Governor Brian Kemp formed a joint initiative on Wednesday between Georgia’s Technical College System, Georgia’s Ministry of Education, Georgia Technology Association, and Amazon Web Services to train 5,500 learners across the state in cloud computing. Announced that it will provide education.

Through this initiative, Georgia high schools and colleges of technology offer cloud computing courses and qualifications tailored to the skills needed to pursue a demanding technical career.

“Georgia’s technology industry is growing rapidly and offers exciting job opportunities for Georgians,” said Kemp. “This collaboration with AWS will give citizens access to innovative training and education to prepare for technical jobs in Georgia.”

The state is working with the AWS Academy program to provide educational institutions with a free, out-of-the-box cloud computing curriculum that prepares students for industry-recognized AWS certifications and high-demand cloud jobs.

Students can also access self-paced online training courses and labs from AWS.

“The use of cloud-based technology is growing rapidly in Georgia, which means there is a growing demand for a skilled workforce in this area,” said TCSG Commissioner Greg Dozier. .. “We are pleased to begin this partnership to provide students and existing information technology workers with the latest training available to ensure success in this growing area.”

SAN FRANCISCO–(BUSINESS WIRE)–Element, a leading software provider in IT/OT data management for industrial companies, today announced a new offering featuring an API integration between its Element Unify product and AWS IoT SiteWise, a managed service from Amazon Web Services, Inc. (AWS), that makes it easy to collect, store, organize, and monitor data from industrial equipment at scale. The API integration is designed to give customers the ability to centralize plant data model integration and metadata management, enabling data to be ingested into AWS services, including AWS IoT SiteWise and Amazon Simple Storage Service (S3) industrial data lake.

Available in AWS Marketplace, the Element Unify AWS IoT Sitewise API integration is designed to allow engineers and operators to monitor operations across facilities, quickly compute performance metrics, create applications that analyze industrial equipment data to prevent costly equipment issues, and reduce gaps in production.

“We are looking forward to bridging the on-premises data models we’ve built for systems like OSIsoft PI to AWS for equipment data monitoring using Element Unify,” said Philipp Frenzel, Head of Competence Center Digital Services at Covestro.

Element also announced its ISO 27001 certification proving both the security controls protect customer data and the Information Security Management System (ISMS) provide governance, risk management, and controls required for modern SaaS applications. Element Unify also supports AWS PrivateLink to provide an additional level of network security and control for customers.

“Our customers are looking for solutions that can help them improve equipment uptime, avoid revenue loss, cut O&M costs, and improve safety,” said Prabal Acharyya, Global Head of IoT Partners for Energy at AWS. “Now, the Industrial Machine Connectivity (IMC) on AWS initiative, along with Element Unify, makes possible a seamless API integration of both real-time and asset context OT data from multiple systems into an industrial data lake on AWS.”

Industrial customers need the ability to digitally transform to maximize productivity and asset availability, and lower costs in order to remain competitive. Element Unify, which aligns IT and operational technology (OT) around contextualized industrial IoT data, delivers key enterprise integration and governance capabilities to industrials. This provides them with rich insight, enabling smarter, more efficient operations. By integrating previously siloed and critical time-series metadata generated by sensors across industrial operations with established IT systems, such as Enterprise Asset Management, IT and OT teams can now easily work together using context data for their entire industrial IoT environment.

“The tie-up between Element and AWS IoT SiteWise not only enhances AWS’s ability to manage IT and OT data together in an industrial data lake, but further demonstrates Element’s leading DataOps infrastructure solution for industrial companies,” said Joe Perino, Principal Analyst for LNS Research.

Though the API integration, AWS IoT SiteWise customers can now:

Centralize plant data model integration and metadata management into a single contextual service for consumption by AWS IoT SiteWise.
Ingest data directly into AWS services, including AWS IoT SiteWise and Amazon S3 data lake.
Integrate and contextualize metadata from IT/OT sources in single-site, multi-site, and multi-instance deployments, and deploy the data model(s) to AWS IoT SiteWise.
Enable metadata to be imported into AWS IoT SiteWise from the customers systems through Inductive Automation Ignition and PTC KEPServerEX to create context data.
Keep both greenfield and brownfield AWS IoT SiteWise asset models and asset hierarchies updated as the Element Unify models adapt to changes in the underlying data.
Close the gap between raw, siloed, and disorganized IT/OT data and enriched, contextualized, and structured data that can be easily paired with business intelligence (BI), analytical tools, and condition monitoring tools like AWS IoT SiteWise.
Empower the user to build complex asset data models with ease.
Build and deploy data models to AWS IoT SiteWise and keep them up to date with changes happening in source systems.
Easily create the underlying data models to power AWS IoT SiteWise and many other analytical systems including Amazon SageMaker, Amazon QuickSight, and Amazon Lookout for Equipment.
“Operations data must be easy to use and understand in both the plant and in the boardroom. Industrial organizations who transform their plant data into context data for use in AWS IoT SiteWise, and other AWS services, will drive greater operational insights and achieve breakthrough business outcomes,” said Andy Bane, CEO of Element.

