MemSQL places data into memory and translates SQL into C++ for the utmost optimization in query execution. This enables MemSQL to write and read data at incredible speeds, and by offering a relational interface, you can unify the data you’d normally store in a short-lived medium—cache or key-value store—and place it directly into a database along with your existing data.

This in-memory data store achieves the speed it claims with a unique concept where a query when fired from the system comes across a linear scan parser that strips of any numbering or string parameters and saves a skeleton of the query in plan cache. Any future query with the same query skeleton is accelerated directly through a hot code path to the pre-compiled execution engine.

MemSQL leverages a combination of lock-free data structures with an innovative coding translation process to efficiently retrieve and process data in memory. The solution is ideal for applications that require fast processing of machine data, including financial services, digital advertising, bioinformatics, government, manufacturing, and mobile applications.

MemSQL also announced today that the company has secured a total of $5 million in funding to date. The company’s investors are a combination of prominent venture capitalists and angels, including First Round Capital, IA Ventures, NEA, SV Angel, Y Combinator, Paul Buchheit, Ashton Kutcher, Max Levchin and Aaron Levie. MemSQL intends to use the funds to scale the company’s infrastructure across sales, marketing and technical support to take advantage of market opportunities where fast analysis of machine data is crucial.

Version 2.0 of the MapR Distribution includes advanced monitoring, management, isolation and security for Hadoop. It now enables control on hardware, software, storage, MapReduce and other MapR components. MapR Control System (MCS), a management interface displays this information in dozens of views, ranging from interactive histograms to time charts, allowing administrators to filter, aggregate and drill-down on individual jobs and tasks.

MapR Distribution for Hadoop is also now available as an option within the Amazon Elastic MapReduce service (EMR). The company also disclosed that AWS has made its own Hadoop enhancements available to MapR customers, allowing them to seamlessly use MapR with other AWS offerings such as Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB and Amazon CloudWatch.

Providing an option to choose MapR’s M3 or M5 hadoop distribution while launching Amazon EMR cluster, Customers can provision MapR clusters on-demand and automatically terminate them after finishing data processing, reducing costs as they only pay for the resources they consume. Customers can augment their existing on-premise deployments with AWS-based clusters to improve disaster recovery and access additional compute resources as required.

With Hadoop adoption on all time high so far this year with the launch of Cloudera CDH4, Hortoworks Data Platform, VMWare’s Project Serengeti, Teradata SQL-H bridging Hadoop with SQL world, all within just a span of 2 weeks, MapR has also joined the leads providing significant updates to its hadoop distribution.

“For many customers there is no longer a compelling business case for deploying an on-premise Hadoop cluster given the secure, flexible and highly cost effective platform for running MapR that AWS provides,” said John Schroeder, CEO and co-founder, MapR Technologies. “The combination of AWS infrastructure and MapR’s technology, support and management tools enables organizations to potentially lower their costs while increasing the flexibility of their data intensive applications.”

While the existing solutions so far relied on complex MapReduce batch jobs to process data stored within Hadoop Distributed File System (HDFS), Aster SQL-H creates a higher-level of abstraction by allowing ANSI standard SQL queries against Hadoop data. It leverages the power and flexibility of Aster SQL and SQL-MapReduce to provide business analysts with a low latency, interactive data discovery environment through their existing BI tools and SQL-MapReduce functions.

So far there was a bit of an impossibility due to short supply of combination of technical and analytical skills required to process big data within an enterprise. Such an integration that can process a combination of data stored both within Apache Hadoop and Aster Data platform along with traditional Business Intelligence software can prove game changing for enterprises.

SQL-H interfaces with the Apache HCatalog project to access data within apache Hadoop from Aster Platform. This Aster-managed communication with Hadoop nodes, to intelligently read just the data needed from Hadoop for SQL queries and SQL-MapReduce functions in Aster is the key part of this announcement. The company has given due credits to Hortonworks which was the major contributor for HCatalog project.

“Aster SQL-H provides value on two levels. This is the first time in the industry that standard query language or SQL access can be transparently and seamlessly provided for Hadoop data,” said Tasso Arygros, co-president, Teradata Aster. “Secondly, a lot of the unique assets and advantages of the Aster MapReduce platform, including our 50-plus pre-built MapReduce analytical applications and the patented SQL-MapReduce® interface are made available to analyze Hadoop data. The business value is huge – more analytics and better enterprise use of data at a fraction of the time and cost.”

Read more: http://www.sfgate.com/cgi-bin/article.cgi?f=/g/a/2012/06/14/prweb9600866.DTL#ixzz1xmov3TfC

Available for free download under the Apache 2.0 license, Serengeti is a “one-click” deployment toolkit that allows enterprises to leverage the VMware vSphere® platform to deploy a highly available Apache Hadoop cluster in minutes, including common Hadoop components like Apache Pig and Apache Hive.  By using Serengeti to run Hadoop on VMware vSphere, enterprises can easily leverage the high-availability, fault tolerance and live migration capabilities of the world’s most trusted, widely deployed virtualization platform to enable the availability and manageability of Hadoop clusters.

In addition, VMware said that it is working with the Apache Hadoop community including Cloudera, Greenplum, Hortonworks, IBM and MapR to contribute extensions that will make key components “virtualization-aware” to support elastic scaling and further improve Hadoop performance in virtual environments.

The organization is also said to be contributing changes to the Hadoop Distributed File System (HDFS) and Hadoop MapReduce projects to make them “virtualization-aware,” so that data and compute jobs can be optimally distributed across a virtual infrastructure. These changes will enable enterprises to achieve a more elastic, secure and high available Hadoop cluster. The extensions can be found here.

“Spring for Apache Hadoop” is also on the road, an open source project first launched in February of 2012 to make it easy for enterprise developers to build distributed processing solutions with Apache Hadoop. These updates allow Spring developers to easily build enterprise applications that integrate with the HBase database, the Cascading library, and Hadoop security. Spring for Apache Hadoop is free to downloadand available now under the open source Apache 2.0 license.