Why Oracle For Enterprise Big Data?

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Oracle’s big data platform turns disruptive technology into enterprise productivity. Preserve your company's existing investments in skills and technology even as you shift to enterprise big data architecture with less risk than with any other way.

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Nanosoft Technologies

We are a technology services company with a reservoir of highly skilled IT professionals. We are focused on designing solutions that improve the way businesses and people communicate with each other.We have built strong and proven competencies in business analysis and process development to provide solutions to business challenges. Our dedicated teams of Analysts, Developers, Network Engineers, Graphics Designers, Quality Assurance Engineers, Hardware upgrade and deployment specialists and Web Marketing Specialists are trained and experienced to create solutions for your unique business needs.

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BIG DATA MANAGEMENT

Enhance Your Customer Experience with Data-Centric AI

Article | July 5, 2022

Data-centric AI is a unique approach to machine learning that depends on the data scientist to design the complete pipeline from data purification and intake through model training. There is no need for a detailed understanding of AI algorithms in this method; instead, it is all about the data. The principle behind data-centric AI is simple: rather than training an algorithm first and then cleaning up the dirty dataset, begin with clean data and train an algorithm on that dataset. Why Is It Necessary to Centralize Datasets? A consolidated data platform can be utilized to produce a single source of truth, therefore simplifying and assuring accuracy. When a team concentrates on continual improvement, wasted time and resources are reduced. You can improve optimization by centralizing data. This is due to the increased opportunity for your team to enhance procedures and make better judgments. The capacity to exploit a single platform that promotes constant improvement in processes, products and operationalization models is provided by centralizing data. Data-Centric AI for Personalized Customer Experience Data-centric AI connects your data and analytics. It's used to detect common habits and preferences, tailor marketing campaigns, provide better suggestions, and much more. Data-Centric AI is being used to evaluate various types of data in order to assist organizations in making quicker, more efficient choices. It can be used to analyze client behavior and trends across several channels in order to provide personalized experiences. It enables applications and websites to adjust the information that individuals view according to their preferences, as well as advertisers to target specific consumers with tailored offers. What Will the Future of Data-Centric AI Look Like? Data-centric AI strives to provide a systematic approach to a wide range of domains, including product design and user experience. Data-centric AI is a systematic technique and technology that enables engineers and other data scientists to employ machine learning models in their own data studies. Moreover, the goal of data-centric AI is to build best practices that make data analysis approaches less expensive and easier for businesses to implement effortlessly.

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BIG DATA MANAGEMENT

Why Adaptive AI Can Overtake Traditional AI

Article | June 24, 2022

With the ever-changing technology world, company demands and results are no longer the norm. Businesses in a variety of sectors are using artificial intelligence (AI) technologies to solve complicated business challenges, build intelligent and self-sustaining solutions, and, ultimately, remain competitive at all times. To that aim, ongoing attempts are being made to reinvent AI systems in order to do more with less. Adaptive AI is a significant step in that direction. It has the potential to outperform standard machine learning (ML) models in the near future because of its ability to enable organizations to get greater results while spending less time, effort, and resources. The capacity of adaptive AI to enable enterprises to achieve greater outcomes while investing less time, effort, and assets is why it can overtake traditional AI models. Why Adaptive AI Overtakes Traditional AI Robust, Efficient and Agile Robustness, efficiency, and agility are the three basic pillars of Adaptive AI. The ability to achieve great algorithmic accuracy is referred to as robustness. The capacity to achieve reduced resource utilization is referred to as efficiency (for example, computer, memory, and power). Agility manages the ability to change operational circumstances in response to changing demands. Together, these three Adaptive AI principles provide the groundwork for super-capable AI inference for edge devices. Data-Informed Predictions A single pipeline is used by the adaptive learning approach. With this method, you can use a continually advanced learning approach that maintains the framework up-to-date and encourages it to achieve high levels of performance. The Adaptive Learning method examines and learns new changes made to the information and produces values, as well as their associated attributes. Moreover, it benefits from events that can modify market behavior in real-time and, as a result, maintains its accuracy consistently. Adaptive AI recognizes information from the operational environment and uses it to produce data-informed predictions. Closing Lines Adaptive AI will be utilized to meet changing AI computing requirements. Operational effectiveness depends on algorithmic performance and available computer resources. Edge AI frameworks that can change their computing demands effectively reduce compute and memory requirements. Adaptive AI is robust in CSPs' dynamic software environments, where inputs and outputs alter with each framework revamp. It can assist with network operations, marketing, customer service, IoT, security, and customer experience.

