Adastra Group (also known as Adastra Corporation), a global leader in cloud, data and artificial intelligence (AI) solutions and services, today announced the launch of its innovative Cloud Analytics Solution, a well-architected framework designed for businesses to maximize operational efficiencies while meeting sustainability targets.

Adastra’s solution utilizes advanced analytics techniques to collect, measure, and calculate an organization’s carbon footprint. The solutions allow patterns and trends to emerge that help identify areas where improvements and efficiencies can be made. Using scalable and maintainable data engineering methods, the solution delivers the necessary insights for informed decision-making for companies to embrace sustainability as a competitive advantage.

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With the product’s successful completion of the Amazon Web Services (AWS) Foundational Technical Review (FTR) to evaluate its risks, stability, and security, the solution is now available in AWS Marketplace, a digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on AWS.

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“We are thrilled to unveil our latest sustainability solution in AWS Marketplace,” said Dr. Johannes Mellenthin, AWS Alliance Lead DACH, Adastra.

“This Cloud Analytics Solution reflects Adastra’s commitment to empowering enterprises worldwide with the tools they need to cultivate a greener future. With this framework, businesses can incorporate operational sustainability analysis that positively impact both their environmental performance and overall bottom line—all in real-time,” said Mayur Hastak, Cloud Principal Architect, Adastra.

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Designed to address challenges faced by organizations aiming to optimize sustainability practices while maintaining a competitive edge, Adastra’s solution adheres to the highest industry standards, enabling businesses to leverage data with confidence and control.

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Realist AI, a leading Google Cloud technology services consultancy, has achieved a new Google Cloud Expertise designation for Google Cloud Analytics. This achievement further strengthens Realist’s position as a leading solutions provider and catalyst for Google Cloud-enabled digital transformation.

Realist is recognized for consistency in delivering measurable business value throughout the entire cloud migration and business transformation journey. Its dedication to tailored solutions has been critical to its success in serving numerous Fortune 500 and Global 2000 clients..

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Realist combines its extensive client experience and Google Cloud technical expertise with deep domain knowledge to ensure that clients achieve long-term success well beyond digital transformation. This approach equips clients to achieve their business objectives, performance targets, and resilience goals, which originally motivated their shift to Google Cloud.

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As a part of the comprehensive certification process, Google Cloud conducts thorough assessments of each partner’s cloud client experience, professional certifications, Google Cloud expertise, and Cloud Adoption Framework to identify top companies and acknowledge those who set the standard in these vital areas. Only the most qualified partner companies receive Google Cloud’s esteemed Expertise designations, and all awardees exhibit exceptional technical proficiency and a proven history of success.

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“We’re pleased to be recognized by Google Cloud with this prestigious Google Cloud Expertise designation”, said Ryan den Otter, Realist CEO and Founder. “At Realist, our goal is to help clients adopt and modernize their data analytics and applications on the Google Cloud Platform.  These recognitions from Google reflect our commitment to ensuring certified expertise in order to achieve project success for our clients.”

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Artificial intelligence (AI) is slowly being used in manufacturing facilities. Thanks to AI advancements, we can now do tasks like predictive maintenance, cognitive computing, swarm intelligence, context-aware computing, smart machines, hardware accelerators, and generative design.

Automated image recognition is used throughout the BMW Group for quality control and inspections, as well as the removal of pseudo-defects (deviations from target despite no genuine flaws). Because of this, their production is extremely precise.
Porsche is yet another manufacturer to reap the benefits of AI technology. Significant amounts of the automobile production process are automated with the use of autonomous guided vehicles (AGVs).

Other applications of AI in production include more accurate demand forecasting, heightened quality control, enhanced inspections, and automated stockrooms. “Industry 4.0,” the movement toward more automation in manufacturing plants and the massive creation and transfer of data, relies heavily on artificial intelligence.

What is the AI trend in the manufacturing industry?

AI will contribute up to $15.7 trillion to the manufacturing industry by 2025.

The graph has been taken from Google Cloud which explains the top three manufacturing sectors deploying AI. Artificial intelligence can revamp the manufacturing sector. Possible benefits include higher output, lower costs, better quality, and less downtime. This technology can be used in a variety of settings, including large industries. AI simplifies industrial operations by fully automating complicated jobs and requiring fewer people to maintain. It also provides the flexibility necessary for organizations to respond swiftly to changes in demand or product specifications by revising production plans or rerouting materials.

