Kentik emerged from stealth to reveal details of a cloud-based platform for network visibility that delivers an exceptional degree of visibility into network performance and operations in comparison to existing products. Formerly known as CloudHelix, the Kentik direct service aggregates network-related data from customers into a non-relational database and subsequently performs analytics to give customers real-time visibility into traffic within their network infrastructures. Kentik allows customers to upload data directly to its SaaS platform or to a virtual appliance that encrypts the data prior to its transmission. The solution has gained early traction with customers such as Box and Yelp in addition to large internet service providers. Sam Eaton, Director of Engineering Operations at Yelp, remarked on the innovation enabled by Kentik as follows:
Kentik has given us the insight and visibility that we have not been able to achieve through other network performance monitoring products or open source tools. With over 100 million average monthly unique visitors coming to Yelp, we see traffic reaching many gigabits per second, so it’s critical for us to be able to look deeply into all network traffic in real time and gain real insight. That’s where Kentik has helped. We can see things we simply couldn’t see before.
Here, Eaton remarks on Kentik’s ability to deliver real-time, granular insights into Yelp traffic that exceeds “gigabits per second.” Similarly, Box.com leverages Kentik to obtain geography-specific insights into network traffic that allow for timely and proactive remediation of network-related problems before they intensify. Today, Kentik also announces the finalization of $12M in Series A funding led by August Capital. The company’s Big Data platform for aggregating and analyzing network data aims to disrupt networking analytics by delivering an unparalleled degree of insight into network behavior in ways that support high availability, performance optimization and security-related use cases.
On June 25, Redis Labs announced the finalization of $15M in a Series B funding round led by Bain Capital Ventures and Carmel Ventures. The funding raise validates the traction of Redis Labs and its cloud platform, the Redis Cloud, a fully managed cloud platform for running Redis databases. An open source, in-memory, key value data store, Redis boasts the capability to facilitate a high volume of read and write requests with sub-millisecond latency. The single threaded event-driven architecture of Redis in conjunction with its other protocols allows Redis to claim speeds that exceed its in-memory database competitors by 5-10x. Popular use cases for Redis involve leaderboards, session management and player profiles with reference to gaming applications. Other use cases include message queues, search engines and caching in highly interactive web applications that need to scale to tens of millions of users, each of which may have a unique user profile and representation in dynamically updated, application-related lists that represent how many “objects” are being “followed” or “saved” by a user. Last week’s funding raise brings the total capital raised by Redis Labs to $28M. With paying customers that include Hotel Tonight, Bleacher Report and Docker and a healthy infusion of Series B funding, Redis Labs stands poised to capitalize on its meteoric growth since the company was founded in 2011. Within a broader landscape of growing adoption of NoSQL databases and a proliferation of datacentric, mobile and web-based applications that require real-time updates that cascade across a constellation of database objects, expect Redis Labs to continue to drive innovation with respect to enterprise-grade Redis deployments and to spearhead Redis adoption more generally.
On June 15, Rancher Labs announced the availability in Beta of Rancher, an open source platform for managing infrastructure for containers. Rancher’s platform delivers enhanced visibility into infrastructure performance for environments that include Docker containers. The platform enables communication between containers residing on different hosts or cloud platforms in conjunction with elastic load balancing functionality that optimizes the distribution of resources across different containers. Additionally, the Rancher platform’s functionality includes storage management, resource management and service-discovery that allows for the discovery of services subsequent to container self-registration as services within a specific infrastructure. Sheng Liang, co-founder and CEO of Rancher Labs, remarked on the significance of the Rancher platform as follows:
Much of the excitement around Docker is its use as a universal packaging and distribution format. However, as users deploy containers across different infrastructures, they quickly realize that different clouds, virtualization platforms and bare metal servers have dramatically different infrastructure capabilities. By building a common infrastructure backplane across any resource, Rancher implements an entirely new approach to hybrid cloud computing.
Here, Liang elaborates on the way in which the Rancher platform supports the ability to “deploy containers across different infrastructures” and thereby undergird a technology ecosystem that enables communication between containers housed in different infrastructures. Unlike many of the existing container management platforms, Rancher Labs focuses on infrastructure in contrast to application performance management and, as such, represents one of the increasing number of vendors dedicated to cloud-based infrastructure management and visibility, albeit in this case in the context of container-based applications. The Rancher team features an impressive roster of talent including a leadership team that built Cloud.com, the company that morphed into the well known Apache CloudStack software platform. The Beta release of Rancher builds on the June 9 finalization of a $10M Series A capital raise from Mayfield and Nexus Venture Partners.