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BIG DATA MANAGEMENT

Data-Centric Approach for AI Development

Article | July 12, 2022

As AI has grown in popularity over the past decade, practitioners have concentrated on gathering as much data as possible, classifying it, preparing it for usage, and then iterating on model architectures and hyper-parameters to attain our desired objectives. While dealing with all of this data has long been known as laborious and time-consuming, it has typically been seen as an upfront, one-time step we take before entering into the essential modeling phase of machine learning. Data quality concerns, label noise, model drift, and other biases are all addressed in the same way: by collecting and labeling more data, followed by additional model iterations. The foregoing technique has worked successfully for firms with unlimited resources or strategic challenges. It doesn't work well for machine learning's long-tail issues, particularly those with fewer users and little training data. The discovery that the prevailing method of deep learning doesn't "scale down" to industry challenges has given birth to a new "trend" in the area termed "Data-Centric AI." Implementing a Data-Centric Approach for AI Development Leverage MLOps Practices Data-centric AI prioritizes data over models. Model selection, hyper-parameter tuning, experiment tracking, deployment, and monitoring take time. Data-centric approaches emphasize automating and simplifying ML lifecycle operations. Standardizing and automating model-building requires MLOps. MLOps automates machine learning lifecycle management pipelines. An organizational structure improves communication and cooperation. Involve Domain Expertise Data-centric AI development requires domain-specific datasets. Data scientists can overlook intricacies in various sectors, business processes, or even the same domain. Domain experts can give ground truth for the AI use case and verify whether the dataset truly portrays the situation. Complete and Accurate Data Data gaps cause misleading results. It's crucial to have a training dataset that correctly depicts the underlying real-world phenomenon. Data augmentation or creating synthetic data might be helpful if gathering comprehensive and representative data is costly or challenging for your use case.

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BIG DATA MANAGEMENT

A Modern Application Must Have: Multi-cloud Database

Article | July 6, 2022

To function well, modern apps require enormous amounts of diverse data from sensors, processes, interactions, etc. However, these apps cannot understand the unstructured big data and extract commercial value for effective operations unless this data is maintained properly. In today's age of cloud computing, apps gather and analyze data from various sources, but the data isn't always kept in the same database or format. While increasing overall complexity, several formats make it more difficult for apps to retain and use various data. Multi-model databases, a cutting-edge management system, provide a sophisticated approach to handling varied and unstructured data. A multi-model database allows various data models to natively utilize a single, integrated backend, as opposed to combining different database models. Why Has Multi-Model Database Become a Necessity for Modern Applications? Modern applications can store diverse data in a single repository owing to the flexible approach to database management, which improves agility and reduces data redundancy. Improve Reliability Each database might be a single point of failure for a larger system or application. Multi-model databases reduce failure points, enhancing data dependability and recovery time. Such recovery minimizes expenses and maintains customer engagement and application experience. Simplify Data Management Fragmented database systems benefit contemporary applications but complicate development and operations. Multi-model databases provide a single backend that maintains data integrity and fault tolerance, eliminating the need for different database systems, software licenses, developers, and administrators. Improve Fault Tolerance Modern apps must be fault-tolerant and respond promptly to failures promptly. Multi-model databases enable this by integrating several systems into a single backend. The integration provides system-wide failure tolerance. Closing Lines As applications get more complicated, so do their database requirements. However, connecting many databases and preserving consistency between data gathered from various sources is a time-consuming and expensive undertaking. Fortunately, multi-model databases provide an excellent option for generating the data models you want on a single backend.

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Spotlight

Nanosoft Technologies

We are a technology services company with a reservoir of highly skilled IT professionals. We are focused on designing solutions that improve the way businesses and people communicate with each other.We have built strong and proven competencies in business analysis and process development to provide solutions to business challenges. Our dedicated teams of Analysts, Developers, Network Engineers, Graphics Designers, Quality Assurance Engineers, Hardware upgrade and deployment specialists and Web Marketing Specialists are trained and experienced to create solutions for your unique business needs.

