This week, Midokura announced details of MEM 5.0, the next generation of its Midokura Enterprise MidoNet platform. As a network virtualization solution for IaaS clouds, MEM 5.0 boasts enhanced operational management functionality such as advanced analytics into the history of network flows through physical and virtual host machines. MEM 5.0 also provides data on network utilization by tenant in the form of usage reports that illustrate which tenants have consumed most network resources within a designated time period. In addition, the MEM Insights component of MEM 5.0 features analytics into bandwidth consumption by way of traffic counters that empower cloud operators to proactively monitor bandwidth consumption as well as port mirroring functionality that allows administrators to mirror devices such as ports, bridges and routers to identify anomalous behavior. Pino de Candia, CTO of Midokura, remarked on the innovation of this release as follows:
Operational tools are generally geared towards configurations, monitoring in OpenStack, but they offer no visibility into encapsulated traffic. From Midokura’s own experience as an operator, and by working with operators ourselves, we’ve seen firsthand the dire need for analytic and end-to-end operational tools for management of network infrastructure. Midokura Enterprise MidoNet 5.0 builds upon our popular technology to meet this need, making OpenStack far simpler to manage, operate and also troubleshoot.
As de Candia notes, MEM 5.0 delivers analytics into “encapsulated traffic” and simplifies the process of troubleshooting disruptive or anomalous network behavior within OpenStack deployments. Cloud operators who leverage Midokura to virtualize networks now have access to an enriched portfolio of operational reports and analytics that enables them to more effectively manage the performance of their network. Given that MEM 5.0 now comes replete with an enhanced set of tools for analyzing and remediating issues within network traffic, the network virtualization space for OpenStack features increasing competition as Midokura vies with the likes of Akanda, Juniper Networks and Plumgrid for market share amongst OpenStack cloud operators and IaaS cloud deployments more generally. The screenshot below illustrates the visualization of network-related analytics available through MEMInsights in MEM 5.0.
The screenshot below illustrates the visualization of network-related analytics available through MEMInsights in MEM 5.0.
MapR has been granted a patent from the USPTO for a converged data architecture that brings together “open source, enterprise storage, NoSQL, and event streams” with enterprise-grade security and disaster recovery functionality. MapR’s converged data architecture supports open source APIs such as POSIX, NFS, LDAP, ODBC, REST, and Kerberos while enabling real-time analytics on data in motion and data at rest. The platform delivers the power of Hadoop and Spark in conjunction with read-write and update functionality that can produce analytics for mission-critical applications and computationally intensive workloads, at scale. The MapR Converged Data Platform empowers customers to avoid data siloes by running analytics on multiple workloads housed within one cluster. Meanwhile, the platform’s enterprise-grade reliability allows customers to ingest, process and analyze big data from a multitude of sources while enjoying the benefits of production-grade data protection and disaster recovery. The innovation of the platform consists in its ability to support storage and analytics from a multitude of data formats and acquisition modalities such as batch uploads as well as streaming data. Wednesday’s patent announcement affirms the innovation specific to the architecture of MapR’s converged big data infrastructure. Expect to hear more details about MapR’s Converged Data Platform as use cases proliferate and differentially illustrate the platform’s ability to support big data analytics in mission critical environments for data from relational databases, NoSQL, Hadoop and streaming data sources, alike.
Google CEO Sundar Pichai recently announced that Gmail has surpassed 1 billion users per month and that its Google Cloud Platform is used by more than 4 million applications. Pichai also asserted that the Google Cloud Platform “is ready to be used at scale,” and that the company has reached a point where its cloud infrastructure and applications have reached a level of maturity at exactly the time when the broader, industry-wide “movement to cloud has reached a tipping point.” Pichai further noted that Catholic Health Initiatives, one of the nation’s largest non-profit health systems, announced its transition to Google Apps last quarter in what amounts to yet another example of the Google Cloud Platform’s readiness to embrace workloads from large organizations and enterprises. Unlike Microsoft and Amazon, Alphabet, Google’s parent company, failed to break out revenue run rate details about its subsidiary cloud business but the company’s appointment of VMware executive Diane Greene to head Google’s cloud services division in November constitutes ample proof of the company’s interest in building out its cloud business. The question now, however, is when and how Google plans to court the enterprise, which has traditionally been dominated by Microsoft and IBM in the enterprise software and infrastructure space. Without more details of its anticipated strategy for gaining traction for cloud products and services in the enterprise, investors and analysts alike will be hard pressed to understand how Google plans to build cloud market share, particularly given continued impressive revenue growth for Amazon Web Services and Microsoft’s growing ascendancy in the cloud products and services space under CEO Satya Nadella.
Redis Labs today announced the release of a Spark-Redis connector that accelerates the performance of Spark in comparison to other database infrastructures. The Spark-Redis connector allows Redis users to leverage the power of Spark to perform analytics on streaming data in real-time on large datasets. The open source Spark-Redis connector boasts the capability to read and write to Redis clusters while preserving Redis data structures. The integration of Redis and Spark results in Spark performance acceleration by a factor of 135 when compared to HDFS and a 45 fold acceleration when compared to Spark using Tachyon. Yiftach Shoolman, co-founder and CTO of Redis Labs, remarked on the significance of the acceleration of Spark on Redis data stores as follows:
Big data is coming of age and customers are demanding that big data insights are extracted in real-time. This is where Redis Labs fills the gap by delivering both the right performance and optimized distributed memory infrastructure to accelerate Spark. Our goal is to make Redis the de-facto data store for any Spark deployment.
