On October 8, Amazon Web Services announced the release of AWS IoT, its platform for Internet of Things data, at its AWS re:Invent conference in Las Vegas. The AWS IoT platform facilitates the connection of devices such as sensors, automobiles and appliances to enable data acquisition of internet of things data, the application of rules to ingested data and the ability to remain connected to devices, even when they are offline. Devices connect to AWS IoT by means of a Device Gateway that leverages the HTTP and Message Queue Telemetry Transport protocol for sensors and mobile devices. Upon ingestion, the AWS IoT rules engine allows customers to create rules to route and filter data and send alerts and notifications to connected devices and related applications. The AWS IoT rules engine empowers customers to route data to the appropriate AWS product (Redshift, Kinesis, S3, Lambda, etc.) for long-term storage and analytics. Devices can stay connected to AWS IoT even when they are offline by means of the platform’s ability to create a virtual, “shadow” version of the device that enables other devices to interact with the latest available data for that device. The AWS IoT platform builds upon Amazon’s acquisition of 2lemetry in March 2015 and represents a much awaited addition to the cloud behemoth’s portfolio, particularly given the proliferation of competing products from Microsoft, Pivotal and ParStream. Expect AWS to integrate advanced analytics into its IoT platform that enhance the insights currently available via its rules engine in upcoming months.
Datagres Announces PerfAccel For Couchbase Server For Storage Performance Management Featuring Granular I/O Analytics
Datagres this week announced the general availability of PerfAccel for Couchbase Server, its performance management solution for Couchbase’s NoSQL database storage platform. The PerfAccel storage management platform now boasts enhanced performance, improved analytics, an upgraded user interface and support for a broader range of storage infrastructures. In the case of Couchbase, PerfAccel delivers deep visibility into real-time I/O operations regarding storage performance and its intersection with the application layer as well as the underlying hardware. Couchbase customers can use PerfAccel to obtain real-time analytics on every Couchbase I/O to facilitate diagnosis of application bottlenecks or related issues by identifying root causes at the storage level and subsequently implementing prescriptive solutions to remediate the problem at hand. Unlike traditional application performance management vendors, PerfAccel focuses on the storage layer and delivers analytics with a granularity that facilitate increased IOPS, reduced latency and faster applications. The solution also features intelligent, auto-tiering of storage data to optimize application performance by reducing the time required for reads and writes between compute and storage depending on the frequency with which data is used. Overall, PerfAccel delivers performance improvements that result in cost savings of 50 to 80% by empowering customers to leverage data-driven analytics to build and manage big data applications marked by low latency and high throughput. The platform features a deep integration with Couchbase Server 4.0 marked by an exceptional level of granularity regarding Couchbase I/O operations to optimize performance and reduce operational costs as illustrated by the architecture diagram below:
On October 6, application delivery vendor Appcito announced the addition of one-click provisioning of security rules for applications that run on the Amazon Web Services cloud. Appcito’s CAFÉ platform features load balancing, application security and application-related analytics via an application delivery architecture marked by limitless scalability. Appcito’s announcement of the ability to deploy security rules for their AWS applications by means of one click streamlines and simplifies the deployment of security rules and parameters. Customers can choose from a pre-defined library of security rules for popular frameworks such as WordPress, Joomla and SharePoint. Appcito’s pre-defined library of security rules empowers customers to deploy the appropriate security configuration for their application without extensive experimentation and testing. Moreover, Appcito updates rules daily to ensure their efficacy against the latest and greatest security threats. The security platform includes a web application firewall, Layer 4 Network Protection, Layer 7 DDoS, IP blacklisting and Bot protection. In addition to protection against security threats, the Appcito platform provides security analytics that allow users to customize their security rules and algorithms. The uniqueness of this week’s announcement, however, consists in Appcito’s ability to provision security rules with one click from a pre-defined library of security templates. Given the bewildering heterogeneity of cloud application security offerings in the contemporary software security landscape, the Appcito platform differentiates by way of its ability to simplify the process of adding security to cloud-based applications while concurrently offering customers a trove of rich analytics that they can mine to customize their deployment to respond to specific security threats and exigencies as illustrated below:
This week, Avere Systems announced Avere CloudFusion, a scalable file storage application for Amazon Web Services. Avere CloudFusion’s proprietary analytics place the most frequently used, hot data on Amazon EC2 RAM while warm data is assigned to Amazon Elastic Block Storage (EBS) and cold data is assigned to Amazon S3 in order to most effectively leverage the economics of the public cloud. Avere Systems proprietary algorithms automate the assignment of datasets to hot, medium and cold storage tiers while updating those same designations in real-time in conjunction with changes in the pattern of data usage and retrieval. CloudFusion’s automated assignment of storage data to different storage infrastructures enables it to deliver storage-related cost reductions of up to 90% in comparison with competing NAS solutions for AWS. Meanwhile, its NAS technology renders available a plethora of use cases for AWS customers such as the storage of website data and the enablement of cloud computing featuring real-time, bi-directional reads and writes between EC2 and Avere CloudFusion’s NAS infrastructure. Notably, CloudFusion makes the economics of the public cloud even more palatable for customers with smaller datasets by means of CloudFusion’s Edge Filer technology for analytics and data pipelines between EC2 compute resources and S3 storage. As such, Avere CloudFusion gives Amazon Web Services customers the opportunity to take advantage of AWS cloud computing while enjoying the economies of scale typically associated with big data processing and storage.