Element Analytics

Element is a leading software provider in IT/OT data management, digitally enabling operations for industrial organizations. Element Unify transforms siloed IT/OT data into context data by enabling contextualize once, compute anywhere, consume everywhere data that is used to achieve cleaner, safer, healthier, more profitable operations. Element’s customers represent over $750 billion in revenue, $500 billion in fixed assets and 450,000 employees.

Nvidia Corp kicked off its annual GTC Technology conference on Monday with some big new product announcements, including the latest version of its DRIVE autonomous vehicle platform that will transform self-driving vehicles into “supercomputers on wheels.”

During today’s opening keynote of the annual GPU Technology Conference, NVIDIA founder and CEO Jensen Huang unveiled NVIDIA DRIVE Atlan, the upcoming generation of its AI-powered compute platform for autonomous vehicles. Atlan centralizes the vehicle’s entire computing infrastructure into a single system-on-a-chip (SoC).

Atlan will be able to handle the large number of applications and deep neural networks that run simultaneously in autonomous vehicles.

Nvidia said its next-generation Atlan platform will achieve an unprecedented 1,000 trillion operations per second (1,000 TOPS) of performance and an estimated SPECint benchmark score of more than 100, which is greater than the total compute power in most robotaxi vehicles in development today and four times faster than Nvidia’s Orin processor.

Atlan will leverage NVIDIA’s latest GPU architecture, new Arm CPU cores and deep learning and computer vision accelerators. The platform architecture provides ample compute horsepower for the redundant and diverse deep neural networks that will power future vehicles, while leaving headroom for developers to continuously add new features and improvements.

“To achieve higher levels of autonomy in more conditions, the number of sensors and their resolutions will continue to increase,” Huang said. “AI models will get more sophisticated. There will be more redundancy and safety functionality. We’re going to need all of the computing we can get.”

Over the past several years, Nvidia has become a leading developer of hardware for autonomous vehicles around the world, due to the robust processing capabilities of its SoCs.

The company’s DRIVE platform is a software-defined, end-to-end platform for the transportation industry that enables continuous improvement and continuous deployment through over the air updates. It delivers everything needed for the development of autonomous vehicles at scale.

Autonomous vehicles equipped with dozens of sensors and AI-powered software for navigation require an incredible amount of processing power and Nvidia’s DRIVE family of products is designed to meet this need.

Automakers and startups around the world have turned to Nvidia to supply the hardware necessary to support the development of robust and safe autonomous driving technology powered by AI. Vehicles from Mercedes Benz, Toyota, Volvo, Audi and others are using Nvidia processors.

The open DRIVE Software stack allows developers build perception, mapping, planning, and driver monitoring capabilities. Using the power of GPUs and AI, developers can train deep neural networks that support these highly advanced systems.

Like the Nvidia DRIVE Orin, the next-gen platform is software compatible with previous DRIVE compute platforms, allowing customers to leverage their existing software investments across multiple product generations.

Atlan will come with Nvidia’s BlueField data processing unit (DPU) for enhanced security

Atlan is also the first SoC to be equipped with Nvidia’s BlueField data processing unit (DPU) for additional security, advanced networking and storage services. Nvidia’s BlueField data processing unit will connect an autonomous vehicle directly to Nvidia’s powerful data centers.

BlueField is a data center services accelerator on a chip, delivering up to 400 gigabits per second (Gb/s) of Ethernet and InfiniBand (IB) connectivity for both traditional applications and modern GPU-accelerated workloads while freeing host CPU cores to run applications instead of infrastructure tasks.

The NVIDIA BlueField DPU is designed to handle the complex compute and AI workloads required for autonomous vehicles. It’s fully programmability to deliver “zero-trust” security to prevent data breaches and cyberattacks.

