architecture at LinkedIn.
• LinkedIn Zoie – is a realtime search/indexing system written in Java.
• Sphnix Search Server – fulltext search engine.

MySQL forks and evolutions

• Amazon RDS – MySQL databases in Amazon’s cloud.
• Drizzle – evolution of MySQL 6.0.
• Google Cloud SQL – MySQL databases in Google’s cloud.
• MariaDB – enhanced, drop-in replacement for MySQL.
• MySQL Cluster – MySQL implementation using NDB Cluster storage engine.
• Percona Server – enhanced, drop-in replacement for MySQL.
• ProxySQL – High Performance Proxy for MySQL.
• TokuDB – TokuDB is a storage engine for MySQL and MariaDB.
• WebScaleSQL – is a collaboration among engineers from several companies that face similar challenges in running MySQL at scale.

PostgreSQL forks and evolutions

• HadoopDB – hybrid of MapReduce and DBMS.
• IBM Netezza – high-performance data warehouse appliances.
• Postgres-XL – Scalable Open Source PostgreSQL-based Database Cluster.
• RecDB – Open Source Recommendation Engine Built Entirely Inside PostgreSQL.
• Stado – open source MPP database system solely targeted at data warehousing and data mart applications.
• Yahoo Everest – multi-peta-byte database / MPP derived by PostgreSQL.

Memcached forks and evolutions

• Facebook McDipper – key/value cache for flash storage.
• Facebook Memcached – fork of Memcache.
• Twemproxy – A fast, light-weight proxy for memcached and redis.
• Twitter Fatcache – key/value cache for flash storage.
• Twitter Twemcache – fork of Memcache.

Embedded Databases

• Actian PSQL – ACID-compliant DBMS developed by Pervasive Software, optimized for embedding in applications.
• BerkeleyDB – a software library that provides a high-performance embedded database for key/value data.
• HanoiDB – Erlang LSM BTree Storage.
• LevelDB – a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
• LMDB – ultra-fast, ultra-compact key-value embedded data store developed by Symas.
• RocksDB – embeddable persistent key-value store for fast storage based on LevelDB.

Business Intelligence

• BIME Analytics – business intelligence platform in the cloud.
• Chartio – lean business intelligence platform to visualize and explore your data.
• Jaspersoft – powerful business intelligence suite.
• Jedox Palo – customisable Business Intelligence platform.
• Microsoft – business intelligence software and platform.
• Microstrategy – software platforms for business intelligence, mobile intelligence, and network applications.
• Pentaho – business intelligence platform.
• Qlik – business intelligence and analytics platform.
• SpagoBI – open source business intelligence platform.
• Tableau – business intelligence platform.
• Zoomdata – Big Data Analytics.

Data Visualization

• Arbor – graph visualization library using web workers and jQuery.
• CartoDB – open-source or freemium hosting for geospatial databases with powerful front-end editing capabilities and a robust API.
• Chart.js – open source HTML5 Charts visualizations.
• Crossfilter – JavaScript library for exploring large multivariate datasets in the browser. Works well with dc.js and d3.js.
• Cubism – JavaScript library for time series visualization.
• Cytoscape – JavaScript library for visualizing complex networks.
• DC.js – Dimensional charting built to work natively with crossfilter rendered using d3.js. Excellent for connecting charts/additional metadata to hover events in D3.
• D3 – javaScript library for manipulating documents.
• Envisionjs – dynamic HTML5 visualization.
• Freeboard – pen source real-time dashboard builder for IOT and other web mashups.
• Gephi – An award-winning open-source platform for visualizing and manipulating large graphs and network connections. It’s like Photoshop, but for graphs. Available for Windows and Mac OS X.
• Google Charts – simple charting API.
• Grafana – graphite dashboard frontend, editor and graph composer.
• Graphite – scalable Realtime Graphing.
• Highcharts – simple and flexible charting API.
• IPython – provides a rich architecture for interactive computing.
• Matplotlib – plotting with Python.
• NVD3 – chart components for d3.js.
• Peity – Progressive SVG bar, line and pie charts.
• Plot.ly – Easy-to-use web service that allows for rapid creation of complex charts, from heatmaps to histograms. Upload data to create and style charts with Plotly’s online spreadsheet. Fork others’ plots.
• Recline – simple but powerful library for building data applications in pure Javascript and HTML.
• Redash – open-source platform to query and visualize data.
• Sigma.js – JavaScript library dedicated to graph drawing.
• Vega – a visualization grammar.

