Tech News - Cloud Computing

Yesterday, the team at MongoDB announced new cloud services and features that will offer a better way to work with data. The beta versions of MongoDB Atlas Data Lake and MongoDB Atlas Full-Text Search will help users to access new features in a fully managed MongoDB environment. MongoDB Charts include embedded charts in web applications The general availability of MongoDB Charts will help customers in creating charts and graphs, and further building and sharing dashboards. It also helps in embedding these charts, graphs and dashboards directly into web apps for creating better user experiences. MongoDB Charts is generally available to Atlas as well as on-premise customers which help in creating real-time visualization of MongoDB data. The MongoDB Charts include new features, such as embedded charts in external web applications, geospatial data visualization with new map charts, and built-in workload isolation for eliminating the impact of analytics queries on an operational application. Dev Ittycheria, CEO and President, MongoDB, said, “Our new offerings radically expand the ways developers can use MongoDB to better work with data.” He further added, “We strive to help developers be more productive and remove infrastructure headaches --- with additional features along with adjunct capabilities like full-text search and data lake. IDC predicts that by 2025 global data will reach 175 Zettabytes and 49% of it will reside in the public cloud. It’s our mission to give developers better ways to work with data wherever it resides, including in public and private clouds.” MongoDB Query Language added to MongoDB Atlas Data Lake MongoDB Atlas Data Lake helps customers to quickly query data on S3 in any format such as BSON, CSV, JSON, TSV, Parquet and Avro with the help of MongoDB Query Language (MQL). One of the major plus points about MongoDB Query Language is that it is expressive and will that allows developers to query the data. Developers can now use the same query language across data on S3, and make querying massive data sets easy and cost-effective. With MQL being added to MongoDB Atlas Data Lake, users can now run queries and explore their data by giving access to existing S3 storage buckets with a few clicks from the MongoDB Atlas console. Since the Atlas Data Lake is completely serverless, there is no need for setting up an infrastructure or managing it. Also, the customers pay only for the queries they run when they are actively working with the data. The team has planned for the availability of MongoDB Atlas Data Lake on Google Cloud Storage and Azure Storage for the future. Atlas Full-Text Search offers rich text search capabilities Atlas Full-Text Search offers rich text search capabilities that are based on Apache Lucene 8 against fully managed MongoDB databases. Also, there is no need for additional infrastructure or systems to manage. Full-Text Search helps the end users in filtering, ranking, and sorting their data for bringing out the most relevant results. So, users are not required to pair their database with an external search engine To know more about this news, check out the official press release. 12,000+ unsecured MongoDB databases deleted by Unistellar attackers MongoDB is going to acquire Realm, the mobile database management system, for $39 million MongoDB withdraws controversial Server Side Public License from the Open Source Initiative’s approval process  

"We believe that the key to enabling cloud native applications, is to provide true portability and communication across disparate cloud infrastructure." Mark Hinkle, co-founder of TriggerMesh Yesterday, TriggerMesh- the open source multi-cloud service management platform- announced their open source project ‘Knative Lambda Runtime’ (TriggerMesh KLR). KLR will bring AWS Lambda serverless computing to Kubernetes which will enable users to run Lambda functions on Knative-enabled clusters and serverless clouds. Amazon Web Services' (AWS) Lambda for serverless computing can only be used on AWS and not on another cloud platform. TriggerMesh KLR changes the game completely as now, users can avail complete portability of Amazon Lambda functions to Knative native enabled clusters, and Knative enabled serverless cloud infrastructure “without the need to rewrite these serverless functions”. [box type="shadow" align="" class="" width=""]Fun fact: KLR is pronounced as ‘clear’[/box] Features of