While vehicles are being packed with more computing technology, they’re lacking the physical security that comes with data center-level processing, according to Nvidia. The company claims that Atlan is a technical marvel for safe and secure AI computing, fusing all of NVIDIA’s technologies in AI, automotive, robotics, safety and BlueField data centers.

Data center development is critical to deploying robotaxis at scale. The ability to operate in thousands of conditions around the world requires intensive deep neural network training requiring massive amounts of data.

By combining data center and in-vehicle solutions, autonomous vehicle developers can create a continuous, end-to-end development cycle. As deep neural networks learn new capabilities in the data center, the validated algorithms can be pushed to the car’s compute platform via an over the air software update.

Cars and trucks of the future will require an optimized AI architecture not only for autonomous driving, but also for intelligent vehicle features like speech recognition and driver monitoring systems. Upcoming software-defined vehicles will be able to converse with occupants answering questions, providing directions and even warn of road conditions ahead.

Nvidia is working with Mercedes Benz on the next generation of software-based vehicles that support over the air updates for the life of the vehicle. The advanced software-based vehicle architecture will be introduced beginning with 2024 model year vehicles, eventually rolling out to the entire Mercedes Benz fleet globally.

Nvidia and Mercedes Benz plan on developing the most sophisticated and advanced computing architecture ever deployed in the auto industry.

The new software-defined architecture will be built on Nvidia’s Orin DRIVE platform. Eventually, every future Mercedes Benz model globally will come with the advanced and upgradable software-based architecture, from the entry-level A-Class to S-Class models.

“Every future Mercedes- Benz with the NVIDIA DRIVE system will come with a team of expert AI and software engineers continuously developing, refining and enhancing the car over its lifetime,” said Huang last year when the partnership with Mercedes Benz was announced.

Nvidia hardware is also powering the autonomous driving systems for the new P7 sedan from Chinese electric vehicle startup and Tesla challenger XPeng.

Nvidia will first deliver its DRIVE Orin processors with vehicle production timelines starting in 2022. Orin will be followed by Atlan with sampling in 2023. It will be ready for production vehicles in 2026.

What Is Google Cloud Dataflow?

Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines on Google Cloud Platform (GCP). It handles provisioning, scaling, and operational management of distributed data processing jobs, supporting both batch and streaming workloads. Developers write pipelines using the Apache Beam SDKs — available in Java, Python, and Go — and Dataflow executes them at scale.

What Is Runner V2?

In mid-2020, Google announced Runner V2, a redesigned version of the Dataflow runner for Apache Beam. The core motivation was to move toward a more services-based, portable architecture that could support all of Dataflow’s language SDKs consistently — rather than maintaining separate, language-specific execution paths that diverged in capability over time.

Runner V2 is built on top of Apache Beam’s portability framework, an initiative within the Apache Beam project to enable pipelines written in any supported SDK to run on any compliant runner without SDK-specific runner code. Google packaged this framework together with two existing Dataflow services:

• Dataflow Shuffle — for optimizing batch job execution
• Streaming Engine — for offloading streaming job state management from worker VMs

By combining these components under the portability framework, Google can now deliver a consistent feature set across all language SDKs, share bug fixes, and roll out performance improvements uniformly.

Key Features of Runner V2

Multi-Language Pipeline Support

Perhaps the most significant capability introduced in Runner V2 is multi-language pipeline support. Prior to this release, a Beam pipeline written in Python could not natively call a transform or I/O connector written in Java, and vice versa. Runner V2 changes this by using the portability framework’s cross-language framework, which allows transforms from one SDK to be embedded in a pipeline written in another.

In practice, this means engineering teams can build reusable pipeline components in their preferred language — say, a high-performance Java I/O connector — and expose those components to teams writing pipelines in Python or Go. The result is a single, high-performance, distributed processing pipeline that spans language boundaries without sacrificing performance or correctness.

Increased SDK Parity

Historically, Beam’s Java SDK was the most feature-complete. Runner V2 significantly narrows the gap between SDKs by delivering:

• State and timer support in Python — enabling stateful processing patterns (e.g., sessionization, aggregation with custom windowing) that were previously only reliable in Java
• More I/O connectors for Python via the cross-language framework, including Kafka I/O — allowing Python pipelines to read from and write to Apache Kafka using the mature Java Kafka I/O implementation

Portable, C++-Based Worker Architecture

Runner V2 introduces a more efficient and portable worker architecture, with critical components rewritten in C++. The worker Virtual Machines (VMs) run the complete pipeline and provide access to the various language SDKs via the portability framework’s gRPC-based worker API. This design separates the runner logic from the SDK-specific user code, making it possible to support new languages without rewriting runner internals.

