On September 21, Datadog announced the release of an Application Performance Management product to its core SaaS product for monitoring cloud infrastructures. Datadog’s Application Performance Management solution is designed to help developers understand and optimize application performance, with a specific focus on applications hosted in hybrid cloud, container or micro-services infrastructures. Datadog’s experience in infrastructure monitoring positions it strongly to enter an application performance management space featuring the likes of New Relic and AppDynamics. Moreover, the company’s expertise in monitoring cloud-based infrastructures renders it uniquely qualified to manage application performance on cloud platforms given the intersection between infrastructure and application performance as noted below by Datadog’s Chief Product Officer, Amit Agarwal:
Most APM tools on the market today are designed to troubleshoot coding issues in isolation. However, in modern cloud-scale applications, quickly resolving problems requires examining changes in both the infrastructure and code simultaneously.
Here, Agarwal remarks on the importance of examining application-related issues within the broader context of the infrastructure on which applications run. Application-related performance bottlenecks or other “coding issues,” Agarwal notes, should no longer be understood separately from their associated infrastructure components and instead require a framework that facilitates a holistic analysis of their root causes with attention to both the application and infrastructure layers. Given the dynamism specific to cloud computing infrastructures as evinced by instances, clusters and containers that are variously launched, spun down or assume different relationships with one another, Datadog’s specialization in cloud infrastructure monitoring and analytics positions it to take a leadership position in the application monitoring space, particularly given the proliferation of cloud-based applications and the concomitant inability of legacy application performance management frameworks to understand how the dynamic quality of cloud infrastructures variously impacts cloud application performance. Separate from its experience with cloud infrastructure monitoring, Datadog brings rich data visualization capabilities to the conversation about application performance management in addition to battle-tested analytics that take advantage of time-series analyses, correlative analytics and predictive analytics as illustrated below:
With $94.5M in Series D funding raised in January, and a whopping $148M in total funding raised to date, expect Datadog to richly build out its application performance management capabilities in conjunction with the progressive expansion of the adoption of its cloud monitoring platform. Meanwhile, Datadog’s release of its application performance management solution signals the intensification of a larger battle in the industry to build and refine a comprehensive infrastructure and application monitoring framework capable of managing the radical heterogeneity of infrastructures, applications, databases and orchestration frameworks specific to contemporary computing.
Pepperdata today announces a new product that helps Amazon EMR customers optimize the performance of their cloud-based, Hadoop jobs. Pepperdata with Amazon EMR delivers enhanced analytics related to the performance of jobs running on EMR data in addition to optimizing the performance of jobs in collaboration with instruction and feedback from users. The product gives Amazon EMR users granular visibility into cluster performance in conjunction with analytics on individual jobs that leverage metrics related to CPU, memory and unused capacity as illustrated by the graphic below:
Because Pepperdata translates its analytics into enhanced performance optimization on Amazon EMR, customers benefit from decreased cloud utilization as well as enhanced job performance. Sean Suchter, CTO of Pepperdata, remarked on the significance of PepperData’s product for Amazon EMR as follows:
Amazon EMR is designed to help companies process huge amounts of data easily and cost-effectively without having to commit unnecessary resources. As customers embrace Hadoop in the cloud they need to be able to manage cost and performance without any big surprises. Pepperdata eliminates those blind spots with very granular insight into the performance of current and historical EMR runs.
Here, Suchter comments on the ability of Pepperdata’s EMR product to enable customers to manage costs for Hadoop-related cloud resources while optimizing performance. Whereas Amazon Web Services EMR clusters terminate upon the completion of a run and subsequently make it difficult for users to access performance-related data, Pepperdata’s product for Amazon EMR allows users to analyze the performance of clusters and their constituent jobs even after the cluster has terminated. As a result, teams can analyze historical data to progressively improve cluster performance by determining the optimal amount of computing resources for cloud-based Hadoop jobs. Today, Pepperdata also announces the availability of Adaptive Scaling for EMR, a product that purchases Amazon EMR instances in accordance with budget and time constraints specified by clients. All told, today’s announcements from Pepperdata represent a notable addition to the space of products specializing in both infrastructure and application optimization for cloud-based Hadoop workloads. Expect to hear more from Pepperdata as big data adoption expands and companies increasingly turn their attention from deploying Hadoop clusters and their related applications toward the task of optimizing performance both at the level of clusters as well their associated jobs and applications.