TriggerMesh Knative Lambda Runtime Knative is a  Google Cloud-led Kubernetes-based platform which can be used to build, deploy, and manage modern serverless workloads. KLR are Knative build templates that can be used to runan AWS Lambda function in a Kubernetes cluster as is in a Knative powered Kubernetes cluster (installed with Knative). KLR enables serverless users to move functions back and forth between their Knative and AWS Lambda. AWS  Lambda Custom Runtime API in combination with the Knative Build system makes deploying KLR possible. Serverless users have shown a positive response to this announcement, with most of them excited for this news. Kelsey Hightower, developer advocate, Google Cloud Platform, calls this news ‘dope’ and we can understand why! His talk at KubeCon+CloudNativeCon 2018 had focussed on serveless and its security aspects. Now that AWS Lambda functions can be run on Google’s Knative, this marks a new milestone for TriggerMesh. https://twitter.com/kelseyhightower/status/1083079344937824256 https://twitter.com/sebgoa/status/1083014086609301504 It would be interesting to see how this moulds the path to a Kubernetes hybrid-cloud model. Head over to TriggerMesh’s official blog for more insights to this news. Introducing Grafana’s ‘Loki’ (alpha), a scalable HA multi-tenant log aggregator for cloud natives; optimized for Grafana, Prometheus and Kubernetes DigitalOcean launches its Kubernetes-as-a-service at KubeCon+CloudNativeCon to ease running containerized apps Elastic launches Helm Charts (alpha) for faster deployment of Elasticsearch and Kibana to Kubernetes  

Alphabet’s ‘Chronicle’, launched last year, announced its first product, ‘Backstory’ at the ongoing RSA 2019. Backstory is a security data platform and stores huge amounts of business’ network data--including information from domain name servers to employee laptops and phones--into a Chronicle-installed collection of servers on a customer’s premises. This data is quickly indexed and organized. According to Forbes, customers can then carry out searches on the data, like “Are any of my computers sending data to Russian government servers?” Cybersecurity investigators can start asking questions such as: What kinds of information are the Russians taking, when and how?. This method of working is very similar to Google Photos. Backstory gives security analysts the ability to quickly understand the real vulnerabilities. According to the Backstory blog, “Backstory is a global security telemetry platform for investigation and threat hunting within your enterprise network. It is a specialized, cloud-native security analytics system, built on the core infrastructure that powers Google itself. Making security analytics instant, easy, and cost-effective.” The company states that this service requires zero customer hardware, maintenance, tuning, or ongoing management and can support security analytics against the largest customer networks with ease. Features of Backstory Backstory provides a real-time and retroactive instant indicator matching across all logs. It checks failure points such as if a domain flips from good to bad, Backstory shows all devices that have ever communicated with that domain). Prebuilt search results and smart filters designed for security-specific use cases. Displays data in real time to support security investigations and hunts. Backstory provides Intelligent analytics to derive insights to support security investigations. Backstory can automatically work with huge petabytes of data. Chronicle’s CEO Stephen Gillett told CNBC that the pricing model will not be based on volume. However, the licenses will be based on the size of the company and not on the size of the customer's data. Backstory also intends to partner with other cybersecurity companies rather than competing with them. Considering that Alphabet already has a history of obtaining sensitive customer information, it will be interesting to see how Backstory operates without this particular methodology. To know more about this news in detail, read Backstory’s official blog. Liz Fong Jones, prominent ex-Googler shares her experience at Google and ‘grave concerns’ for the company Google finally ends Forced arbitration for all its employees Shareholders sue Alphabet’s board members for protecting senior execs accused of sexual harassment  