Custom Container Support

Runner V2 adds support for custom containers. Teams can supply their own Docker container images for the Beam SDK worker environment. This is particularly valuable when pipelines depend on native libraries, non-standard Python packages, or specific OS-level dependencies that cannot be satisfied through standard Dataflow worker images alone.

Increased Throughput via SplittableDoFns

SplittableDoFns (SDF) is an Apache Beam API that allows a single DoFn (a fundamental processing unit in Beam) to be split and processed in parallel across multiple workers. Runner V2 improves the execution of SplittableDoFns, enabling better parallelism for I/O-bound operations such as reading from large files, databases, or message queues. This translates directly to higher throughput for pipelines that use SDF-based I/O sources.

Architecture Overview

The high-level architecture of Runner V2 centers on the worker VM. Each worker VM:

• Runs the portable Dataflow runner (C++-based harness)
• Communicates with language-specific SDK worker containers via gRPC
• Offloads shuffle state (for batch) to Dataflow Shuffle and streaming state to Streaming Engine
• Executes user-defined transforms inside isolated SDK environments, including custom containers

This separation of the runner harness from SDK worker code is the architectural shift that makes multi-language pipelines possible. The runner does not need to understand the semantics of user code; it simply coordinates execution by sending and receiving data via a well-defined portable API.

Why This Matters for DevOps and Data Engineering Teams

For teams running production data pipelines on GCP, Runner V2 offers several practical benefits:

• Reduced maintenance overhead — bug fixes and performance improvements are applied across all SDKs simultaneously, rather than being ported language by language.
• Greater code reuse — high-quality connectors or transforms written in Java can be used in Python pipelines without rewriting them.
• Dependency flexibility — custom container support makes it easier to manage complex dependency trees in containerized CI/CD workflows.
• Future-proofing — as Apache Beam adds new language SDKs (e.g., Go, Typescript), they will benefit from the portability framework from the start, rather than requiring bespoke runner integration work.

Getting Started with Runner V2

Runner V2 can be enabled on existing Dataflow jobs by passing the --experiments=use_runner_v2 flag when submitting a pipeline. Consult the official Cloud Dataflow documentation for the latest guidance on enabling Runner V2, supported regions, and any feature-specific prerequisites such as Streaming Engine or Dataflow Shuffle enablement.

Teams evaluating Runner V2 for production workloads should test pipeline behavior carefully, particularly around stateful operations, I/O connectors, and any custom dependencies, before migrating from the default runner.

New Relic Unveils a New Generation of Observability at FutureStack 2019

At its annual FutureStack conference in New York City, New Relic announced a series of significant platform innovations centred on making New Relic One the industry’s first observability platform described as simultaneously open, connected, and programmable. CEO and founder Lew Cirne led the announcements, which included new products for log management, distributed tracing, metrics ingestion, and AI-powered incident response.

The additions respond directly to how modern engineering teams operate: running distributed, containerised workloads across multiple clouds, often managing data from a heterogeneous mix of agents, open-source tools, and third-party services. New Relic One aims to consolidate that data into a single platform with built-in intelligence and extensibility.

“New Relic One is now the world’s first observability platform that is open, enabling customers to bring in agent-based and open telemetry data so they have no blind spots; connected, allowing customers to see relationships between their systems so they can act more quickly and effectively; and programmable, empowering customers to build entirely new applications on top of New Relic to fuel their business. It’s not a platform unless you can build apps on it.”

— Lew Cirne, CEO and Founder, New Relic

Programmability: Building Custom Applications on New Relic One

One of the headline announcements was the general availability of programmability across the New Relic One platform. This capability allows New Relic customers, partners, and developers to build purpose-built applications directly on top of New Relic One — combining observability data with their own business data in a single interface.

Key Programmability Features

• Familiar tech stack: Applications are built using React.js and GraphQL — widely used technologies in modern web development — lowering the barrier to entry for engineering teams already familiar with the JavaScript ecosystem.
• Fast time-to-app: New Relic claims developers can go from idea to working application in minutes, not days.
• No additional cost: Programmability is available to all existing New Relic customers at no extra charge.
• Open-source app catalogue: New Relic released an initial set of applications, co-developed with early-access customers and partners, on GitHub under the Apache licence.

