Cloudera recently released the latest version of its software distribution, CDH5. Almost 20 months after the last major version, CDH4 seems like ages in the Big Data world. We take a look at new features this release brings and the future direction of Cloudera after the latest round of investment from Intel and Google Ventures.
The social-networking company AddThis open-sourced Hydra under the Apache version 2.0 License in a recent announcement. Hydra grew from an in-house platform created to process semi-structured social data as live streams and do efficient query processing on those data sets.
Spark users can now use a new Big Data platform provided by intelligence company Atigeo, which bundles most of the UC Berkeley stack into a unified framework optimized for low-latency data processing that can provide significant improvements over more traditional Hadoop-based platforms.
Apache released HBase 0.98 primarily addressing convergence with Apache Accumulo via cell-based security while resolving over 230 JIRA issues. These new security features are modeled after Accumulo.
Processing extremely large graphs has been and remains a challenge, but recent advances in Big Data technologies have made this task more practical. Tapad, a startup based in NYC focused on cross-device content delivery, has made graph processing the heart of their business model using Big Data to scale to terabytes of data.
Domino, a Platform-as-a-Service for data science, enables people to do analytical work using languages such as Python or R in the cloud (EC2).
According to a new Forrest report, Hadoop’s momentum is unstoppable. Its usage in the enterprise is continuously growing due to its ability to offer companies new ways to store, process, analyze, and share big data. The report takes a look at Hadoop vendors and ranks them.
At the Mobile World Congress, IBM has announced a developer contest for developers to create mobile consumer and business apps powered by IBM Watson cognitive computing platform. The winners of the IBM Watson Mobile Developer Challenge will receive design consulting and support from IBM to gain access to the market.
Recently, Spark graduated from the Apache incubator. Spark claims up to 100x speed improvements over Apache Hadoop over in-memory datasets and gracefully falling back to 10x speed improvement for on-disk performance. Based on Scala, it can run SQL queries and be used directly in R. It provides Machine Learning, Graph database capabilities and other further discussed in the article.
Hazelcast, an open source in-memory data grid solution introduces a MapReduce API for its offering.
Elasticsearch released version 1.0.0 of its self-titled, open-source analytics tool. Elasticsearch is a distributed search engine which allows for real-time data analysis in big-data environments. The new version comes with various functional enhancements and changes to the API to make Elasticsearch more intuitive and powerful to use.
UC Berkeley’s AMPLab announced a developer preview of their new project SparkR to use Apache Spark natively from R.
In the race for interactive SQL in Big Data environments, there are two open source based front-runners, Impala and Hive with the Stinger project. Cloudera recently announced that Impala is up to 69 times faster than Hive 0.12 and can outperform DBMS. Other than raw speed, we take a look at other considerations in choosing a SQL engine for Hadoop and also Tez, an application framework for YARN.
LinkedIn’s DataFu project, a collection of libraries for Hadoop, has now officially entered the incubation status at the Apache Software Foundation (ASF) since the first week of January.