Yesterday, Russia’s largest search engine, Yandex, launched its intelligent public cloud platform named Yandex.Cloud. This intelligent public cloud platform has been tested by more than 50 Russian and international companies since April. Yandex.Cloud is easy to use and offers flexible pricing with a pay per use pricing model. Also, the platform has an easy access to all the Yandex technologies, which makes it easy for companies to complement an existing IT infrastructure or even serve as an alternative to it. The platform will assist companies and industries of different sizes to boost their efficiency or expand their business without large-scale investment. Yandex plans to roll the Yandex.Cloud platform slowly, first to its users of Yandex services for business, and then to all by the end of 2018. It enables companies to store and use databases containing personal data in Russia, as required by law. Features of the ‘Yandex.Cloud’ public cloud platform A scalable virtual infrastructure The new intelligent public cloud platform includes a scalable virtual infrastructure having multiple management options. Users can manage from a graphical interface or the command line. It also includes developer tools for popular programming languages such as Python and Go Automated services Labour-intensive management tasks of popular databases systems such as PostgreSQL, ClickHouse (Yandex open source high-performance database management system) and MongoDB have been automated. AI-based Yandex services Yandex.Cloud includes AI based services such as a SpeechKit speech recognition and synthesis and Yandex.Translate machine translation. Yan Leshinsky, Head of Yandex.Cloud said, “Yandex has an entire ecosystem of successful products and services that are used by millions of people on a daily basis. Yandex.Cloud provides access to the same infrastructure and technologies that we use to power Yandex services, creating unique opportunities for any business to develop their products and services based on this platform.” To know more about Yandex.Cloud, visit its official website. Ansible 2 for automating networking tasks on Google Cloud Platform [Tutorial] Machine learning APIs for Google Cloud Platform Cloud Filestore: A new high-performance storage option by Google Cloud Platform

Cloudera and Hortonworks have announced a corporate partnership to jointly become a data platform provider, spanning multi-cloud, on-premises and the Edge. They will also accelerate innovation in IoT, streaming, data warehouse, and Artificial Intelligence. This merger will also expand market opportunities for Hortonworks DataFlow and Cloudera Data Science Workbench along with partnerships with public cloud vendors and systems integrators. Tom Reilly, chief executive officer at Cloudera, called their merger as highly complementary and strategic. He said, “By bringing together Hortonworks’ investments in end-to-end data management with Cloudera’s investments in data warehousing and machine learning, we will deliver the industry’s first enterprise data cloud from the Edge to AI.” Rob Bearden, chief executive officer of Hortonworks agrees saying that, “Together, we are well positioned to continue growing and competing in the streaming and IoT, data management, data warehousing, machine learning/AI and hybrid cloud markets.” The terms of the transaction agreement are: Cloudera stockholders will own approximately 60% of the equity of the combined company. Hortonworks stockholders will own approximately 40% of the equity of the combined company. Hortonworks stockholders will receive 1.305 common shares of Cloudera for each share of Hortonworks stock owned, which is based on the 10-day average exchange ratio of the two companies’ prices through October 1, 2018. The companies have a combined fully-diluted equity value of $5.2 billion based on closing prices on October 2, 2018. This merger is expected to generate significant financial benefits and improved margin profile for both the companies which includes: Approximately $720 million in revenue More than 2,500 customers More than 800 customers over $100,000 ARR More than 120 customers over $1 million ARR More than $125 million in annual cost synergies More than $150 million cash flow in CY20 Over $500 million cash, no debt Read more about the announcement on the Hortonworks blog. Hortonworks Data Platform 3.0 is now generally available. Hortonworks partner with Google Cloud to enhance their Big Data strategy. Cloudera Altus Analytic DB: Modernizing the cloud-based data warehouses.

