Microsoft has announced their implementation of the Apache Avro wire protocol. Avro is described a “compact binary data serialization format similar to Thrift or Protocol Buffers” with additional features needed for distributed processing environments such as Hadoop.
The recently released open source scan report by Coverity mainly detected and fixed Resource Leaks, Null Pointer and Control Flow issues besides several other issues. It also scanned the source code of Linux and fixed several bugs.
Starting from the premise that today “80 percent of enterprise data is unstructured and growing at twice the rate of structured data”, Cloudera and MongoDB have announced a “strategic” partnership meant to provide customers the option to combine Cloudera’s Apache-based Big Data platform with MongoDB’s NoSQL solution.
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.
Hadoop 2.4.0 was recently released with several enhancements to both HDFS and YARN. This includes support for Access Control Lists, Native support for Rolling upgrades, Full HTTPS support for HDFS, Automatic failover of YARN and other operational improvements
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.
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.
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.
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.
The patterns & practices group at Microsoft have released a guide with solutions and patterns suitable when implementing cloud-hosted applications. The guide contains ten guidance topics together with 24 design patterns targeting eight categories of problems covering common areas in cloud application development. Also included are ten sample applications to demonstrate the usage these patterns.
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.
Hadoop is definitely the platform of choice for Big Data analysis and computation. While data Volume, Variety and Velocity increases, Hadoop as a batch processing framework cannot cope with the requirement for real time analytics. Spark, Storm and the Lambda Architecture can help bridge the gap between batch and event based processing.
With a new connector, it is now possible for Hadoop to run directly against Google Cloud Storage instead of using the default, distributed file system. This results in lower storage costs, fewer data replication activities, and a simpler overall process.