How large is the AI market in manufacturing?

This graph has been taken from Deloitte. AI in manufacturing would rise from its 2023 level of USD 5,070 million to a whopping USD 68,360 million by 2032, expanding at a compound annual growth rate of 33.5%.

• In 2022, the market share for North America was the highest. Between 2023 and 2032, Asia-Pacific economies are projected to expand at an exceptional CAGR.
• In 2022, software made well over 32% of Offering’s total revenue.
• From 2023 to 2032, the computer vision technology subsegment is anticipated to grow at the highest compound annual growth rate (CAGR).
• Between 2023 and 2032, the Application market is expected to be led by the predictive maintenance and machinery inspection subsegment.
• Between 2023 and 2032, the market for medical devices is expected to grow at the fastest rate of any industry.

What role is AI playing in the future of manufacturing?

Artificial intelligence (AI) is revolutionizing the industrial sector by increasing efficiency and allowing for more precise quality management. Because it can handle massive volumes of data in real-time, make choices on the fly, and automate procedures, AI is revolutionizing the manufacturing industry.

Artificial intelligence (AI) is being implemented in factories to reduce the frequency and length of downtime. Artificial intelligence may be used in many different ways in the industrial industry. Such applications might be generalized as smart manufacturing, corporate operations, supply chain, and decision-making. The increased use of AI in manufacturing has improved my company’s capacity for strategic planning, supply chain management, and overall operation management. Using artificial intelligence, manufacturing companies with R&D programs may cut down on the time and money spent on conventional operational procedures.

The adoption of cutting-edge technical solutions like analytics, augmented reality, virtual reality, smart packaging, and additive manufacturing is driving the growth of artificial intelligence (AI) in the manufacturing market. In the future, businesses in sectors presently undergoing digital transformation are predicted to use AI-based services. Important elements contributing to the expansion of AI in the industrial market are the sector’s inherent resilience and the need for long-term solutions from businesses operating in it.

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What are some specific examples of AI being used in manufacturing?

• Cobots work with humans.
• RPA tackles tedious tasks.
• Digital twins help boost performance.
• Predictive maintenance improves safety, and lowers costs.
• Machine learning algorithms predict demand.
• Inventory management prevents bottlenecks.
• AI autonomous vehicles
• AI for factory automation
• AI in design and manufacturing
• AI-based connected factory
• AI-based visual inspections and quality control
• AI for purchasing price variance

  10 Predictions for Artificial Intelligence (AI) in Manufacturing Domain in 2024

McKinsey claims that businesses who adopt AI see increased profits and decreased expenses. While 18% of respondents observed a rise in income of 6-10%, 16% noted a drop in expenses of 10-19%.

Trend 1: Artificial intelligence is being used to automate manufacturing processes.

When AI is added to robots, they may take over activities that need extreme precision. Using smart technology, many factories have reduced production costs, improved worker safety, and boosted productivity. Artificial intelligence (AI) can help manufacturers cut down on labor expenses while simultaneously raising plant output and efficiency. In addition, there are uses for:

• Implement elaborate plant automation systems
• Create a consolidated store for all operational data and context, making staff moves easier.
• Minimize the amount of inputs required to maintain production.
• Increase or decrease output quickly in response to changes in demand or manufacturing tactics.

Siemens is a leader in manufacturing automation. The two businesses have joined forces to boost factory output with the use of computer vision, cloud analytics, and AI algorithms. The Japanese automation firm Fanuc also employs robots with artificial intelligence to run its production lines. The robots can make crucial parts for CNC and motors, keep the factory floor’s gear running nonstop, and keep an eye on everything at all times.

Trend 2: Using AI to Determine Quality

The industrial sector has the greatest demand for AI in quality control. It turns out that even factory robots may get it wrong sometimes. Despite being very rare compared to humans, the costs associated with releasing flawed items to the market can add up. When applied to manufacturing processes, AI and ML combine human intellect with potent technology to bring about revolutionary breakthroughs.

Artificial intelligence (AI) can spot problems in machinery or products that a robot would miss. Using technology like cameras and Internet of Things sensors, AI software may examine products to automatically discover problems. The computer may then decide what to do with faulty items automatically.

• The final product’s quality and functionality benefit from this to a greater extent because of it. This is the major motivation for the widespread use of AI-powered automation and robust tools by many manufacturers in the detection of process or product design problems in the present day. By doing rigorous quality testing using AI, manufacturers ensure high-quality goods with a quicker time to market.
• BMW employs automatic image recognition for quality control, inspections, and the removal of pseudo-problems (variations from the target notwithstanding the absence of real defects). This has led to increased accuracy in their production methods.

Trend 3: NLP

• The development of NLP is facilitating workers’ ability to report problems and find answers to customer inquiries.
• The use of chatbots fueled by natural language processing (NLP) is a significant AI development with significant potential to enhance the effectiveness of factory problem reporting and assistance requests.
• This subfield of AI focuses on creating convincing simulations of human communication. Artificial intelligence can improve the quality, timeliness, and thoroughness of reports submitted by workers if they can utilize their mobile devices to speak with chatbots and report difficulties. This improves worker responsibility while lessening the burden on managers.

Trend 4: Using AI/ML/deep learning to boost prediction precision

Artificial intelligence (AI) in supply chain and logistics has great promise for facilitating real-time forecast updates and improved decision-making in the manufacturing industry. Planning and forecasting need to be more sophisticated and sensitive to disturbances. Machine learning and deep neural networks are being used by manufacturers to cut down on transactions while improving output. They hope that by replacing time-consuming processes like scanning with AI, they can speed up pallet preparation and improve packing accuracy.

The French food producer Danone Group is a perfect case. Danone has accomplished this by implementing ML into its demand forecasting procedures the following:

• Errors in forecasting have decreased by 20%.
• Sales decline by 30%
• Demand planners’ task has been cut in half.

Thales SA is yet another illustration. They have been using ML to forecast upkeep for Europe’s high-speed train systems. The organization collects data on the present and historical health of subsystems and thousands of sensors across Europe’s intercity rail networks. To achieve a high degree of dependability, it has built an AI system based on the data to detect probable problems and determine when certain parts need to be replaced.

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Trend 5: AI is accelerating progress toward the Sustainable Development Goals.

BCG found that AI may save between $1.3 trillion and $2.6 trillion in income and cost reductions while reducing greenhouse gas emissions by between 2.6 and 5.3 gigatonnes of CO2. Artificial intelligence and analytics will be used by businesses to determine their carbon footprints and identify areas for improvement.

The benefits of AI extend beyond the reduction of greenhouse gas output. Waste prevention measures including those aimed at decreasing ocean plastic litter and the creation of environmentally friendly goods and production processes are two more. Furthermore, manufacturing organizations must find a balance between operational efficiency and the dangers to corporate assets and people. Improvements in video analytics and building management systems have allowed businesses to leverage AI and analytics to make their workplaces safer for employees.

Due to the high cost of time, money, and resources, as well as the need to train a new generation of workers, it is essential for manufacturers to keep up with the latest developments in AI and incorporate them into their operations as soon as feasible.
The window of opportunity to integrate AI into production processes is closing fast for those who haven’t done so before.

Trend 6: Predictive analytics

The ability of AI to accurately forecast outcomes is still crucial, no matter how widespread the use of AI solutions becomes. Anticipating when the running machinery could break and preparing the necessary repairs in advance, enables manufacturers to avoid any future problems. Predictive analytics is also used in software that predicts the price of raw materials.

Trend 7: Creative pattern making

Businesses in the manufacturing sector may employ cloud computing and AI to create and improve 3D models. Here, ML models mimic the design process utilized by engineers, letting factories quickly develop a plethora of design options for a given product. The quality and speed of data collection is a major obstacle to integrating AI in manufacturing. To produce reliable inferences and judgments, AI models require vast amounts of information.

The reliability of the findings obtained is dependent on the quality and timeliness of the data utilized. Improving operational efficiency and productivity is a major benefit of using AI in manufacturing. Intelligent automation solutions driven by AI algorithms may improve efficiency in manufacturing, streamline logistics, and cut down on downtime.

Trend 8:Assurance of Quality

This is especially important in manufacturing since it guarantees a constant standard of quality across the board. Most faults are readily apparent to the human eye, making computer vision a powerful tool for artificial intelligence.

Foxconn, a contract manufacturer for Apple, Nintendo, Nokia, and Sony, among others, has successfully adopted Google Cloud Visual Inspection AI to improve quality control in its factories and cut down on QA costs.

Trend 9:Automatic, robotic execution of processes.