“The new programmability features have equipped us to get significantly more value from New Relic, at no additional cost. Our DevOps & SRE teams are now able to create tailored apps, for their specific business needs, that easily fit into our workstream. At an enterprise-scale, programmability has enabled an agility in data for both application and platform teams.”

— Joshua Biggley, Sr. Engineer, Enterprise Monitoring, Cardinal Health

Initial Open-Source New Relic One Applications

New Relic released the following apps as free, open-source downloads from GitHub at launch:

• Cloud Optimize — Compares provisioned cloud resource sizes against actual utilisation, helping teams identify over-provisioned infrastructure and reduce cloud spend.
• GitHub Integration — Surfaces service ownership and recent commit history directly within New Relic One, so on-call engineers can immediately understand what a service does and who last changed it.
• Site Analyzer — Models how improvements to performance KPIs such as page load time and traffic volume can translate into better conversion and customer experience metrics.
• Status Page — Aggregates Statuspage.io status feeds into a single dashboard, giving teams a unified view of upstream dependencies during an incident.
• Customer Journeys — Tracks cohort progress through conversion funnels, providing product and engineering teams with visibility into how user behaviour correlates with technical performance.

Open: New Relic Logs, Traces, and Metrics

New Relic’s “open” pillar focuses on eliminating blind spots by making it straightforward to ingest the full range of telemetry data — logs, metrics, events, and traces — regardless of source.

New Relic Logs

New Relic Logs is a log management capability integrated directly into New Relic One. Rather than context-switching between a dedicated log aggregation tool and an APM or infrastructure dashboard, teams can search, filter, and correlate log data alongside application traces and infrastructure metrics in a single interface.

• Ad hoc log searches return results without pre-defined indexing configurations.
• Designed to scale without the operational overhead of self-hosted log management solutions (e.g. managing Elasticsearch clusters).
• Available to New Relic customers at time of announcement.

New Relic Traces and New Relic Metrics

New Relic Traces and New Relic Metrics extend the platform’s data ingestion capabilities to cover distributed tracing and time-series metrics from any source — not just New Relic’s own agents.

• Agent-based and agentless: Teams can send data from existing New Relic agents or route telemetry from open-source and third-party instrumentation directly into the platform.
• OpenTelemetry support: New Relic confirmed active contributions to and support for the OpenTelemetry project, as well as involvement in AdoptOpenJDK and the Cloud Native Computing Foundation (CNCF).
• Both products were made generally available at FutureStack 2019.

Together, Logs, Traces, and Metrics cover the four pillars of observability telemetry (the fourth being Events), giving teams a complete data foundation within a single platform.

Connected: New Relic AI for AIOps and Smarter Incident Response

New Relic AI is the company’s answer to alert fatigue — a chronic problem for DevOps, SRE, and on-call engineering teams operating large-scale systems. The product applies machine learning to correlate, aggregate, and de-duplicate incident signals, presenting teams with a prioritised, contextualised view of issues rather than a flood of raw alerts.

New Relic AI Capabilities

• Noise reduction: New Relic reported that initial customers observed more than 80% reduction in alerting noise. (See review notes.)
• Automatic anomaly detection: The system identifies unusual behaviour in production metrics without requiring teams to define manual thresholds for every signal.
• PagerDuty integration: New Relic AI connects directly with PagerDuty, enriching incidents with contextual observability data without requiring changes to existing on-call rotations or escalation policies.
• Slack integration: Critical incident context and anomaly alerts are surfaced in Slack channels, keeping teams informed in the tools they already use during an incident.
• Reduced MTTR: By automatically correlating related alerts and surfacing probable root causes, New Relic AI aims to cut mean time to resolution.

Why This Matters for DevOps and SRE Teams

The FutureStack 2019 announcements reflect a broader shift in how observability platforms are expected to work. Stitching together separate tools for logs, metrics, traces, and alerting introduces toil, context loss, and integration maintenance burden. The New Relic One direction — a unified data plane with open ingest, cross-signal correlation, and a programmable surface for custom applications — addresses operational pain points that are well-recognised across the industry.

For teams already invested in open-source telemetry standards like OpenTelemetry, New Relic’s stated commitment to those projects (including contributions to CNCF) signals that vendor lock-in at the instrumentation layer is less of a concern than it was with earlier generations of APM tooling.

