Best Data Management Software for Red Hat OpenShift

Find and compare the best Data Management software for Red Hat OpenShift in 2024

Use the comparison tool below to compare the top Data Management software for Red Hat OpenShift on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    New Relic Reviews
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    Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.
  • 2
    IBM Instana Reviews
    IBM®, Instana®, is the gold-standard of incident prevention. It offers automated full-stack transparency, 1-second granularity, and 3-second notification. In today's highly complex and dynamic cloud environments, an hour of downtime could cost you six figures or more. Traditional application performance monitoring tools (APMs) are not fast enough to keep pace or comprehensive enough to contextualize issues identified. They are also typically only available to super users, who must undergo months of training. IBM Instana Observability is a solution that goes beyond traditional APM by democratizing observability. Anyone in DevOps or SRE, Platform Engineering, ITOps, and Development can access the data they need with the context needed. Instana delivers high-fidelity data with a 1-second granularity, and end-toend traces, as well as the context of logical, physical, and mobile dependencies, across applications, web, and infrastructure.
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    Bacula Enterprise Reviews
    Bacula Enterprise offers a single platform that provides cloud backup and recovery software for the Modern Data Center. Bacula Enterprise backup & recovery software is ideal for medium and large businesses. It offers unique innovation, modern architecture and business value benefits, as well as low cost of ownership. Bacula Enterprise corporate backup software solution uses unique technologies that increase the interoperability of Bacula Enterprise into many IT environments, such as managed service providers, software vendors, cloud providers, enterprise data centers, and cloud providers. Bacula Enterprise is used by thousands of organizations around the world in mission-critical environments such as NASA, Texas A&M University and Unicredit. Bacula offers more security features than other vendors and advanced hybrid Cloud connectivity to Amazon S3, Google, Oracle, and many others.
  • 4
    Keen Reviews

    Keen

    Keen.io

    $149 per month
    Keen is a fully managed event streaming platform. Our real-time data pipeline, built on Apache Kafka, makes it easy to collect large amounts of event data. Keen's powerful REST APIs and SDKs allow you to collect event data from any device connected to the internet. Our platform makes it possible to securely store your data, reducing operational and delivery risks with Keen. Apache Cassandra's storage infrastructure ensures data is completely secure by transferring it via HTTPS and TLS. The data is then stored with multilayer AES encryption. Access Keys allow you to present data in an arbitrary way without having to re-architect or re-architect the data model. Role-based Access Control allows for completely customizable permission levels, down to specific queries or data points.
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    Appranix Reviews

    Appranix

    Appranix

    $25/unit/month
    Appranix Cloud App Resilience provides app-centric cloud resources backup, restore and cross-region disaster recovery. Appranix simplifies application resilience with an app-centric cloud-native model for backup, recovery, and DR compared to the legacy infrastructure-centric models. Appranix protects and recovers VMs and containers as well as databases. It also provides load balancers, VPC and security configurations. These components make up an entire application environment. Continuously improve your cloud application service level agreements (SLAs) with fully automated whole-application environment recovery and reports. No third-party data management required on top of your cloud infrastructure.
  • 6
    Sedai Reviews

    Sedai

    Sedai

    $10 per month
    Sedai intelligently finds resources, analyzes traffic patterns and learns metric performance. This allows you to manage your production environments continuously without any manual thresholds or human intervention. Sedai's Discovery engine uses an agentless approach to automatically identify everything in your production environments. It intelligently prioritizes your monitoring information. All your cloud accounts are on the same platform. All of your cloud resources can be viewed in one place. Connect your APM tools. Sedai will identify and select the most important metrics. Machine learning intelligently sets thresholds. Sedai is able to see all the changes in your environment. You can view updates and changes and control how the platform manages resources. Sedai's Decision engine makes use of ML to analyze and comprehend data at large scale to simplify the chaos.
  • 7
    Dataplane Reviews
    Dataplane's goal is to make it faster and easier to create a data mesh. It has robust data pipelines and automated workflows that can be used by businesses and teams of any size. Dataplane is more user-friendly and places a greater emphasis on performance, security, resilience, and scaling.
  • 8
    cnvrg.io Reviews
    An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.
  • 9
    SAS Visual Statistics Reviews
    SAS Visual Statistics allows multiple users to interactively explore data and then create and refine predictive models. Your statisticians and data scientists can use the most appropriate analytical modeling techniques to analyze your observations at a fine level. What will you get? The result? You can quickly build and refine models to target specific segments or groups, and run multiple scenarios simultaneously. To get better results, you can ask more "what-if" questions. You can also use an automatically generated score code to put your results into practice. Multiple users can interact with data visually. They can add, change, or remove outliers. You can instantly see how changes affect the predictive power of your model and make adjustments quickly. Data science teams have the freedom to work in the language they prefer, so they can make the most of their talents. SAS Visual Statistics combines all analytical assets.
  • 10
    SAS Viya Reviews
    SAS®, Viya®, data science offerings offer a comprehensive, scalable analytical environment that is quick and easy to use, allowing you to meet diverse business requirements. Automatically generated insights allow you to identify the most commonly used variables across all models, the most significant variables selected across models, and assess results for all models. Natural language generation capabilities allow you to create project summaries in plain language. This makes it easy to interpret reports. Analytics team members can add project notes and comments to the insights report to facilitate communication between team members. SAS allows you to embed open source code into an analysis and call open-source algorithms seamlessly within its environment. This allows for collaboration within your organization as users can program in the language they prefer. SAS Deep Learning with Python (DLPy) is also available on GitHub.
  • 11
    Red Hat Data Grid Reviews
    Red Hat®, Data Grid is a distributed, in-memory NoSQL datastore. Your applications can access, process and analyze data at high speed to provide a superior user experience. High performance, elastic scaling, always available You can quickly access your data using low latency data processing with memory (RAM), and distributed parallel execution. Linear scaling is possible with data partitioning and distributed across cluster nodes. Data replication across cluster nodes ensures high availability. Cross-datacenter geo-replication, clustering and fault tolerance allow you to be resilient and recover from disasters. A flexible, functionally rich NoSQL database store allows you to develop more efficiently and increase productivity. You can achieve complete data security through encryption and role-based access. Data Grid 7.3.10 offers a security enhancement to fix a CVE. Data Grid 7.3.10 must be upgraded as soon as possible.
  • 12
    SAS Visual Analytics Reviews
    A low-code visual interface allows you to create and deploy natural language chatbots. You can access data, reports, visualizations, and text responses. You can even apply analytics and AI using a natural language, conversational interface. You can connect to external services or configure bots within SAS to get more insights. Sending a message is all it takes to get insights from your data. Using suggestions and clearly identified related actions, you can quickly spot important relationships within your data. Machine learning and natural language explanations can be used to create stories and insights that are simple to understand and explain. Examine all options, find the reasons for what happened, and discover hidden opportunities in your data. Automately highlight key relationships, outliers and clusters to uncover vital insights that can inspire action.
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