Robots used in industry, often known as industrial robots, are programmed to do repetitive tasks automatically, considerably reducing the likelihood of human mistakes. They shift the emphasis of human labor to more profitable activities.

Case in point, Schneider Electric, a French multinational specializing in digital automation and energy management, deployed RPA to minimize non-value-added jobs, saving time for staff to emphasize customer satisfaction.

Trend 10:Data Science for Warehousing

Artificial intelligence may be used to automate several tasks in warehouse management. Thanks to the constant stream of data they gather, manufacturers can keep a close check on their stockrooms and optimize their operations.

Automating quality control and inventory may boost productivity, save labor costs, and reduce the number of personnel needed to manage a warehouse. As a consequence, manufacturers may increase their income and sales.

The Potential of Artificial Intelligence for the Manufacturing Sector

The use of artificial intelligence (AI) spans the whole production cycle, from sourcing raw materials to shipping finished goods. Predictive maintenance is where AI shines. Businesses in the manufacturing sector may improve machine failure prediction and prevention by using AI to produce data. As a result, production downtime is reduced, saving money. Other applications of AI in production include more accurate demand forecasting, heightened quality control, enhanced inspections, and automated stockroom.

“Industry 4.0,” the movement toward more automation in manufacturing plants and the massive creation and transfer of data, relies heavily on artificial intelligence. Artificial intelligence (AI) and machine learning (ML) are essential to help businesses make sense of the massive volumes of data produced by industrial equipment. Saving money, making the workplace safer, and streamlining the supply chain are just a few of the many potential outcomes of applying AI to this information.
Machine learning and deep learning, natural language processing, and machine vision are just a few of the AI sub-technologies that play an important part in many manufacturing tasks.

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Teradata announced the integration and general availability of Teradata VantageCloud, the complete cloud analytics and data platform, with Microsoft Azure Machine Learning (Azure ML). VantageCloud’s scalability, openness and industry-leading analytics – ClearScape Analytics – combined with Azure ML’s ability to simplify and accelerate the ML lifecycle helps customers unlock the full value of their data, even in the most complex and demanding environments.

Despite continued investment by organizations in artificial intelligence and machine learning (AI/ML), many AI/ML initiatives struggle to get off the ground. “On average, 54% of AI projects make it from pilot to production,” according to Gartner, Inc., “Gartner Survey Reveals 80% of Executives Think Automation Can Be Applied to Any Business Decision,” August 22, 2022. Teradata VantageCloud delivers the enterprise-scale performance that ensures customers can execute complex analytics and AI/ML on massive datasets with the ability to incorporate the favorite data science tools of their choice, including Azure ML. This unparalleled performance, when combined with the platform’s ability to integrate preferred tools and languages, gives customers the freedom to unleash the full potential of their AI/ML investments by building, deploying and managing more high-quality models in production, faster and with confidence.

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“As AI/ML rapidly expands in use, more organizations across industries – from healthcare to financial services to retail – are ramping up investment in AI/ML at scale to harness the full power of their data,” said Hillary Ashton, Chief Product Officer at Teradata. “But with only half of AI/ML projects making it into production, it’s become clear that organizations are not able to fully scale their advanced analytics initiatives and maximize their investments. To address this challenge, Teradata is combining Vantage Cloud’s scalability, openness and unparalleled in-database analytics with Azure ML’s acceleration and management of the day-to-day workflows of the ML project lifecycle. This gives ML professionals, data scientists, and engineers the ability to quickly and nimbly train and deploy models, and manage MLOps, leveraging the massive amount of data that Vantage ingests.”

Teradata VantageCloud and Azure Machine Learning work seamlessly, giving customers using VantageCloud on Azure the ability to tap into the power of their data. With this integration, joint customers across industries can realize the benefits from the following use cases, and much more:

• Retail – Streamline supply chains by integrating data from myriad sources to better forecast demand, improve visibility, increase real-time flexibility, and drive automation.
• Fin Serv – Enhance risk management by fully automating decisioning and integrating risk data into balance sheet optimization.
• Healthcare – Improve patient care by using machine learning to proactively predict when medical devices may need maintenance.

“Microsoft Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models and accelerates time to value with support for the entire ML lifecycle. The platform is trusted by users and designed for responsible AI applications in machine learning – with built-in fairness and responsible usage for compliance. This reliability and trust, coupled with Teradata’s reputation for performance and stability, give our customers the confidence and support to ramp up their AI/ML initiatives to drive measurable business impact,” said, Tony Surma, Chief Technology Officer, U.S. Global Partner Solutions at Microsoft.