The programmability layer is particularly notable for platform engineering teams: the ability to embed observability data into custom internal tooling — without maintaining a separate backend — has meaningful implications for developer experience and operational efficiency at scale.

VMware Introduces the Tanzu Portfolio

At VMworld 2019, VMware unveiled Tanzu, a portfolio of products and services aimed at transforming how enterprises build, run, and manage software on Kubernetes. The announcement represented a significant strategic pivot for VMware, positioning the company as a major player in the cloud-native ecosystem.

VMware Chief Operating Officer (Products and Cloud Services) Raghu Raghuram summarized the vision:

“We’re positioned to help customers succeed along each step of their journey—building their applications with the addition of Pivotal’s developer platform, tools and services; running their applications with the groundbreaking Project Pacific which will transform vSphere into a Kubernetes native platform; and managing their growing Kubernetes footprint across environments from a single control point with VMware Tanzu Mission Control.”

Raghu Raghuram, COO (Products and Cloud Services), VMware

Three Pillars: Build, Run, Manage

The Tanzu portfolio was structured around three core activities for enterprise Kubernetes adoption:

• Build: Helping development teams create modern applications using a modern software supply chain, supported by the pending Pivotal acquisition and its developer-centric platform.
• Run: Transforming VMware vSphere into a Kubernetes-native platform through Project Pacific, enabling enterprises to run containerized workloads alongside traditional VMs.
• Manage: Providing centralized control of Kubernetes clusters across environments using VMware Tanzu Mission Control.

Project Pacific: Kubernetes Embedded in vSphere

The centerpiece of the VMworld 2019 announcement was Project Pacific, a re-architecture of VMware vSphere to make it Kubernetes-native. Rather than treating Kubernetes as an add-on, Project Pacific aimed to embed Kubernetes capabilities directly into the vSphere hypervisor layer.

Key technical aspects of Project Pacific included:

• Container runtime in the hypervisor: A new ESXi-native pod concept was introduced, combining properties of both Kubernetes pods and virtual machines to deliver improved security and performance for mission-critical workloads.
• App-level policy controls: Operators could apply policies, resource quotas, and role-based access controls at the application level directly through vSphere tooling.
• Kubernetes API access to SDDC infrastructure: Developers could consume Software-Defined Data Center (SDDC) resources via standard Kubernetes APIs, while IT operators continued to use familiar vSphere tools to provision and manage Kubernetes clusters.
• Consistent view for all stakeholders: Both developers and IT operators would interact with the same underlying infrastructure through Kubernetes constructs, reducing operational friction.

This approach was designed to protect existing investments in vSphere skills, tooling, and infrastructure while enabling a gradual and practical migration toward cloud-native application architectures.

Acquisitions Underpinning the Portfolio: Pivotal and Bitnami

Two acquisitions were central to the Tanzu build story:

• Pivotal: VMware announced its intention to acquire Pivotal, which brought a comprehensive developer platform, professional services expertise, and a strong connection to the enterprise developer community. Pivotal’s Cloud Foundry heritage and Spring ecosystem were particularly relevant to enterprises modernizing Java-based applications.
• Bitnami: Bitnami contributed what VMware described as the largest catalog of pre-built, scanned, tested, and continuously maintained application content for Kubernetes clusters, with a stated audience of 2.5 million developers at the time of announcement.

Project Galleon: Curated Application Stacks for Developers

Also announced at VMworld 2019 was Project Galleon, introduced as a beta offering. Project Galleon was designed to allow enterprise IT teams to deliver customized, up-to-date application stacks and package formats to their internal developers, with multi-cloud readiness as a design goal. This addressed a common enterprise pain point: ensuring that developer-facing tooling is both current and compliant with organizational standards.

Tanzu Mission Control: Managing Kubernetes at Scale

VMware Tanzu Mission Control was positioned as the management layer for organizations running Kubernetes clusters across multiple environments. It leveraged several open-source projects to deliver key capabilities:

• Cluster API — for Kubernetes cluster lifecycle management
• Velero — for backup and recovery of cluster state and persistent volumes
• Sonobuoy — for conformance testing and configuration validation
• Contour — for ingress control using the Envoy proxy

VMware’s commitment to open-source was highlighted as a core principle of the Tanzu portfolio. The company cited its position as a top-three contributor to the Kubernetes project at the time of the announcement.