Here, Shoolman comments on how the integration between Redis and Spark enhances the derivation of analytic insights from big datasets. The increase in Spark’s ability to perform on Redis allows users to conduct analyses in real-time while subsequently enjoying the performance the “optimized distributed memory infrastructure” that Redis delivers. In addition to benefits in speed, one of the key advantages of using Spark with Redis consists of the latter’s ability to allow Spark to access to individual data elements in ways that avoid the operational overhead associated with transferring or running analytics on large batches of data. Today’s announcement features news of a Spark-Redis connector, support for Spark SQL and the capability to use Redis as a distributed memory database for Spark. The Spark-Redis connector’s acceleration of Redis to blistering speeds promises to catapult the positioning of Redis within the NoSQL database landscape and the database infrastructure space more generally. By accelerating Spark to speeds over 100 times faster than its performance on HDFS, Redis gives customers faster access to data analytics on real-time in ways that can be crucial for use cases that demand split second analytic transactions on massive datasets. Going forward, Redis plans to collaborate with Spark to enable the use of Spark’s functionality for machine learning and graph database use cases as well. The graphic below illustrates the acceleration in speed enabled by the Spark-Redis connector as compared to other database infrastructures:
The key point about Microsoft’s earnings call from last week lies neither in the impressive numbers about growth in operating income and net income or earnings per share, but in the preliminary success of Nadella’s strategy to re-focus Microsoft around cloud technologies and cloud services. The earnings report revealed that revenue from intelligent cloud grew 5% to $6.3B with growth of 10% in cloud services revenue and products related to servers. Azure revenue increased by 140% in conjunction with a threefold growth of Azure premium services revenue, year over year. More than a third of Fortune 500 customers now use Microsoft’s Enterprise Mobility suite, which reported year over year growth by a factor of three. To put things in perspective, Intelligent cloud revenue of $6.3B comes close to the $6.7B in revenue that the company reported for Productivity and Business Processes spanning Office 365 and Dynamics revenue. Meanwhile, Microsoft’s annualized revenue run rate is now greater than $9.4B and well on track to achieve the company’s target of $20B. The company’s cloud revenue run rate of $9.4B represents a brisk increase from the $8.2B figure disclosed at its previous earnings call for the quarter ending October 31, 2015. Windows 10 now runs on more than 200 million devices, a sharp increase from the previous quarter that had reported Windows 10 usage on 110 million devices. All this means that, in the space of two years, Nadella has transformed a sluggish company that seemed to be reaching the end of its life span as a tech behemoth qua dinosaur into a company bursting with life, creativity, energy and ideas. The unified simplicity of Nadella’s cloud-first mission has caught the eye of investors and positioned the company to go head to head with Amazon Web Services for ownership of the enterprise cloud services space and cloud market share more generally. Expect more innovation to burst out of Microsoft in upcoming months as it builds on its release of the Azure Stack, consolidates on its release of Windows 10 and continues to catch the attention of the enterprise and consumers alike.
On Thursday, Amazon.com announced that Amazon Web Services generated $2.4B in revenue during Q4 of 2015, or year over year revenue growth of 69%. The Q4 year over year growth rate declined from Q3 and Q2 of 2015, which recorded year over year growth rates of 78% and 82% respectively. Nevertheless, Amazon Web Services brought in a whopping $7.88B for the entirety of 2015, meaning that it is now fundamentally an $8B business. Operating income for Q4 was $687M and approximately $1.9B for the entire year. The $687M in operating income for Q4 2015 constitutes an increase in comparison to $521M in Q3 and $240M for Q4 of 2014. Amazon’s announcement of $2.4B in quarterly revenue represents the highest quarterly revenue figure for Amazon Web Services in its history, exceeding the $2.08B recorded in Q3 of 2014. Despite impressive financials for Amazon Web Services, shares of Amazon tumbled on Thursday after news that it had missed its earnings estimates and continues to operate on razor thin margins. Regardless, Amazon Web Services remains the fastest growing business unit within Amazon.com.
Recommind today revealed details of the Axcelerate Cloud, the latest version of its SaaS eDiscovery platform built using the Axcelerate platform. The Axcelerate Cloud delivers business analytics that helps legal teams obtain insights into their operational processes. Built using a virtual private cloud on Amazon Web Services, the Axcelerate Cloud delivers a secure infrastructure for storing sensitive legal documents by means of data encryption at rest. In addition to enterprise-grade security, the platform provides granular visibility into the status of projects in ways that allow leaders to track metrics about the performance and progress of teams, resources, projects and portfolios. Moreover, customers have the ability to scale up or scale down the volume of data stored within the repository and concomitantly enjoy the benefits of predictable pricing as a result of a subscription-based pricing model tiered by data volume. This iteration of the Axcelerate Cloud features Efficiency Scoring marked by metrics and business intelligence that allow legal team leaders to obtain granular analytics about the status and performance of operational processes and teams. Efficiency scoring gives legal leaders deeper insight into their business processes that enables the optimization of business processes and the identification of high performing resources and teams. Additional benefits of efficiency scoring include the ability to replicate successful operational workflows and quantify achievements, thereby bringing enhanced visibility and performance management to the practice of legal research and analysis. By bringing robust, cloud-based security functionality, scalability, predictable pricing and visibility to the work specific to legal discovery and analysis, Recommind stands poised to consolidate its leadership position in the space of cloud-based solutions for the legal industry, particularly given its enhanced efficiency scoring and analytic capabilities. The Recommind Axcelerate Cloud solution is available as a managed or self-service offering via the cloud or an on premise deployment.