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BIG DATA MANAGEMENT,DATA VISUALIZATION

AtScale Announces Data Science and Enterprise AI Capabilities Within Semantic Layer Platform

AtScale | September 29, 2022

AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, today announced at the Semantic Layer Summit an expanded set of product capabilities for organizations working to accelerate the deployment and adoption of enterprise artificial intelligence (AI). These new capabilities leverage AtScale’s unique position within the data stack with support for common cloud data warehouse and lakehouse platforms including Google BigQuery, Microsoft Azure Synapse, Amazon Redshift, Snowflake, and Databricks. Organizations across every industry are racing to realize the true potential of their data science and enterprise AI investments. IDC predicts spending on AI/ML solutions will grow 19.6% with over $500B spent in 2023. Despite this investment, Gartner reports that only 54% of AI models built will make it into production, with organizations struggling to generate business outcomes that justify investment to operationalize models. This disconnect creates an enormous opportunity for solutions that can simplify and accelerate the path to business impact for AI/ML initiatives. The AtScale Enterprise semantic layer platform now incorporates two new capabilities available to all customers leveraging AtScale AI-Link: Semantic Predictions - Predictions generated by deployed AI/ML models can be written back to cloud data platforms through AtScale. These model-generated predictive statistics inherit semantic model intelligence, including dimensional consistency and discoverability. Predictions are immediately available for exploration by business users using common BI tools (AtScale supports connectivity to Looker, PowerBI, Tableau, and Excel) and can be incorporated into augmented analytics resources for a wider range of business users. Semantic predictions accelerate the business outcomes of AI investments by making it easier and more timely to work with, share, and use AI-generated predictions. Managed Features - AtScale creates a hub of centrally governed metrics and dimensional hierarchies that can be used to create a set of managed features for AI/ML models. Managed features can be sourced from the existing library of models maintained by data stewards or by individual work groups. Furthermore, new features created by AutoML or AI platforms can also become managed features. AtScale managed features inherit semantic context, making them more discoverable and easier to work with, consistently, at any stage in ML model development. Managed features can now be served directly from AtScale, or through a feature store like FEAST, to train models in AutoML or other AI platforms. “Despite rising investments, greater adoption of AI/ML within the modern enterprise is still hindered by complexity. “The need for AI is huge, exploration is on the rise, but many businesses are still not able to use the predictive insights AI models can generate. Here at AtScale we can leverage our unique position in the data stack to streamline and simplify how the business can consume and use AI immediately, generating faster time to value from their enterprise AI investments.” Gaurav Rao, Executive Vice President and General Manager of AI/ML at AtScale About AtScale AtScale enables smarter decision-making by accelerating the flow of data-driven insights. The company’s semantic layer platform simplifies, accelerates, and extends business intelligence and data science capabilities for enterprise customers across all industries. With AtScale, customers are empowered to democratize data, implement self-service BI and build a more agile analytics infrastructure for better, more impactful decision making.

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BIG DATA MANAGEMENT

Denodo Recognized as an Enterprise Data Fabric Leader by Independent Analyst Firm Evaluation