Evernote, the company behind the widely used platform for note taking and capturing digital content, has announced plans to transition its hosting infrastructure to the Google Cloud Platform. The company’s decision to transition to the cloud comes after years of managing its own servers and networking infrastructure. As noted in a blog post, Evernote believes that its transition to the cloud will deliver “improvements in performance, security, efficiency, and scalability” that enable it to focus on enhancing the execution of its product roadmap in contrast to managing infrastructure. By hosting its platform on the Google Cloud Platform, Evernote hopes to accelerate updates to infrastructure and the roll-out of features to end users. Separate from the Google Cloud Platform’s ability to enhance its IT infrastructure, Evernote also hopes to take advantage of Google’s deep-learning technologies as noted below by Ben McCormack, Evernote’s VP of Operations:
In addition to scale, speed, and stability, Google will also give Evernote access to some of the same deep-learning technologies that power services like translation, photo management, and voice search. We look forward to taking advantage of these technologies to help you more easily connect your ideas, search for information in Evernote, and find the right note at the moment you need it. That’s exciting to us, and we’re already exploring some ideas that we think you’ll love.
Here, McCormack remarks on how its partnership with Google promises to enhance its product with the technologies responsible for much of the richness of Google’s translation, image management and voice search capabilities. As such, Evernote’s collaboration with Google stands to enrich its collaboration, search and storage capabilities and subsequently augment its collaboration and content management functionality. Evernote is working with Google to transition its software to the Google Cloud and plans to complete the migration by 2016. Evernote’s migration to the Google Cloud Platform illustrates the depth of the company’s transformation over the last year and additionally suggests the birth of exciting possibilities for product development that leverage Google’s machine learning capabilities. Meanwhile, Evernote’s decision to partner with Google as opposed to AWS or Microsoft Azure represents yet another coup for the Google Cloud Platform, which is steadily building out a roster of high-profile enterprise and startup customers under the leadership of Diane Greene.
Microsoft Azure has recovered from a service disruption related to Azure DNS that affected customers in all regions on September 15. The outage affected a subset of customers and additionally impacted Azure’s SQL Database, Virtual Machines, Visual Studio Team Services, Service Bus, API Management, and App Service\Web Apps. Available in preview, Azure DNS allows customers to host their DNS domains in Azure. The September 15 Azure DNS outage follows upon an Azure outage that affected customers in Europe on September 9 for several hours. Both outages are notable because they span multiple regions. Even though the Azure DNS outage is resolved as of the evening of September 15, customer concerns are beginning to proliferate about the reliability of Azure’s global architecture, particularly given that recent outages featured spillover effects to other services and products in conjunction with impacts that spanned multiple regions.
On September 8, Google announced its intent to acquire Apigee, a company that specializes in application programming interface (API) management. APIs are used to connect data from different applications, systems and databases to one another, thereby enhancing the ability of applications to share data and collaborate. Walgreens, for example, uses Apigee to empower developers to build apps using Walgreen APIs marked by the ability to use mobile apps to order photographs at the store of their choosing or submit prescription refills. Google’s acquisition of Apigee expands its ability to integrate APIs into the Google Cloud Platform, thereby enabling the Google Cloud Platform to integrate with a greater variety of applications and infrastructures. For starters, Google’s acquisition of Apigee positions the Google Cloud Platform to seamlessly integrate with Apigee’s impressive roster of customers including AT&T, Burberry and First Data, in addition to Walgreens. The acquisition of Apigee underscores Google’s commitment to building a developer-friendly cloud computing platform that empowers developers to create APIs either for new apps or to enrich existing the capability of existing apps. By giving developers freedom with respect to their choice of a development framework, in addition to an API marked by a battle-tested degree of security and stability, Apigee stands to augment the ease with which Google Cloud Platform and its constituent portfolio of products can integrate with the broader app development and infrastructure ecosystem. In a blog post, Diane Greene, the head of Google’s Cloud Business, noted that Google Cloud Platform plans to deepen its integration with Kubernetes in a further indication that Google plans to augment its efforts to court developers by enriching their experience with, and range of options associated with the Google Cloud Platform. Google will acquire Apigee for $625M or $17.40/share.
Oracle has acquired LogFire, an Atlanta-based company that developed a cloud-based platform that helps companies manage product inventory. LogFire will join Oracle Supply Chain Management (SCM), the supply chain management component of Oracle’s cloud business. LogFire’s cloud-based warehouse management platform delivers an integrated warehouse, inventory and workforce management platform that tracks products located in 250 stores. The platform empowers organizations to implement scalable warehouse management solutions that help them optimize their inventory management, fulfillment and sourcing practices. LogFire customers include Sears, Glad and Ryder rental trucks. Terms of the acquisition were not disclosed.
DigitalOcean today announces Hatch, a global online incubator that intends to help startups scale their operations and develop their products. Hatch provides startups with $100,000 in access credits toward the DigitalOcean cloud for a year in addition to training, mentorship and a community of other founders, advisors and investors. Currently in Beta, Hatch will work with 100 startups in the first 30 days of its launch while it enriches the set of offerings available to members of its incubator program and refines its process for onboarding other startups. Hatch aims to help startups decrease their expenses and subsequently empower them to focus on developing and selling their products. DigitalOcean’s online incubator Hatch reflects its history as a member of the TechStars accelerator program and its larger commitment to ensuring that startups focus on running their business instead of managing IT infrastructure as an IaaS platform geared toward the needs of developers and startups.