Basho today announces Basho Riak TS, a distributed, NoSQL database designed for the internet of things and massive amounts of streaming, unstructured data. Importantly, Riak TS is optimized to support time series analytics marked by the ability to ingest and analyze data from sensors, logs, financial data, user activity and performance and risk metrics. The ability of Basho TS to support time series data involves co-location functionality that ensures data from the same time resides on the correct node to enable the most accurate analysis, as well as definitions of fields and tables that support structured and semi-structured data. Basho Riak TS also supports SQL-based queries as well as an integration with Apache Spark to further support analytics on streaming data. Like its counterpart Basho KV, Riak TS delivers massive scalability and high availability to enable production-grade usage on datasets that require time-series analytics. Basho Riak TS also integrates with the Basho Data Platform, an integrated Big Data platform that empowers customers to create high performing applications for real-time analytics. All told, Basho Riak TS marks a notable addition to Basho’s portfolio of Big Data products as represented by the optimization of its design for time series analytics and its concomitant ability to embrace analytic use cases for the internet of things.
Puppet Labs recently announced Puppet Application Orchestration, a platform that helps customers orchestrate and manage distributed applications. Puppet Application Orchestrator allows customers to model applications as code, thereby enhancing the operational agility of DevOps teams by enhancing their ability to deploy and operationalize applications. Puppet Application Orchestration enables customers to model dependencies between applications, database stacks and infrastructure components. Moreover, the Puppet Application Orchestration platform can model applications as code across multiple nodes and distributed infrastructures and subsequently map inter-dependencies for a distributed application to expedite application updates, performance monitoring and troubleshooting. Puppet Application Orchestration represents an extension of Puppet’s renowned infrastructure management tools that empowers customers to model their deployments all the way from infrastructure to application as code, thereby giving them an unprecedented degree of agility into full stack management.
Luke Kanies, founder and CEO of Puppet Labs, remarked on the significance of Puppet Application Orchestration as follows:
Over the past several years, Puppet’s model-based approach has become the standard for modern infrastructure management. Puppet Application Orchestration is a direct extension of our existing strengths and technologies, adding new tools and capabilities that give our customers a full management stack, from bare metal all the way up to modern distributed applications. It marks a huge step forward for the industry, and it’s just the beginning of another decade of innovation from Puppet Labs. Because Application Orchestration is built on the core concepts underlying our past 10 years of success, and can use any of the 3,500 public Forge modules, any team using Puppet has an unfair advantage over competitors who can’t deploy as quickly.
Puppet Application Orchestration marks a breakthrough innovation for contemporary application development given the complexity of distributed applications and their dependencies on infrastructures that often span a combination of on-premise, private cloud, public cloud and container environments. The uniqueness of Puppet Application Orchestration is that it works in conjunction with Puppet’s battle-tested framework for infrastructure management and orchestration and thereby positions Puppet Labs as a leader in application orchestration by empowering DevOps teams to optimize application performance, upgrades, monitoring and control. Puppet Application Orchestration’s ability to model applications as code also facilitates migrations of applications from one deployment to another by enabling the identification of inter-dependencies and critical pathways that need to be addressed in an application migration from, for example, an on-premise infrastructure to the public cloud. The innovation of the platform consists in its ability to model applications as opposed to conducting application orchestration by means of sequencing discrete actions. With an application infrastructure platform that builds on its well known infrastructure orchestration framework, expect Puppet to continue consolidating its market traction amongst enterprise customers and building on its unique position in full stack orchestration and management.
On October 1, NodePrime emerged from stealth with $7M in seed funding from Menlo Ventures, NEA, Ericsson, Crosslink Capital and other investors. NodePrime’s software defined technology empowers data center operators to obtain real-time, granular visibility into data center operations toward the end of more effectively managing enterprise infrastructures. In addition to delivering insight into data center performance, NodePrime facilitates automation and optimization of data center operations by means of a holistic picture of distributed data centers through metrics, log data, inventory data, machine learning, advanced analytics as noted in the video clip below: