Best Data Management Software for Cloud - Page 89
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Compare the Top Data Management Software for Cloud as of June 2026 - Page 89
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Fosfor Decision Cloud
Fosfor
Everything you need to make better business decisions. The Fosfor Decision Cloud unifies the modern data ecosystem to deliver the long-sought promise of AI: enhanced business outcomes. The Fosfor Decision Cloud unifies the components of your data stack into a modern decision stack, built to amplify business outcomes. Fosfor works seamlessly with its partners to create the modern decision stack, which delivers unprecedented value from your data investments. -
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Oracle Enterprise Metadata Management (OEMM) is a comprehensive metadata management platform. OEMM can harvest and catalog metadata from virtually any metadata provider, including relational, Hadoop, ETL, BI, data modeling, and many more. OEMM however is not just a metadata repository, OEMM allows for interactive searching and browsing of the metadata as well as providing data lineage, impact analysis, semantic definition and semantic usage analysis for any metadata asset within the catalog. OEMM's advanced algorithms stitch together metadata from each of the providers providing the complete path of data from source to report or vice versa. OEMM supports virtually any metadata provider including: Data modeling tools, databases, CASE tools, Hadoop, ETL engines, Warehouses, BI, EAI environments, as well as many more.
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SAP Information Steward software supports data profiling and monitoring and information policy management. As the information governance layer of SAP Business Technology Platform, it can help you anticipate risk and drive better business outcomes. Combine data profiling, data lineage, and metadata management to gain continuous insight into the integrity of your enterprise data model. Gain a better understanding of data quality across your data management landscape,while accessing and analyzing metrics with intuitive dashboards and scorecards. Improve enterprise information management initiatives by supporting analysts, data stewards, and IT experts with consistent validation rules and guidelines. Discover, assess, define, monitor, and improve the quality of your enterprise data assets with data profiling and metadata management – all with one solution. Forecast the potential savings of improved data quality by running what-if analyses.
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Data360 Analyze
Precisely
The most successful businesses have common denominators: maximizing organizational efficiencies, mitigating risk, growing revenue and innovating – fast. Data360 Analyze is the fastest way to aggregate and organize large amounts of data to uncover valuable insights across business units. Easily access, prep and analyze quality data through its intuitive browser-based architecture. A solid understanding of your organization’s data landscape can shed light on disparate data sources, missing and outlying values and anomalies in data logic. Accelerate the discovery, validation, transformation and blending of data from across your organization to deliver accurate, relevant and trusted information for analysis. Visual data inspection and lineage allow you to trace and access data at any step within the data flow analytic process to collaborate with other stakeholders and build confidence and trust in the data and insights. -
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Accurity
Accurity
With Accurity, the all-in-one data intelligence platform, you get a company-wide understanding and complete trust in your data — speed up business-critical decision making, increase your revenue, reduce your costs, and ensure your company’s data compliance. Equipped with timely, relevant, and accurate data, you can successfully satisfy and engage with your customers, elevating your brand awareness and driving sales conversions. With everything accessible from a single interface, automated quality checks, and data quality issue workflows, you can lower personnel and infrastructure costs, and spend time utilizing your data rather than just managing it. Discover real value in your data by revealing and removing inefficiencies, improving your decision-making processes, and finding valuable product and customer information to boost your company’s innovation. -
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Adapt and respond to change faster and more effectively by managing your master data, whether migrating an application to the cloud; managing the effects of mergers and acquisitions; or reconciling metadata differences across business functions and applications. Import lists or hierarchies from contributing applications. Save time without having to validate data on entry, even with dirty data. Improve the quality of data within an application or business domain by validating separate perspectives, called viewpoints. Migrate and map enterprise data elements and ongoing changes across public, private, and hybrid cloud environments from Oracle or third parties. Create new or change existing enterprise data, relationships, and attributes with requests in an easy-to-understand shopping cart interface.