Denodo | June 27, 2022

Denodo, a leader in data management, today announced that Forrester Research, Inc., a leading independent technology and market research company, has positioned Denodo as a Leader in The Forrester Wave™: Enterprise Data Fabric, Q2 2022. According to the report, “Denodo is best fit for customers that are focusing on an enterprise-wide data fabric strategy to support BI, data collaboration, customer intelligence, data engineering, data science, IoT analytics, operational insights, and predictive analytics use cases.” The complete and complementary report, published on June 23, is available here. The Wave revealed that organizations want real-time, consistent, connected, and trusted data to support their critical business operations and insights. However, new data sources, slow data movement between platforms, rigid data transformation workflows, and governance rules, expanding data volume, and distributed data across clouds and on-premises, can cause organizations to fail when executing their data strategy. Forrester VP, Principal Analyst and author of the report, Noel Yuhanna wrote, “Denodo Technologies has been a longtime player in data virtualization and now supports data fabric with expanded integration, management, and delivery capabilities to support self-service BI, advanced analytics, and enterprise data services.” With some of the best in class and latest innovations, Denodo Platform offers: An augmented data catalog to facilitate data exploration, discovery, improved collaboration and data governance. Active metadata-based historical analysis, which serves as the foundation for AI processes. A semantic layer with extended metadata to enrich traditional technical information with business terms, tags, status, or documentation, to fuel improvements in self-service, security, and governance across all data assets. AI-based recommendations to learn from usage and simplify the entire lifecycle of the data management practice, including development, operations, performance tuning, etc. DataOps and multi-cloud provisioning to reduce management and operational cost and enable the system to be cloud-vendor agnostic. “We are thrilled to see our position as a leader in the Forrester Wave for Enterprise Data Fabric. “Most of our customers, including many Fortune 500, use our platform to build their enterprise data fabric for use cases such as IoT analytics, customer intelligence, fraud detection, real-time analytics, operational insights and many more, which are all areas where Denodo received the maximum score possible. We are looking forward to continuing to help organizations across the world unleash their data-driven decision making power to create an enterprise level logical data fabric.” Ravi Shankar, Senior Vice President and Chief Marketing Officer at Denodo About Denodo Denodo is a leader in data management. The award-winning Denodo Platform is the leading data integration, management, and delivery platform using a logical approach to enable self-service BI, data science, hybrid/multi-cloud data integration, and enterprise data services. Realizing more than 400% ROI and millions of dollars in benefits, Denodo’s customers across large enterprises and mid-market companies in 30+ industries have received payback in less than 6 months.

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BIG DATA MANAGEMENT

HPE Announces Solution Marketplace for Ezmeral Data Analytics Platform

Hewlett Packard Enterprise | March 19, 2021

Hewlett Packard Enterprise has refreshed its Ezmeral data and analytics programming portfolio adding another item and an ISV and open source solutions marketplace. Ezmeral Data Fabric is currently accessible independently as a product characterized, scale-out data store streamlined for data analytics abilities, just as a piece of the Container Platform and ML Ops contributions. It gives a data stockpiling layer across on-reason, private or public cloud, and edge conditions. The Ezmeral Container Platform and ML Ops are accessible as cloud benefits through HPE GreenLake now, and Ezmeral Data Fabric will be accessible as a GreenLake administration later on. ISVs can approve their application on Ezmerel for snappier arrangement because of another innovation environment program. An Ezmerel marketplace has likewise been dispatched that offers approved full-stack solutions from ISV accomplices, and open-source tasks like Apache Spark, and Tensorflow. Accomplices and clients can get ISV administrations through the marketplace however installments go straightforwardly to the ISV and accomplices don't right now approach discounts or motivations from HPE by buying ISV solutions as such. An assertion from HPE said that the organization intends to add more ISV accomplices to the marketplace each quarter. “The separate HPE Ezmeral Data Fabric data store and new HPE Ezmeral Marketplace provide enterprises with the environment of their choice, and with visibility and governance across all enterprise applications and data through an open, flexible, cloud experience everywhere,” chief technology officer and head of software Kumar Sreekanti said. “HPE Ezmeral is invaluable to our customers that are now embracing a digital-first strategy, as is evident with our continued growth into new enterprise accounts. The enterprises that use data and artificial intelligence effectively are better equipped to evolve rapidly in a dynamic, constantly changing marketplace.” HPE's head of the Ezmeral group for APJ Celine Siow told CRN the marketplace was "essentially a collaborative, one-stop shop for HPE Ezmeral-validated Independent Software Vendor (ISV) applications". "The marketplace has been launched to help our clients and partners adopt integrated solutions that combine HPE Ezmeral software with validated industry-leading, third-party, commercial and open-source applications. "Both partners and clients can buy HPE Ezmeral and the solutions available on the marketplace directly from the ISVs. For our channel partners, they have the ability to quickly and easily roll out containers in a hybrid and multicloud world for their customers. "Partners get the flexibility Ezmeral provides for deployments from edge to cloud, with the ability to seamlessly move data in a unified software-defined environment. We will continue to strengthen and build out the ISV marketplace."

