Best Data Management Software for Cloud Foundry

Find and compare the best Data Management software for Cloud Foundry in 2025

Use the comparison tool below to compare the top Data Management software for Cloud Foundry 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
    Splunk Enterprise Reviews
    Accelerate the transition from data to tangible business results with Splunk. Splunk Enterprise streamlines the process of gathering, analyzing, and leveraging the hidden potential of the vast data created by your technological framework, security measures, and enterprise applications—equipping you with the knowledge necessary to enhance operational efficiency and achieve business objectives. Effortlessly gather and index log and machine data from a variety of sources. Merge your machine data with information stored in relational databases, data warehouses, as well as Hadoop and NoSQL data repositories. The platform's multi-site clustering and automatic load balancing capabilities are designed to accommodate hundreds of terabytes of data daily, ensuring quick response times and uninterrupted access. Customizing Splunk Enterprise to suit various project requirements is straightforward with the Splunk platform. Developers have the flexibility to create bespoke Splunk applications or incorporate Splunk data into existing applications. Furthermore, applications developed by Splunk, our collaborators, and the community enhance and expand the functionalities of the Splunk platform, making it a versatile tool for organizations of all sizes. This adaptability ensures that users can extract maximum value from their data in a rapidly changing business landscape.
  • 3
    Lyftrondata Reviews
    If you're looking to establish a governed delta lake, create a data warehouse, or transition from a conventional database to a contemporary cloud data solution, Lyftrondata has you covered. You can effortlessly create and oversee all your data workloads within a single platform, automating the construction of your pipeline and warehouse. Instantly analyze your data using ANSI SQL and business intelligence or machine learning tools, and easily share your findings without the need for custom coding. This functionality enhances the efficiency of your data teams and accelerates the realization of value. You can define, categorize, and locate all data sets in one centralized location, enabling seamless sharing with peers without the complexity of coding, thus fostering insightful data-driven decisions. This capability is particularly advantageous for organizations wishing to store their data once, share it with various experts, and leverage it repeatedly for both current and future needs. In addition, you can define datasets, execute SQL transformations, or migrate your existing SQL data processing workflows to any cloud data warehouse of your choice, ensuring flexibility and scalability in your data management strategy.
  • 4
    Abstract Security Reviews
    Save your security teams from drowning in noise and hassle! With Abstract, they can focus on what truly matters without worrying about vendor lock-ins, SIEM migration costs or compromise on speed of access over storage! Abstract Security is an AI driven security data management platform that streamlines your data operations with noise reduction, AI based normalization and advanced threat analytics performed on live streaming data so you can analyze insights before routing it to any storage destination.
  • 5
    Spring Cloud Data Flow Reviews
    Microservices architecture enables efficient streaming and batch data processing specifically designed for platforms like Cloud Foundry and Kubernetes. By utilizing Spring Cloud Data Flow, users can effectively design intricate topologies for their data pipelines, which feature Spring Boot applications developed with the Spring Cloud Stream or Spring Cloud Task frameworks. This powerful tool caters to a variety of data processing needs, encompassing areas such as ETL, data import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server leverages Spring Cloud Deployer to facilitate the deployment of these data pipelines, which consist of Spring Cloud Stream or Spring Cloud Task applications, onto contemporary infrastructures like Cloud Foundry and Kubernetes. Additionally, a curated selection of pre-built starter applications for streaming and batch tasks supports diverse data integration and processing scenarios, aiding users in their learning and experimentation endeavors. Furthermore, developers have the flexibility to create custom stream and task applications tailored to specific middleware or data services, all while adhering to the user-friendly Spring Boot programming model. This adaptability makes Spring Cloud Data Flow a valuable asset for organizations looking to optimize their data workflows.
  • 6
    GenRocket Reviews
    Enterprise synthetic test data solutions. It is essential that test data accurately reflects the structure of your database or application. This means it must be easy for you to model and maintain each project. Respect the referential integrity of parent/child/sibling relations across data domains within an app database or across multiple databases used for multiple applications. Ensure consistency and integrity of synthetic attributes across applications, data sources, and targets. A customer name must match the same customer ID across multiple transactions simulated by real-time synthetic information generation. Customers need to quickly and accurately build their data model for a test project. GenRocket offers ten methods to set up your data model. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.
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