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Will Expand Qlik’s Existing Set of 250+ Connectors With 100 New Connectors for Qlik Cloud Data Integration in 2023

 Qlik introduced Connector Factory, its strategy to continually expand access to and delivery of data from hundreds of SaaS applications and data sources to fuel ever-evolving enterprise cloud analytics and data integration needs. Qlik customers already benefit from over 250 existing connectors, and throughout 2023 will be able to enhance their analytics and data integration efforts with 100 new connectors for Qlik Cloud Data Integration.

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With over 400 databases and thousands of packaged and legacy applications on the market – all with ever-changing APIs – enterprise IT, analytics and data engineering teams are spending significant time and resources dealing with data complexity. Connector Factory’s dedicated R&D team utilizes unique technology to alleviate this burden by rapidly developing connectors for a wide range of the most used and strategic enterprise sources through standard APIs. This eliminates the need for one-off integrations while unlocking more data for better insights, delivering significant value across the entire data lifecycle. The initial delivery of new connectors for Qlik Cloud Data Integration is expected over the next two quarters and includes support for 30 of the most popular enterprise applications including NetSuite, Workday, SAP SuccessFactors, Salesforce, Epic, Cerner, OSIsoft, ADP, SAP Ariba and HubSpot.

“With the rapid proliferation of new databases and SaaS applications, it’s difficult for vendors to keep pace,” said Mike Leone, Principal Analyst at Enterprise Strategy Group. “Qlik is taking this challenge head on with the Connector Factory and I see it serving as a linchpin for data engineering and analytics teams to unlock and integrate any data needed to drive better insights and deliver greater business outcomes.”

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Connector Factory is an evolution in Qlik’s continued focus on connecting and unlocking data from key enterprise sources for analysis and action. Qlik has a long and established track record of expertise in accessing and transforming data from complex enterprise applications such as SAP and mainframes for delivery into any cloud or analytics application, along with Qlik’s ability to provide application to application workflows through Qlik Application Automation. With Connector Factory, Qlik aims to provide universal connectivity to the expanding range of enterprise SaaS data applications and sources, creating additional value from real-time data delivery to cloud repositories, direct access for analytics, and taking direct action by linking to other applications.

“Seamlessly connecting and delivering data from a variety of sources and applications is an essential role data integration plays in modern enterprise data strategies,” said James Fisher, Chief Product Officer at Qlik. “With Connector Factory, we are doubling down on our proven ability to unlock key enterprise data sources like mainframes and SAP with expanded connectivity to the entire universe of sources and SaaS applications that are fueling enterprise data efforts in the cloud.”

Qlik will continue to deliver Qlik Cloud Data Integration connectors through Connector Factory over the course of the year, keeping pace with new sources towards the goal of creating universal connectivity for enterprises.

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Analog Devices Opts for End-to-End Deployment on Exensio Platform

 PDF Solutions, a leading provider of unified data and cloud analytics for the semiconductor ecosystem, announced that Analog Devices, Inc. is standardizing and centralizing production and new product development data on PDF Solutions’ unified Big Data Analytics platform. Analog Devices has selected PDF Solutions’ Exensio Manufacturing Analytics and Exensio Test Operations as its backend worldwide data management and analytics solution, deployed in the Exensio cloud environment.

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Centralized Big Data systems such as Exensio enable one source of truth, helping to eliminate data handling conflicts and redundancies. A solution like the Exensio Big Data Analytics Platform facilitates an ecosystem of real-time visibility and control, allowing customers to disposition issues at back-end test that has the potential to positively impact yield, quality, and throughput.

“We are looking forward to PDF Solutions being a key partner in helping us to make better use of our data,” said Michael O’Sullivan, Managing Director, Test Technology & Systems Group at Analog Devices. “We expect that the Exensio technology platform will enable Analog Devices to standardize and harmonize data and processes across our backend operations.”

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“We are very happy to continue our support and work with Analog Devices, and to bring them into a dedicated Exensio cloud environment,” said Said Akar, GM and VP of the Exensio Analytics Group at PDF Solutions. “This relationship is important to achieve next-level business objectives. Combining Exensio Manufacturing Analytics and Exensio Test Operations on a single platform, with an analysis-ready database and our DEX network, which is intended to provide OSAT connectivity from around the world, is designed to deliver critical information consolidated on the Exensio platform.”