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BIG DATA MANAGEMENT,DATA VISUALIZATION

AtScale Announces Data Science and Enterprise AI Capabilities Within Semantic Layer Platform

AtScale | September 29, 2022

AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, today announced at the Semantic Layer Summit an expanded set of product capabilities for organizations working to accelerate the deployment and adoption of enterprise artificial intelligence (AI). These new capabilities leverage AtScale’s unique position within the data stack with support for common cloud data warehouse and lakehouse platforms including Google BigQuery, Microsoft Azure Synapse, Amazon Redshift, Snowflake, and Databricks. Organizations across every industry are racing to realize the true potential of their data science and enterprise AI investments. IDC predicts spending on AI/ML solutions will grow 19.6% with over $500B spent in 2023. Despite this investment, Gartner reports that only 54% of AI models built will make it into production, with organizations struggling to generate business outcomes that justify investment to operationalize models. This disconnect creates an enormous opportunity for solutions that can simplify and accelerate the path to business impact for AI/ML initiatives. The AtScale Enterprise semantic layer platform now incorporates two new capabilities available to all customers leveraging AtScale AI-Link: Semantic Predictions - Predictions generated by deployed AI/ML models can be written back to cloud data platforms through AtScale. These model-generated predictive statistics inherit semantic model intelligence, including dimensional consistency and discoverability. Predictions are immediately available for exploration by business users using common BI tools (AtScale supports connectivity to Looker, PowerBI, Tableau, and Excel) and can be incorporated into augmented analytics resources for a wider range of business users. Semantic predictions accelerate the business outcomes of AI investments by making it easier and more timely to work with, share, and use AI-generated predictions. Managed Features - AtScale creates a hub of centrally governed metrics and dimensional hierarchies that can be used to create a set of managed features for AI/ML models. Managed features can be sourced from the existing library of models maintained by data stewards or by individual work groups. Furthermore, new features created by AutoML or AI platforms can also become managed features. AtScale managed features inherit semantic context, making them more discoverable and easier to work with, consistently, at any stage in ML model development. Managed features can now be served directly from AtScale, or through a feature store like FEAST, to train models in AutoML or other AI platforms. “Despite rising investments, greater adoption of AI/ML within the modern enterprise is still hindered by complexity. “The need for AI is huge, exploration is on the rise, but many businesses are still not able to use the predictive insights AI models can generate. Here at AtScale we can leverage our unique position in the data stack to streamline and simplify how the business can consume and use AI immediately, generating faster time to value from their enterprise AI investments.” Gaurav Rao, Executive Vice President and General Manager of AI/ML at AtScale About AtScale AtScale enables smarter decision-making by accelerating the flow of data-driven insights. The company’s semantic layer platform simplifies, accelerates, and extends business intelligence and data science capabilities for enterprise customers across all industries. With AtScale, customers are empowered to democratize data, implement self-service BI and build a more agile analytics infrastructure for better, more impactful decision making.

Read More

BIG DATA MANAGEMENT

Denodo Recognized as an Enterprise Data Fabric Leader by Independent Analyst Firm Evaluation