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Global IDs
Global IDs
Find out some of the best platform features provided by Global IDs, which brings in a set of Enterprise Data Solutions like data governance, data compliance, cloud migration, rationalization, privacy, analytics & much more! Global IDs EDA Platform feature comprises a set of core functions: automated discovery and profiling, data classification, data lineage, data quality and more – that render data transparent, trustworthy, and explainable across the ecosystem. Global IDs EDA platform architecture is designed for integration from the ground up with all platform functionality accessible via APIs. Global IDs EDA platform automates data management for enterprises of any size or data ecosystem. -
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OpenText File Intelligence
OpenText
Most organizations don’t know where and what information is contained in their documents, including sensitive and regulated information, such as PII and PCI. Much of the content resides outside of managed systems, relying on manual enforcement of governance policies and procedures by individuals, increasing legal and regulatory risk. With increased regulations, litigation and internal audits, understanding what content exists, where it resides and what actions are necessary is critical. OpenText™ File Intelligence is a file analysis application that is quickly deployed, easy to use and provides enterprises with robust functionality to catalog unmanaged sources (i.e., file shares, email, etc.), identify and analyze content files and support decision making to ensure the appropriate management of critical and valuable content. -
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Diwo
Diwo
Make faster, smarter business decisions with AI‑powered insight and actionable recommendations. Learn how Decision Intelligence (DI) is transforming analytics by enabling in-the-moment awareness of your business and immediate decision making. Diwo automates the process of analyzing, correlating and synthesizing your data to provide timely, relevant insights. It leverages AI and contextual intelligence to continuously monitor your data environment and deliver actionable recommendations. Diwo automatically surfaces opportunities and quantifies their potential impact to your business. More importantly, Diwo then provides recommendations that you validate and put into action. Diwo uses AI and contextual intelligence to automatically identify opportunities, detail the trade-offs, apply your business context and deliver the best advice to maximize results. -
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An intuitive, low-code visual interface enables you to create and deploy custom, natural language chatbots. Get text responses, access data, reports and visualizations, and even apply analytics and AI through a natural language, conversational interface. Configure bots within the SAS environment for easier access to insights, or connect to external services to roll them out to the world. Now getting insights from data is as easy as sending a message. Quickly spot important relationships in your data using suggestions and clearly identified related measures. Use machine learning and natural language explanations to find, visualize and narrate stories and insights that are easy to understand and explain. Find out why something happened, examine all options and uncover opportunities hidden deep in your data. Automatically highlight key relationships, outliers, clusters and more to reveal vital insights that inspire action.
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Crisp Data Platform
Crisp
At Crisp, we leverage the power of the cloud to connect and analyze all of your data sources in real-time, providing you with the most meaningful insights and trends for your business. Taking your data and leveraging it with thousands of brands, distributors, and retailers is the Crisp way. We provide next-generation industry insights and trends; setting a new standard for retailer/distributor/brand collaboration. Your data is visualized in a dashboard that is easy-to-read, easy-to-share and always up-to-date. You have lots of data. But what do you do with all of it? And how can you harness it into insights you can use? Crisp gives you the data you need, with the ability to leverage it for a true competitive advantage. You can analyze and organize multiple sources of data, and then collect and transform it into unified, actionable insights for a supply chain you can rely on. -
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PeerLogix
PeerLogix
PeerLogix (OTC: LOGX) is the recognized leader in the global OTT data-as-a-service space. The company tracks viewership data of more than 50,000 premium TV shows and movies across more than 180 million households as streamed from virtually every open-source OTT network, globally. This data can be leveraged to create actionable insights for advertisers, content creators, OTT platforms and strategic investors. PeerLogix observes, catalogs, and analyzes a massive amount of raw OTT viewership data. We then collate and package these data sets into unique products, both pre-constructed and customized, creating actionable value across an evolving and expanding set of use cases. Media buyers and data platforms utilize Peerlogix’s OTT viewership data to target advertising campaigns directly to households streaming OTT TV shows and movies, based on real-time viewership behaviors. -
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ActiveNav
ActiveNav
Identify sensitive data, optimize storage, and comply with privacy regulations. Take control of your sensitive data using a hybrid-cloud platform that allows you to quickly discover and map data across diverse data repositories. ActiveNav’s Inventory provides the insights you need to support all your data initiatives. By isolating and visually depicting data at scale, you can manage risky, stale data and make informed content decisions. Our platform tackles the hardest challenges behind discovering and mapping unstructured data, enabling you to gain value from your data like never before. Personal, sensitive data is hiding everywhere in your organization: on-premises, in the cloud, on file shares and servers, and throughout numerous other repositories. The platform is uniquely built to handle the challenges of mapping unstructured data repositories, enabling risk mitigation and compliance with evolving privacy laws and regulations. -
