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.
2013 has been rich in announcements for new programs, degrees and grants for aspiring data scientists and Big Data practitioners.
Qubole, a managed Hadoop-as-a-Service offering is now available on Google Compute Engine (GCE). Qubole was so far only available on Amazon's AWS and this announcement follows only a few days after Google releasing GCE into general availability.
The MapReduce paradigm is not always ideal when dealing with large computationally intensive algorithms. A small team of entrepreneurs is building a product called ParallelX to solve that bottleneck by harnessing the power of GPUs to give Hadoop jobs a significant boost.
This post presents the results of a Hortonworks survey of over 500 Hadoop Summit 2013 attendees on how they use Hadoop, and an interview with David McJannet on Hadoop trends today.
With Facebook recently releasing Presto as open source, the already crowded SQL-in-Hadoop market just became a tad more intricate. A number of open source tools are competing for the attention of developers: Hortonworks Stinger initiative around Hive, Apache Drill, Apache Tajo, Cloudera’s Impala, Salesforce’s Phoenix (for HBase) and now Facebook’s Presto.
On each day of the 3-day conference at the inviting environs offered at the Hyatt there was a jam-packed schedule of speakers, exhibits and activities that made for some difficult decisions as to which tracks and what happening to attend.
New version of Cascading released this week incorporates Hadoop 2 support and includes Cascading Lingual - an open source project that provides a comprehensive ANSI SQL interface for accessing Hadoop-based data