Industry Context

Jay Lyman, Principal Analyst at 451 Research, offered third-party perspective on the announcement:

“Kubernetes can help organizations achieve consistency and drive developer velocity across a variety of infrastructures, but enterprises also require effective control, policy and security capabilities. Building on its acquisitions, organic innovation and open source contributions, VMware has staked out its place as a leader in this rapidly evolving cloud-native industry.”

Jay Lyman, Principal Analyst, 451 Research

The Tanzu announcement signaled that VMware recognized Kubernetes as the de facto standard for enterprise container orchestration and was repositioning its core vSphere platform accordingly—rather than competing with Kubernetes or treating it as an optional feature.

Summary

VMware’s Tanzu portfolio, announced at VMworld 2019, represented a comprehensive response to the shift toward cloud-native application development in the enterprise. By embedding Kubernetes into vSphere through Project Pacific, acquiring developer-focused platforms via Pivotal and Bitnami, and providing multi-cluster management through Tanzu Mission Control, VMware aimed to position itself as the platform of choice for enterprises at every stage of their Kubernetes journey.

Overview

Cloudian announced general availability of its fully native S3-compatible object storage solution for VMware Cloud Providers in August 2019. The solution integrates directly with VMware vCloud Director — a core component of the VMware Cloud Provider Platform — allowing service providers to manage object storage from a single control plane and automate storage workflows without leaving the vCloud Director interface.

The integration targets VMware Cloud Providers who want to offer revenue-generating, managed storage services to their customers without building a separate storage management stack. By combining Cloudian’s HyperStore object storage with vCloud Director’s orchestration capabilities, providers gain a foundation for launching multiple as-a-service offerings backed by S3-compatible APIs.

Key Highlights

• Limitlessly scalable, native S3-compatible storage managed from VMware vCloud Director
• Partnership with Veeam enables S3-compatible data protection services within vCloud Director environments
• Cloudian achieved Veeam Ready – Object designation through the Veeam Ready Program, indicating the highest qualification tier for object storage products
• Cloudian CMO Jon Toor participated in a VMworld 2019 industry panel alongside VMware executives and joint customer phoenixNAP

Supported As-a-Service Offerings

VMware Cloud Providers can use the Cloudian integration to build and deliver the following categories of managed services:

• Storage-as-a-Service (STaaS) — tenant-isolated, scalable object storage billed on consumption
• Backup-as-a-Service (BaaS) — S3-compatible backup targets for VM and application data
• Archive-as-a-Service (AaaS) — low-cost long-term data retention using object storage tiers
• Disaster Recovery-as-a-Service (DRaaS) — replication and recovery workflows anchored to S3-compatible endpoints
• Big Data-as-a-Service (BDaaS) — S3-compatible data lakes accessible to analytics toolchains

Deployment Flexibility

Cloudian can be deployed either as a hardware appliance or as software-defined storage running on commodity hardware anywhere in the provider’s infrastructure. In both deployment modes, vCloud Director manages the storage resources centrally and provides native workflow automation, removing the need for providers to operate a separate management interface for their object storage tier.

Out-of-the-box features included with the solution are designed specifically for multi-tenant service provider environments:

• Multi-tenant resource pooling — isolate storage capacity per tenant or customer account
• Integrated billing — track and report consumption per tenant for chargeback or showback
• Quality of Service (QoS) controls — set IOPS and throughput limits per tenant to prevent noisy-neighbor issues
• Exabyte-scale modular scalability — add capacity nodes incrementally without service disruption

Cloud-Ready Data Protection: Cloudian and Veeam Integration

A central use case for this release is enabling VMware Cloud Providers to deliver data protection services using a combined Cloudian and Veeam stack, fully orchestrated through vCloud Director.

How it works: Veeam Backup & Replication uses vCloud Director APIs to back up vApps and virtual machines and restore them directly into the vCloud Director hierarchy. Cloudian serves as the S3-compatible object storage target for Veeam Cloud Tier, providing unlimited long-term retention capacity at object storage economics. Providers can deploy both Cloudian and Veeam directly through vCloud Director, keeping the entire workflow inside a single management plane.

Cloudian achieved Veeam Ready – Object status through the Veeam Ready Program. This qualification and testing program validates that storage products meet Veeam’s interoperability and performance standards, and the “Ready – Object” designation represents the highest tier of qualification for object storage solutions in the program.

Industry Quotes

“With Veeam Backup & Replication, service providers can now offer their customers S3-compatible data protection services compatible with Cloudian and VMware vCloud Director, easily migrating and protecting their VMware workloads, all with the same proven Veeam solutions they already use today.”