Early this week, the Kubeflow project launched its latest version- Kubeflow 0.3, just 3 months after version 0.2 was out. This release comes with easier deployment and customization of components along with better multi-framework support. Kubeflow is the machine learning toolkit for Kubernetes. It is an open source project dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Users are provided with a easy to use ML stack anywhere that Kubernetes is already running, and this stack can self configure based on the cluster it deploys into. Features of Kubeflow 0.3 1. Declarative and Extensible Deployment Kubeflow 0.3 comes with a deployment command line script; kfctl.sh. This tool allows consistent configuration and deployment of Kubernetes resources and non-K8s resources (e.g. clusters, filesystems, etc. Minikube deployment provides a single command shell script based deployment. Users can also use MicroK8s to easily run Kubeflow on their laptop. 2. Better Inference Capabilities Version 0.3 makes it possible to do batch inference with GPUs (but non distributed) for TensorFlow using Apache Beam.  Batch and streaming data processing jobs that run on a variety of execution engines can be easily written with Apache Beam. Running TFServing in production is now easier because of the Liveness probe added and using fluentd to log request and responses to enable model retraining. It also takes advantage of the NVIDIA TensorRT Inference Server to offer more options for online prediction using both CPUs and GPUs. This Server is a containerized, production-ready AI inference server which maximizes utilization of GPU servers. It does this by running multiple models concurrently on the GPU and supports all the top AI frameworks. 3. Hyperparameter tuning Kubeflow 0.3 introduces a new K8s custom controller, StudyJob, which allows a hyperparameter search to be defined using YAML thus making it easy to use hyperparameter tuning without writing any code. 4. Miscellaneous updates The upgrade includes a release of a K8s custom controller for Chainer (docs). Cisco has created a v1alpha2 API for PyTorch that brings parity and consistency with the TFJob operator. It is easier to handle production workloads for PyTorch and TFJob because of the new features added to them. There is also support provided for gang-scheduling using Kube Arbitrator to avoid stranding resources and deadlocking in clusters under heavy load. The 0.3 Kubeflow Jupyter images ship with TF Data-Validation. TF Data-Validation is a library used to explore and validate machine learning data. You can check the examples added by the team to understand how to leverage Kubeflow. The XGBoost example indicates how to use non-DL frameworks with Kubeflow The object detection example illustrates leveraging GPUs for online and batch inference. The financial time series prediction example shows how to leverage Kubeflow for time series analysis The team has said that the next major release:  0.4, will be coming by the end of this year. They will focus on ease of use to perform common ML tasks without having to learn Kubernetes. They also plan to make it easier to track models by providing a simple API and database for tracking models. Finally, they intend to upgrade the PyTorch and TFJob operators to beta. For a complete list of updates, visit the 0.3 Change Log on GitHub. Platform9 announces a new release of Fission.io, the open source, Kubernetes-native Serverless framework Introducing Alpha Support for Volume Snapshotting in Kubernetes 1.12 ‘AWS Service Operator’ for Kubernetes now available allowing the creation of AWS resources using kubectl    

Juniper networks, one of industry leads in automated, scalable and secure networks, today announced fifth generation of it’s MX Series 5G Universal Routing Platform. This series has more offerings for cutting-edge infrastructure and technology like cloud and IoT, enabling high-level network programmability. It has improved the programmability, performance and flexibility, for rapid cloud deployment by introducing a new set of software. This platform supports complex networks and service-intensive applications such as secured SD-WAN-based services and so on. Executive vice president and chief product officer at Juniper Networks, Manoj Leelanivas, said “ Cloud is eating the world, 5G is ramping up, IoT is presenting a host of new challenges, and security teams simply can’t keep up with the sheer volume of cyber attacks on today’s network. One thing service providers should not have to worry about among all this is the unknown of what lies ahead.” Few highlights of this release are as follows: Juniper Penta Silicon Penta silicon is considered the heart of the 5G platform which is next-generation 16 nm service-optimized, having a packet-forwarding engine that delivers upto 50% power efficiency over existing Junos trio chipset. Pena silicon has native support to MACsec and IPsec crypto engine that enables end to end secure connectivity at scale. In addition