storm auf GitHub. Twitter has published its distributed event system as open source. Sounds very interesting - basically a load-distributed and fault-tolerant RPC dispatcher with a guarantee of execution for each event. Generally interesting where you need to process high events/sec - Storm offers easy scaling as needed by simply adding new nodes. And it's also quite independent of the programming languages used (Ruby and Python are already on board as additional languages besides JVM languages).
distributed
Replication, atomicity and order in distributed systems. A very interesting article about distribution and ordering in distributed systems with parallel execution - because it's not really trivial. Worth reading just for the links to various projects in that area. At the end a bit of a cliffhanger, because it refers to an upcoming article - hopefully it will come, because its topic sounds interesting too.
Basho: An Introduction to Riak. I should take a closer look at this, it has a quite clean and simple architecture and all nodes in the system are equal (this is similar to Cassandra). The whole thing is written in Erlang here, but interesting is the MapReduce interface: functions can be delivered as JavaScript code and the communication goes over a simple JSON interface.
HBase vs Cassandra: why we moved « Dominic Williams. Not entirely uninteresting blog post that dares to compare Hadoop/HBase with Cassandra and tries to highlight the different focuses. His conclusion: HBase is more for warehousing, Cassandra more for transaction processing. Alone, this would make something like Brix even more interesting if it could really combine these two aspects.
Brisk – Apache Hadoop™ powered by Cassandra | DataStax. I should also keep an eye on this, as someone is marrying Hadoop with Cassandra as a backend. This makes higher-level layers from the Hadoop project also usable with the high performance of Cassandra and, above all, with the freer update of data in Cassandra and its eventually-consistent model.
HIVE: Data Warehousing & Analytics on Hadoop. A point that particularly interests me at the moment: evaluations, especially BI, of large amounts of data. Hadoop provides Hive as a solution for this. Access to HIVE can not only be made via Thrift, but also via JDBC and ODBC.
Apache Thrift. Also worth taking a closer look, in principle something similar to Google Protocol Buffers, but more prevalent in the Apache environment. Therefore, in areas like Hadoop, it is often chosen as the path of choice for accessing services from various languages. A small comparison of the two protocols. I like that Thrift not only allows binary representation but also a JSON-based representation - this makes the integration of Thrift APIs into web solutions easier, as JSON is native to JavaScript.
The Secrets of Building Realtime Big Data Systems. This is how I came across Cascalog and ElephantDB: a talk by the programmer of both projects about Big Data. He is also currently writing a book "Big Data". Could be very interesting.
nathanmarz/elephantdb. Same author as for Cascalog, here he built a distributed Key/Value-Store on Hadoop with Clojure. Also not uncool.
nathanmarz/cascalog - take a closer look, a marriage of Clojure and Hadoop for easier evaluation of large data sets. The interesting thing about Cascalog: it draws ideas from Datalog and forms a query language for Hadoop data sets in Clojure.
Trinity - Microsoft Research. I should take a closer look at this, it sounds somewhat like distributed Redis (in-memory structures that are persisted) combined with a query semantics that is more based on graph relationships (comparable to RDF Triple Stores).