Internet of things and sensor data

• TempoIQ – Cloud-based sensor analytics.

Interesting Readings

• Big Data Benchmark – Benchmark of Redshift, Hive, Shark, Impala and Stiger/Tez.
• NoSQL Comparison – Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Couchbase vs Neo4j vs Hypertable vs ElasticSearch vs Accumulo vs VoltDB vs Scalaris comparison.

Interesting Papers

2013 – 2014

• 2014 – Stanford – Mining of Massive Datasets.
• 2013 – AMPLab – Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices.
• 2013 – AMPLab – MLbase: A Distributed Machine-learning System.
• 2013 – AMPLab – Shark: SQL and Rich Analytics at Scale.
• 2013 – AMPLab – GraphX: A Resilient Distributed Graph System on Spark.
• 2013 – Google – HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm.
• 2013 – Microsoft – Scalable Progressive Analytics on Big Data in the Cloud.
• 2013 – Metamarkets – Druid: A Real-time Analytical Data Store.
• 2013 – Google – Online, Asynchronous Schema Change in F1.
• 2013 – Google – F1: A Distributed SQL Database That Scales.
• 2013 – Google – MillWheel: Fault-Tolerant Stream Processing at Internet Scale.
• 2013 – Facebook – Scuba: Diving into Data at Facebook.
• 2013 – Facebook – Unicorn: A System for Searching the Social Graph.
• 2013 – Facebook – Scaling Memcache at Facebook.

2011 – 2012

• 2012 – Twitter – The Unified Logging Infrastructure for Data Analytics at Twitter.
• 2012 – AMPLab – Blink and It’s Done: Interactive Queries on Very Large Data.
• 2012 – AMPLab – Fast and Interactive Analytics over Hadoop Data with Spark.
• 2012 – AMPLab – Shark: Fast Data Analysis Using Coarse-grained Distributed Memory.
• 2012 – Microsoft – Paxos Replicated State Machines as the Basis of a High-Performance Data Store.
• 2012 – Microsoft – Paxos Made Parallel.
• 2012 – AMPLab – BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data.
• 2012 – Google – Processing a trillion cells per mouse click.
• 2012 – Google – Spanner: Google’s Globally-Distributed Database.
• 2011 – AMPLab – Scarlett: Coping with Skewed Popularity Content in MapReduce Clusters.
• 2011 – AMPLab – Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center.
• 2011 – Google – Megastore: Providing Scalable, Highly Available Storage for Interactive Services.

2001 – 2010

• 2010 – Facebook – Finding a needle in Haystack: Facebook’s photo storage.
• 2010 – AMPLab – Spark: Cluster Computing with Working Sets.
• 2010 – Google – Storage Architecture and Challenges.
• 2010 – Google – Pregel: A System for Large-Scale Graph Processing.
• 2010 – Google – Large-scale Incremental Processing Using Distributed Transactions and Notifications base of Percolator and Caffeine.
• 2010 – Google – Dremel: Interactive Analysis of Web-Scale Datasets.
• 2010 – Yahoo – S4: Distributed Stream Computing Platform.
• 2009 – HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads.
• 2008 – AMPLab – Chukwa: A large-scale monitoring system.
• 2007 – Amazon – Dynamo: Amazon’s Highly Available Key-value Store.
• 2006 – Google – The Chubby lock service for loosely-coupled distributed systems.
• 2006 – Google – Bigtable: A Distributed Storage System for Structured Data.
• 2004 – Google – MapReduce: Simplied Data Processing on Large Clusters.
• 2003 – Google – The Google File System.

Other Awesome Lists

Other amazingly awesome lists can be found in the awesome-awesomeness list.

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