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Metaflow
Netflix
Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. Use Metaflow with your favorite data science libraries, such as Tensorflow or SciKit Learn, and write your models in idiomatic Python code with not much new to learn. Metaflow also supports the R language. Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions and tracks all your experiments and data automatically. It allows you to inspect results easily in notebooks. Metaflow comes packaged with the tutorials, so getting started is easy. You can make copies of all the tutorials in your current directory using the metaflow command line interface. -
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Sudo Platform
Anonyome Labs
Sudo Platform is an API-first, developer-focused ecosystem that delivers the tools necessary to empower our partners to quickly and completely deliver to end-user consumers the necessary capabilities to protect and control their personal information while navigating the digital world. It provides a modular, quick to implement, and powerful collection of the most important digital privacy and cyber safety tools. including safe and private browsing, password management, VPN, virtual cards, encrypted and open communications, and decentralized identity. This developer-focused platform includes: Developer-focused documentation API-first ecosystem SDK source code via GitHub Sample applications for test-to-deploy of various capabilities Vendor-brandable (white-label) apps for quick go-to-market deployments. -
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StorCentric Data Mobility Suite
StorCentric
StorCentric Data Mobility Suite (DMS), an all-inclusive software solution, empowers organizations to seamlessly move data where it needs to be. DMS is a cloud-enabled solution that supports data migration, data replication, and data synchronization across mixed environments including disk, tape, and cloud to maximize ROI by eliminating data silos. DMS supports vendor-agnostic file replications and synchronization and is easily deployed and managed on a non-proprietary server. DMS can transfer millions of files simultaneously and protects data in transit to and from the cloud with SSL encryption. DMS streamlines point-to-point data movement and tackles data flow requirements from any storage platform to another. Fine-grained filtering and continuous incremental updates alleviate the challenges of moving and consolidating data across heterogeneous environments. DMS enables files to be synchronized across multiple storage repositories, including disk and tape. -
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Spring Cloud Data Flow
Spring
Microservice-based streaming and batch data processing for Cloud Foundry and Kubernetes. Spring Cloud Data Flow provides tools to create complex topologies for streaming and batch data pipelines. The data pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a range of data processing use cases, from ETL to import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server uses Spring Cloud Deployer, to deploy data pipelines made of Spring Cloud Stream or Spring Cloud Task applications onto modern platforms such as Cloud Foundry and Kubernetes. A selection of pre-built stream and task/batch starter apps for various data integration and processing scenarios facilitate learning and experimentation. Custom stream and task applications, targeting different middleware or data services, can be built using the familiar Spring Boot style programming model. -
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Spark Streaming
Apache Software Foundation
Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part. By running on Spark, Spark Streaming lets you reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state. Build powerful interactive applications, not just analytics. Spark Streaming is developed as part of Apache Spark. It thus gets tested and updated with each Spark release. You can run Spark Streaming on Spark's standalone cluster mode or other supported cluster resource managers. It also includes a local run mode for development. In production, Spark Streaming uses ZooKeeper and HDFS for high availability. -
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Samza
Apache Software Foundation
Samza allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka. Battle-tested at scale, it supports flexible deployment options to run on YARN or as a standalone library. Samza provides extremely low latencies and high throughput to analyze your data instantly. Scales to several terabytes of state with features like incremental checkpoints and host-affinity. Samza is easy to operate with flexible deployment options - YARN, Kubernetes or standalone. Ability to run the same code to process both batch and streaming data. Integrates with several sources including Kafka, HDFS, AWS Kinesis, Azure Eventhubs, K-V stores and ElasticSearch. -
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Apache Beam
Apache Software Foundation
The easiest way to do batch and streaming data processing. Write once, run anywhere data processing for mission-critical production workloads. Beam reads your data from a diverse set of supported sources, no matter if it’s on-prem or in the cloud. Beam executes your business logic for both batch and streaming use cases. Beam writes the results of your data processing logic to the most popular data sinks in the industry. A simplified, single programming model for both batch and streaming use cases for every member of your data and application teams. Apache Beam is extensible, with projects such as TensorFlow Extended and Apache Hop built on top of Apache Beam. Execute pipelines on multiple execution environments (runners), providing flexibility and avoiding lock-in. Open, community-based development and support to help evolve your application and meet the needs of your specific use cases. -