to this, Penta silicon also supports flexible native Ethernet (FlexE). MX 5G Control User-Plane Separation (CUPS) The 3GPP CUPS standard allows the customer to separate the evolved packet core user plane (GTP-U), and control plane (GTP-C) with standard interface to help service providers scale each independently as needed. The MX Series 5G platform is the first networking platform to support a standard-based hardware accelerated 5G user-plane in both existing and future MX routers. It enables converged services (wireless and wireline) on the same platform while also allowing integration with third-party 5G control planes. MX10008 and MX10016 Universal Chassis MX series continues to do innovations in the area of cloud, enterprise networking, and previously announced PTX and QFX Universal Chassis gains two new MX variants with today’s announcement: MX10008 and MX10016. A variety of line cards and software are available to satisfy specific networking use cases across the data center, enterprise and WAN. Refer to the official Juniper website for details on MX Series 5G. Five developer centric sessions at IoT World 2018 Cognitive IoT: How Artificial Intelligence is remoulding Industrial and Consumer IoT Windows 10 IoT Core: What you need to know  

In a post yesterday, the AWS Developer team has announced that AWS Lambda support will be provided for PowerShell Core 6.0. Users can now execute PowerShell Scripts and functions in response to Lambda events. Why should Developers look forward to this upgrade? The AWS Tools for PowerShell will allow developers and administrators to manage their AWS services and resources in the PowerShell scripting environment. Users will be able to manage their AWS resources with the same PowerShell tools used to manage Windows, Linux, and MacOS environments. These tools will let them perform many of the same actions as available in the AWS SDK for .NET. What’s more is that these tools can be accessed from the command line for quick tasks. For example: controlling Amazon EC2 instances. The PowerShell scripting language composes scripts to automate AWS service management. With direct access to AWS services from PowerShell, management scripts can take advantage of everything that the AWS cloud has to offer. The AWS Tools for Windows PowerShell and AWS Tools for PowerShell Core are flexible in handling credentials including support for the AWS Identity and Access Management (IAM) infrastructure. To understand how the support works, it is necessary to set up the appropriate development environment as shown below. Set up the Development Environment This can be done in a few simple steps- 1. Set up the correct version of PowerShell 2. Ensure Visual Studio Code is configured for PowerShell Core 6.0. 3. PowerShell Core is built on top of .NET Core hence install .NET Core 2.1 SDK 4. Head over to the PowerShell Gallery and install AWSLambdaPSCore module The module provides users with following cmdlets to author and publish Powershell based   Lambda functions- Source: AWS Blog You can head over to the AWS blog for detailed steps on how to use the Lambda support for PowerShell. The blog gives readers a simple example on how to execute a PowerShell script that ensures that the Remote Desktop (RDP) port is not left open on any of the EC2 security groups. How to Run Code in the Cloud with AWS Lambda Amazon hits $1 trillion market value milestone yesterday, joining Apple Inc Getting started with Amazon Machine Learning workflow [Tutorial]

Hybrid cloud is becoming an increasing reality for many businesses. This is something the software world is only just starting to acknowledge. However, at this year's Google Cloud Next, Google does seem to be making a play for the hybrid market. Its new Cloud Services Platform combines a number of tools, including Kubernetes and Istio, to support a hybrid cloud solution. In his speech at Google Cloud Next, Urs Holze, Senior VP of technical infrastructure, said that although cloud computing offers many advantages, it's "still missing something... a simple way to combine the cloud with your existing on-premise infrastructure or with other clouds." That's the thinking behind Cloud Services Platform, which brings together a whole host of tools to make managing a cloud potentially much easier than ever before. What's inside Google's Cloud Services Platform In a blog post Holze details what's going to be inside Cloud Services Platform: Service mesh: Availability of Istio 1.0 in open source, Managed Istio, and Apigee API Management for Istio Hybrid computing: GKE On-Prem with multi-cluster management Policy enforcement: GKE Policy Management, to take control of Kubernetes workloads Ops tooling: Stackdriver Service Monitoring Serverless computing: GKE Serverless add-on and Knative, an open source serverless framework Developer tools: Cloud Build, a fully managed CI/CD platform This diagram provides a clear illustration of how the various components of the Cloud Services Platform will fit