Denodo | June 27, 2022

Denodo, a leader in data management, today announced that Forrester Research, Inc., a leading independent technology and market research company, has positioned Denodo as a Leader in The Forrester Wave™: Enterprise Data Fabric, Q2 2022. According to the report, “Denodo is best fit for customers that are focusing on an enterprise-wide data fabric strategy to support BI, data collaboration, customer intelligence, data engineering, data science, IoT analytics, operational insights, and predictive analytics use cases.” The complete and complementary report, published on June 23, is available here. The Wave revealed that organizations want real-time, consistent, connected, and trusted data to support their critical business operations and insights. However, new data sources, slow data movement between platforms, rigid data transformation workflows, and governance rules, expanding data volume, and distributed data across clouds and on-premises, can cause organizations to fail when executing their data strategy. Forrester VP, Principal Analyst and author of the report, Noel Yuhanna wrote, “Denodo Technologies has been a longtime player in data virtualization and now supports data fabric with expanded integration, management, and delivery capabilities to support self-service BI, advanced analytics, and enterprise data services.” With some of the best in class and latest innovations, Denodo Platform offers: An augmented data catalog to facilitate data exploration, discovery, improved collaboration and data governance. Active metadata-based historical analysis, which serves as the foundation for AI processes. A semantic layer with extended metadata to enrich traditional technical information with business terms, tags, status, or documentation, to fuel improvements in self-service, security, and governance across all data assets. AI-based recommendations to learn from usage and simplify the entire lifecycle of the data management practice, including development, operations, performance tuning, etc. DataOps and multi-cloud provisioning to reduce management and operational cost and enable the system to be cloud-vendor agnostic. “We are thrilled to see our position as a leader in the Forrester Wave for Enterprise Data Fabric. “Most of our customers, including many Fortune 500, use our platform to build their enterprise data fabric for use cases such as IoT analytics, customer intelligence, fraud detection, real-time analytics, operational insights and many more, which are all areas where Denodo received the maximum score possible. We are looking forward to continuing to help organizations across the world unleash their data-driven decision making power to create an enterprise level logical data fabric.” Ravi Shankar, Senior Vice President and Chief Marketing Officer at Denodo About Denodo Denodo is a leader in data management. The award-winning Denodo Platform is the leading data integration, management, and delivery platform using a logical approach to enable self-service BI, data science, hybrid/multi-cloud data integration, and enterprise data services. Realizing more than 400% ROI and millions of dollars in benefits, Denodo’s customers across large enterprises and mid-market companies in 30+ industries have received payback in less than 6 months.

Read More

BIG DATA MANAGEMENT

HPE Announces Solution Marketplace for Ezmeral Data Analytics Platform

Hewlett Packard Enterprise | March 19, 2021

Hewlett Packard Enterprise has refreshed its Ezmeral data and analytics programming portfolio adding another item and an ISV and open source solutions marketplace. Ezmeral Data Fabric is currently accessible independently as a product characterized, scale-out data store streamlined for data analytics abilities, just as a piece of the Container Platform and ML Ops contributions. It gives a data stockpiling layer across on-reason, private or public cloud, and edge conditions. The Ezmeral Container Platform and ML Ops are accessible as cloud benefits through HPE GreenLake now, and Ezmeral Data Fabric will be accessible as a GreenLake administration later on. ISVs can approve their application on Ezmerel for snappier arrangement because of another innovation environment program. An Ezmerel marketplace has likewise been dispatched that offers approved full-stack solutions from ISV accomplices, and open-source tasks like Apache Spark, and Tensorflow. Accomplices and clients can get ISV administrations through the marketplace however installments go straightforwardly to the ISV and accomplices don't right now approach discounts or motivations from HPE by buying ISV solutions as such. An assertion from HPE said that the organization intends to add more ISV accomplices to the marketplace each quarter. “The separate HPE Ezmeral Data Fabric data store and new HPE Ezmeral Marketplace provide enterprises with the environment of their choice, and with visibility and governance across all enterprise applications and data through an open, flexible, cloud experience everywhere,” chief technology officer and head of software Kumar Sreekanti said. “HPE Ezmeral is invaluable to our customers that are now embracing a digital-first strategy, as is evident with our continued growth into new enterprise accounts. The enterprises that use data and artificial intelligence effectively are better equipped to evolve rapidly in a dynamic, constantly changing marketplace.” HPE's head of the Ezmeral group for APJ Celine Siow told CRN the marketplace was "essentially a collaborative, one-stop shop for HPE Ezmeral-validated Independent Software Vendor (ISV) applications". "The marketplace has been launched to help our clients and partners adopt integrated solutions that combine HPE Ezmeral software with validated industry-leading, third-party, commercial and open-source applications. "Both partners and clients can buy HPE Ezmeral and the solutions available on the marketplace directly from the ISVs. For our channel partners, they have the ability to quickly and easily roll out containers in a hybrid and multicloud world for their customers. "Partners get the flexibility Ezmeral provides for deployments from edge to cloud, with the ability to seamlessly move data in a unified software-defined environment. We will continue to strengthen and build out the ISV marketplace."

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