This week, Menlo Security emerged from stealth to reveal details of a security platform that leverages isolation technology to tackle malware and related threats to IT security. In contrast to IT security models that focus on perimeter defense or behavioral analytics to identify anomalous behavior, Menlo Security deploys an isolation platform to quarantine web-based applications in disposable containers as illustrated in the graphic below:
As the graphic illustrates, all content is housed within Menlo Security’s isolation platform, irrespective of whether it poses a security threat or not. Because the platform disposes of all content, security threats never reach endpoint devices or infrastructures. The platform removes malware from web traffic, attachments and email without requiring the installation of endpoint software. In addition to announcing details of its isolation platform, Menlo Security revealed the finalization of a Series B funding raise totaling $25M. The Series B funding raise was led by Sutter Hill Ventures and complemented by participation from General Catalyst and Osage Partners. The finalization of Menlo’s Series B round means that it has now raised a total of $35.5M, building on a November 2014 Series A funding round of $10.5M. The launch of its isolation platform promises to disrupt cloud security methodologies by delivering a model for disposing of malware without relying on analytics to detect deviations from “baseline” usage patterns or perimeter defense approaches. As such, Menlo’s isolation platform places it in a unique ballpark when it comes to cloud security and malware detection by taking the discussion about cloud security away from what is good and bad, toward a different model entirely.
On June 9, JethroData announced the finalization of an $8.1M Series B funding raise led by Square Peg Capital with additional participation from Pitango Venture Capital. The funding will be used to accelerate product development and sales and marketing initiatives for the company’s breakthrough SQL on Hadoop offering. As told to Cloud Computing Today in a phone interview with JethroData CEO Eli Singer, the company’s SQL on Hadoop platform differentiates itself from the landscape of SQL interfaces for Hadoop by way of its superior performance with respect to querying Hadoop-based data. The JethroData platform derives its exceptional performance by indexing Hadoop data and subsequently limiting queries only to rows of data containing the relevant indices. Rather than querying the entire cluster, JethroData’s queries hone in on the subset of Hadoop data containing the relevant index attributes and thereby delivers results in a fraction of the time required by its competitors. For the use case of constructing predictive modeling algorithms, however, JethroData queries the entire Hadoop dataset in question as opposed to a subset for the purpose of creating models that illustrate trends and variations in data points and various aggregations. The platform’s design supports interactive business intelligence applications that depend on rapid data refreshes for both their analytics and visualizations and, as such, integrates with the likes of Tableau, MicroStrategy and Qlik. Tuesday’s announcement of the company’s funding raise validates the company’s early traction and supports JethroData’s claim to deliver queries on the order of 100x faster than competing SQL on Hadoop platforms.
Today, Arcadia Data revealed details of its business intelligence and data visualization platform for Big Data. Arcadia Data’s BI platform enables business stakeholders to create data visualizations of Hadoop data by means of a rich user interface that allows users to drag and drop data fields. In addition, customers can select datasets for drill-downs to perform more advanced analyses such as root cause analytics, correlation analytics and trend analytics. The platform’s rich drag and drop functionality supports exploratory analysis of Hadoop-based data as illustrated below:
The graphic above shows how customers can use the Arcadia data platform to obtain different aggregations of cab ride fares and duration within various geographies in NYC. Importantly, the simplicity and speed of the platform mean that business stakeholders can comfortably obtain the analyses and data visualizations needed to represent their own data-driven insights. Given that the Arcadia Data platform also features data modeling functionality that enables users to massage and organize data prior to taking advantage of Arcadia’s data visualization functionality, the platform also lends itself to use by more savvy data users in addition to business users. Arcadia supports all major Hadoop distributions including Cloudera, Hortonworks and MapR and additionally enables users to glean insights from applications built using MySQL, Oracle and Teradata. In addition to today’s product announcement, Arcadia Data today announced the finalization of $11.5M in Series A funding from Mayfield, Blumberg Capital and Intel Capital. As revealed to Cloud Computing Today in a live product demonstration, the depth and sophistication of the Arcadia Data platform illustrates the changing face of business intelligence in the wake of the big data revolution, particularly as evinced by the ease with which business stakeholders can now make sense of Hadoop-based data using data visualization, transformation, drill-downs, trend analysis and analytics more broadly.