Microsoft recently announced new machine learning capabilities for Microsoft Azure platform. Developers can also create their own web services and publish them to Azure Marketplace. Microsoft also announced availability of Apache Storm for Azure. Azure Stream Analytics, Data Factory and Event Hubs for Azure were all announced in the past few weeks by Microsoft. In this article we explore moreabout
Following on from the Stinger initiative delivered in Apache Hive 0.13, Hortonworks has laid out the Stinger.next roadmap to provide fully ACID transactions, a sub-second query engine, and more complete SQL 2011 analytics support, all driving towards the goal of “enhancing the speed, scale and breadth of SQL support” in Hive.
Hadoop Summit Day One report covers the important trends and changes from last year's summit. It also covers the important announcements of the day in relation to this year's trending topics. This report focuses on the platform specific innovations and announcements and not the broader partner ecosystem, which will be covered in the next few days.
DataTorrent is a real-time streaming and analyzing platform that can process over 1B real-time events/sec.
This year's ApacheCON North America conference saw key speakers focus on open source and its community. With more than 400 attendees, over 70 projects represented and 180 conference sessions it covered as many diverse topics as diverse the Apache Software Foundation projects are.
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.
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.
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.
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.
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.
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
In his new whitepaper, Best Practices for Amazon EMR, Parviz Deyhim outlines the best practices in using AWS EMR including moving data to AWS, strategies for collecting, compressing, aggregating the data, and common architectural patterns for setting up and configuring Amazon EMR clusters for processing.