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PDF Solutions, a leading provider of unified data and cloud analytics for the semiconductor ecosystem, and proteanTecs, a global leader of deep data analytics, announced a collaboration on combined semiconductor analytics solutions intended to provide deeper insights on yield, quality and reliability by integrating proteanTecs deep agent and analytics solutions with the Exensio Platform. PDF Solutions and proteanTecs bring to the collaboration complementary expertise and market knowledge that combines PDF Solutions’ process characterization and semiconductor big data analytics solutions, with proteanTecs’ deep data offering for chip and system lifecycle analytics.

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The semiconductor industry is poised to grow in the next decade to become a trillion-dollar industry by 2030. About 70-percent of this growth is anticipated to be driven by three industries: automotive, computation and data storage, and wireless.¹ These markets rely on high performance electronics with uncompromised lifetime functionality, leading semiconductor companies to seek advanced approaches for characterization, volume production and in-field monitoring.

Through this collaboration, proteanTecs and PDF Solutions intend to offer combined solutions that leverage each company’s strengths, to enable data-driven insights and visibility in all stages of manufacturing. The collaboration includes PDF Solutions’ Exensio® Analytics platform, advanced AI/ML models and DEX™ data exchange network, and proteanTecs’ agents with cloud/edge analytics based on ML-driven chip telemetry. The combined solutions are intended to deliver unique benefits, such as adaptive test capabilities to further improve test quality and reduce DPPM (defective parts per million), enhanced device quality grading for downstream testing, and more insights around RMAs (return material authorizations) from in-field degradation monitoring.

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“Primarily addressing the needs of the data center and automotive markets, this is a collaboration where one plus one equals three,” said Uzi Baruch, Chief Strategy Officer at proteanTecs. “Many of the leading semiconductor companies rely on PDF Solutions for their semiconductor big data analytics. With PDF Solutions’ advanced analytics and proteanTecs’ silicon-proven deep data solutions, we can provide unprecedented insights and enrich production flows, enabling visibility into the device performance and software operations throughout its lifecycle.”

“By collaborating with proteanTecs, we are providing high-resolution insights on the SoC’s profile, health, and performance, enabling our mutual customers to achieve even greater benefits across the supply chain,” said Andrzej Strojwas, Chief Technology Officer at PDF Solutions. “Our solution for unified data is designed to enable semiconductor companies to leverage best-in-class technologies from our two companies.”

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Corvil Cloud Analytics provides the highly granular, real-time visibility required to understand the cause of variable performance that continues to impact real-time applications running in public cloud

New offering is simple to scale, easy to deploy and can be up and running in hours

Corvil Cloud Analytics measures every order, every market data tick and every packet needed to manage real-time performance in public cloud environments

Pico, a leading provider of mission-critical technology services, software, data and analytics for the financial markets community, has expanded the reach and visibility of industry leading Corvil Analytics into the cloud with the launch of Corvil Cloud Analytics.

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Pico’s Corvil Analytics has a 20-plus year legacy across financial services in extracting and correlating technology and transaction performance intelligence from global dynamic network environments. Corvil’s high throughput, lossless, granularly time-stamped data capture provides an incredibly rich data source that can be used for broader analytics and use cases, including trade analytics. Corvil is available across multiple environments including colocation and on-prem, and now those same attributes that make Corvil Analytics an industry leader are available in the cloud with Corvil Cloud Analytics.

“As companies look to move real-time applications to the cloud, they struggle with visibility when utilizing existing cloud monitoring solutions,” said Stacie Swanstrom, Chief Product Officer at Pico. “There is a need for deeper visibility to fill those voids, and Corvil Cloud Analytics is the solution, providing market-leading analytics for applications running in the cloud. Corvil Cloud Analytics provides our clients with the real-time analytics required to migrate their most critical workloads to the cloud, with confidence.”