together: [caption id="attachment_21065" align="aligncenter" width="960"] What's inside Google's Cloud Services Platform (via cloudplatform.googleblog.com)[/caption] Why Kubernetes and Istio are at the center of the Cloud Services Platform Holze explains the development of cloud in the context of containers. "The move to software containers", he says, "has helped some [businesses] in simplifying and speeding up how we package and deliver software." Kubernetes has been  an integral part of this shift. And although Holze has a vested interest when he says that "today it's by far the most popular way to run an manage containers," he's ultimately right - Kubernetes is one of the fastest growing open source projects on the planet. Read next: The key differences between Kubernetes and Docker Swarm Holze then follows on from this by introducing Istio. "Istio extends Kubernetes into these higher level services and makes service to service communications secure and reliable in a way that's very easy on developers." Istio is due to hit its first stable release in the next couple of days. So, insofar as both Istio and Kubernetes make it possible to manage and monitor containers at scale, bringing them together in a single platform makes for a compelling proposition for engineers. The advantage of being able to bring in tools like Kubernetes and Istio might make hybrid cloud solutions a much more attractive proposition for business and technology leaders - and for those already convinced, it could make life even better. According to Chen Goldberg, Google's Director of Engineering, speaking to journalists and Google Cloud Next, Cloud Services Platform "allows you to modernize wherever you are and at your own pace." Whether businesses buy into Google's vision remains to be seen - but it could well be a game-changer that threatens AWS dominance in the cloud world.  Read next: Go Cloud is Google’s bid to establish Golang as the go-to language of cloud Google Cloud Next: Fei-Fei Li reveals new AI tools for developers Dispelling the myths of hybrid cloud

Yesterday, Twilio announced the general availability of Flex. Since its preview announcement in March, Flex has been used by thousands of contact center agents including support and sales teams at Lyft, Scorpion, Shopify, and U-Haul. Twilio Flex is a fully-programmable contact center platform that aims to give businesses complete control over customer engagement. It is a cloud-based platform that provides infinite flexibility in your hands. What functionalities does Flex provide to enterprises? Twilio Flex enables enterprises to do the following: Answer user queries using Autopilot Flex provides a conversational AI platform called Autopilot using which businesses can build custom messaging bots, IVRs, and home assistant apps. These bots are trained with the data pulled by Autopilot using Twilio’s natural language processing engine. Companies can deploy those bots across multiple channels including voice, SMS, Chat Alexa, Slack, and Google Assistant. With these bots, enterprises can also respond to frequently asked questions and if the queries become complex the bots can then transfer the conversation to a human agent. Secure phone payment with Twilio Pay With only one line of code, you can activate the Twilio Pay service that provides businesses the tools needed to process payments over the phone. It relies on secure payment methods such as tokenization to ensure that credit card information is securely handled. Provide a true omnichannel experience Flex gives enterprises access to a number of channels out of the box including voice, SMS, email, chat, video, and Facebook Messenger, among others. Also, agents can switch from channel to channel without losing the conversation or context. Customize user interface programmatically Flex user interfaces are designed with customization in mind. Enterprises can customize the customer-facing components like click-to-call or click-to-chat. It also allows adding entirely new channels or integrating new reporting dashboards to display agent performance or customer satisfaction. Integrate any application Enterprises can integrate their third-party business-critical applications with Flex. These applications may include systems such as customer relationship management (CRM), workforce management (WFM), reporting, analytics, or data stores. Analytics and insights for better customer experience It offers real-time event stream, a supervisor desktop, and admin desktop, which gives supervisors and administrators complete visibility and control over interaction data. Using these analytics and insights they will be able to better monitor and manage an agent’s performance. To know more about Twilio Flex, check out their official announcement. Twilio acquires SendGrid, a leading Email API Platform, to bring email services to its customers Twilio WhatsApp API: A great tool to reach new businesses Building a two-way interactive chatbot with Twilio: A step-by-step guide