Papers on GitHub

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 Elasti Csearch 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 ALG Orithm.
• 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 the Internet scale.
• 2013– Facebook –scuba:diving to Data at Facebook.
• 2013– Facebook –unicorn:a System for searching the social Graph.
• 2013– Facebook –scaling Memcache at Facebook.

2011–2012

• 2012– Twitter , Haven 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–present Amplab –scarlett:coping with skewed popularity Content in MapReduce Clusters.
• 2011–present Amplab –mesos:a Platform for fine-grained Resource sharing in the Data Center.
• 2011–present 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 noti?cations base of Percol Ator 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–present Amplab –chukwa:a large-scale monitoring system.
• 2007–present Amazon –dynamo:amazon ' s highly Available key-value Store.
• 2006–present Google , Haven Chubby Lock Service for loosely-coupled distributed systems.
• 2006–present Google –bigtable:a distributed Storage System for structured Data.
• 2004–present Google –mapreduce:simplied Data processing on Large Clusters.
• 2003–present Google , haven Google File System.

Papers on GitHub