Highlights of Corvil Cloud Analytics include:

Maximum Visibility: Measures every order, every market data tick and every packet to fill the missing gap of visibility needed to manage real-time performance in public cloud environments

Granular Instrumentation: Provides per-packet and per-application message analytics alongside Corvil’s AppAgent to instrument internal application performance
Corvil Analytics: Provides all functions of Corvil Analytics including network congestion analytics for public cloud infrastructure, and per-hop trading and market data analytics for cloud-hosted deployments

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Flexibility: Pay for only what is needed in the public cloud
Corvil Analytics is currently used by the world’s largest banks, exchanges, electronic market makers, quantitative hedge funds, data service providers and brokers. With the launch of Corvil Cloud Analytics, and as exchanges partner with the major cloud providers to bring trading into the cloud, Corvil can now provide a single pane of glass for monitoring colocation, on-prem and cloud environments together.

“We had the vision to provide clients the same technology, visibility and rich analytics they’ve come to rely on through Corvil,” Swanstrom said. “Since Corvil Cloud Analytics is software only, this accelerates our deployments and also provides an expedited avenue for proof-of-concept use cases. It’s now easier than ever for clients to access the platform so they can see firsthand what makes Corvil an industry leader in data analytics.”

Corvil Cloud Analytics provides the highly granular, real-time Corvil visibility required to understand the cause of variable performance that continues to impact real-time applications running in the public cloud. With cloud applications, there is no hardware CapEx costs, lead times, or shipping and installation challenges. Corvil Cloud Analytics is simple to scale, easy to deploy and can be up and running in hours instead of weeks. Corvil’s industry leading visibility and intelligence is now available for businesses wanting the competitive edge in the cloud.

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The Active Archive Alliance  announced that Seagate Technology has joined the organization. The Active Archive Alliance is a collaboration of industry-leading storage and IT vendors that collectively support the use of active archive solutions for data lifecycle management.

“The goals of Seagate and the Alliance are now more closely aligned than ever, making this the ideal time to join forces.”

“Seagate is proud to join the Active Archive Alliance to support their important mission to maximize data access across all storage media through its entire lifecycle. We are dramatically broadening our technology investments and product portfolio to offer customers hardware, software and services that solve their most challenging mass-capacity storage, transfer and migration needs,” said Ted Oade, product marketing director at Seagate. “The goals of Seagate and the Alliance are now more closely aligned than ever, making this the ideal time to join forces.”

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Active archives enable organizations to realize value from their vast and growing data sets and are an important part of data management roadmaps. Active archiving allows organizations to take control over their archival data, keeping it immediately accessible so users can find what they need when they need it. Broad integration across storage systems and platforms saves money and time, and cybersecurity measures like air gaps and strong user authentication keep data safe. Active archive file systems span multiple media types, including flash, disk, tape, optical, or cloud (public or private), file, block or object storage systems.

Seagate’s active archive solutions include the Seagate Lyve Cloud, an always-on mass-capacity object storage platform designed to enable multicloud freedom. With zero add-on charges for API or egress, Lyve Cloud helps enterprises overcome vendor lock-in, unpredictable cloud TCO, and multicloud data management complexity. Leveraging more than four decades of global leadership in storage technology innovation, Seagate’s object-storage-as-a-service offering empowers customers to store, move, and activate data at scale—all while maintaining full data security and control.

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Three additional service offerings support Seagate’s Lyve Cloud object storage service:

• Harness the freedom and scalability of open data lake architecture with Lyve Cloud Analytics, a fully managed, end-to-end platform for DataOps—including machine learning operations (MLOps)—combining frictionless object storage with flexible compute resources and pre-built analytic accelerators.
• Lyve™ Cloud Tape Migration and Storage, a service that helps customer digitize their old film/movie archives and transform their IT with migration services for legacy LTO tape archives for data reuse and monetization.
• Lyve™ Mobile is a high-capacity edge storage solution that enables businesses to aggregate, store, move and activate their data. Scalable, modular and vendor-agnostic, this integrated solution bundle eliminates network dependencies so you can transfer mass data sets in a fast, secure and efficient manner.

“We’re pleased to welcome Seagate to the Active Archive Alliance,” said Rich Gadomski, co-chairman of the Active Archive Alliance and head of tape evangelism at FUJIFILM Recording Media, USA, Inc. “Active archives are increasingly an essential component of organizations’ global data strategies due to their scalability, ease of use and straightforward integration into modern architectures. The Alliance is dedicated to bringing the best-of-breed technologies together to provide end users with the latest advancements, such as multi-vendor active archiving systems, sustainability, analytics, and AI/ML. These advantages contribute to active archiving’s fast marketplace growth and continual innovation.”

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