— Ken Ringdahl, Vice President Global Alliances Architecture, Veeam

“One of our continuing priorities is creating new revenue opportunities for VMware Cloud Providers by enabling high-value solutions. Ecosystem partners play a key role in this endeavor, and we look forward to working with Cloudian and Veeam to offer these Cloud Providers an enhanced platform for serving their customers’ data protection needs.”

— Ajay Patel, Senior Vice President of Product Development, Cloud Services, VMware

Why This Matters for VMware Cloud Providers

For service providers running on the VMware Cloud Provider Platform, a native S3-compatible object storage tier addresses several practical gaps:

• S3 ecosystem compatibility — thousands of applications natively support the S3 API. Offering an S3-compatible endpoint means providers can immediately serve customers using tools like Veeam, Veritas, Commvault, Splunk, and most major data platforms without custom integration work.
• Margin improvement — object storage is significantly cheaper per gigabyte than block storage. Moving backup, archive, and bulk data workloads to object storage lowers provider infrastructure costs while maintaining competitive pricing.
• Reduced management overhead — centralized management inside vCloud Director reduces the operational complexity of running separate storage platforms with separate tooling.
• Multi-tenancy at scale — built-in tenant isolation, billing, and QoS controls mean providers can onboard new customers and storage workloads without significant per-tenant configuration effort.

About Cloudian HyperStore

Cloudian’s flagship product, HyperStore, is an S3-compatible object storage platform designed for on-premises and hybrid deployments. It provides a global data fabric architecture that allows enterprises and service providers to store, search, and protect object and file data across multiple sites — both on-premises and in public clouds — within a single unified namespace. Cloudian positions HyperStore as a drop-in S3-compatible alternative to public cloud object storage for organizations that need data sovereignty, low-latency local access, or predictable cost structures.

VMworld 2019 US: What to Expect

VMware, Inc. (NYSE: VMW) announced the General Session keynote speakers and sponsorship lineup for VMworld 2019 US, scheduled to begin August 25, 2019 in San Francisco, California. The conference theme — “Make Your Mark” — centered on helping IT and business professionals accelerate technology-driven transformation without disruption.

VMworld 2019 US featured more than 700 unique breakout sessions from VMware, its partners, and customers, covering topics ranging from cloud infrastructure and Kubernetes to networking, security, and digital workspaces.

Monday General Session: Business Strategy and Portfolio Innovations

The opening General Session took place on Monday, August 26, 2019 at 9:00 a.m. PDT. VMware CEO Pat Gelsinger led the session with an industry-wide perspective, joined by COO of Customer Operations Sanjay Poonen. Together they outlined VMware’s business strategy and spotlighted new innovations across the company’s portfolio, including:

• Cloud infrastructure and hybrid cloud strategy
• Kubernetes and container workload management
• Networking and security advances
• Digital workspace solutions

Customer stories were also featured to illustrate real-world examples of technology-driven transformation at scale.

Tuesday General Session: Tech Demos and Fireside Chat

On Tuesday, August 27, 2019 at 9:00 a.m. PDT, VMware CTO Ray O’Farrell led a team of technologists through live technology demonstrations showcasing how VMware and its ecosystem partners are pushing innovation in unexpected directions.

Following the technical demos, Sanjay Poonen hosted a fireside chat with two world-renowned athletes:

• Lindsey Vonn — Olympic gold and bronze medal-winning alpine ski racer for the U.S. Ski Team, New York Times best-selling author, and founder of the Lindsey Vonn Foundation, which works to empower young girls.
• Steve Young — Record-setting NFL quarterback, acclaimed motivational speaker, and Founder and Chairman of the Forever Young Foundation (FYF), which supports children’s charities nationwide.

The conversation focused on the athletes’ work ethic, how they overcame significant obstacles throughout their careers, and how they now channel that drive into empowering others.

Sponsors and Exhibitors

VMworld 2019 US was supported by more than 220 sponsors and exhibitors. Global Diamond Sponsors included:

• Amazon Web Services (AWS)
• CenturyLink
• Dell Technologies
• Hewlett Packard Enterprise (HPE)
• IBM
• Intel

Livestream Availability

Both General Sessions were made available via live webcast through the VMworld homepage — Monday, August 26 and Tuesday, August 27, each starting at 9:00 a.m. PDT — allowing attendees and those unable to travel to San Francisco to follow along in real time.