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Apache Flume
Apache Software Foundation
Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault-tolerant with tunable reliability mechanisms and many failovers and recovery mechanisms. It uses a simple extensible data model that allows for online analytic applications. The Apache Flume team is pleased to announce the release of Flume 1.8.0. Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming event data. -
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ksqlDB
Confluent
Now that your data is in motion, it’s time to make sense of it. Stream processing enables you to derive instant insights from your data streams, but setting up the infrastructure to support it can be complex. That’s why Confluent developed ksqlDB, the database purpose-built for stream processing applications. Make your data immediately actionable by continuously processing streams of data generated throughout your business. ksqlDB’s intuitive syntax lets you quickly access and augment data in Kafka, enabling development teams to seamlessly create real-time innovative customer experiences and fulfill data-driven operational needs. ksqlDB offers a single solution for collecting streams of data, enriching them, and serving queries on new derived streams and tables. That means less infrastructure to deploy, maintain, scale, and secure. With less moving parts in your data architecture, you can focus on what really matters -- innovation. -
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Apache Heron
Apache Software Foundation
Heron is built with a wide array of architectural improvements that contribute to high-efficiency gains. Heron is API compatible with Apache Storm and hence no code change is required for migration. Easily debug and identify the issues in topologies, allowing faster iteration during development. Heron UI gives a visual overview of each topology to visualize hot spot locations and detailed counters for tracking progress and troubleshooting. Heron is highly scalable both in the ability to execute large number of components for each topology and the ability to launch and track large numbers of topologies. -
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InfinyOn Cloud
InfinyOn
InfinyOn has architected a programmable continuous intelligence platform for data in motion. Unlike other event streaming platforms that were built on Java, Infinyon Cloud is built on Rust and delivers industry leading scale and security for real-time applications. Ready to use programmable connectors that shape data events in real-time. Provision intelligent analytics pipelines that refine, protect, and correlate events in real-time. Attach programmable connectors to dispatch events and notify stakeholders. Each connector is either a source, which imports data, or a sink, which exports data. Connectors may be deployed in one of two ways: as a Managed Connector, in which the Fluvio cluster provisions and manages the connector; or as a Local Connector, in which you manually launch the connector as a docker container where you want it. Additionally, connectors conceptually have four stages, where each stage has distinct responsibilities. -
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Pandio
Pandio
Connecting systems to scale AI initiatives is complex, expensive, and prone to fail. Pandio’s cloud-native managed solution simplifies your data pipelines to harness the power of AI. Access your data from anywhere at any time in order to query, analyze, and drive to insight. Big data analytics without the big cost. Enable data movement seamlessly. Streaming, queuing and pub-sub with unmatched throughput, latency, and durability. Design, train, and deploy machine learning models locally in less than 30 minutes. Accelerate your path to ML and democratize the process across your organization. And it doesn’t require months (or years) of disappointment. Pandio’s AI-driven architecture automatically orchestrates your models, data, and ML tools. Pandio works with your existing stack to accelerate your ML initiatives. Orchestrate your models and messages across your organization.Starting Price: $1.40 per hour -
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Akka
Akka
Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications for Java and Scala. Akka Insights is intelligent monitoring and observability purpose-built for Akka. Actors and Streams let you build systems that scale up, using the resources of a server more efficiently, and out, using multiple servers. Building on the principles of The Reactive Manifesto Akka allows you to write systems that self-heal and stay responsive in the face of failures. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure. Fully async and streaming HTTP server and client provides a great platform for building microservices. Streaming integrations with Alpakka. -
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Red Hat OpenShift Streams
Red Hat
Red Hat® OpenShift® Streams for Apache Kafka is a managed cloud service that provides a streamlined developer experience for building, deploying, and scaling new cloud-native applications or modernizing existing systems. Red Hat OpenShift Streams for Apache Kafka makes it easy to create, discover, and connect to real-time data streams no matter where they are deployed. Streams are a key component for delivering event-driven and data analytics applications. The combination of seamless operations across distributed microservices, large data transfer volumes, and managed operations allows teams to focus on team strengths, speed up time to value, and lower operational costs. OpenShift Streams for Apache Kafka includes a Kafka ecosystem and is part of a family of cloud services—and the Red Hat OpenShift product family—which helps you build a wide range of data-driven solutions. -
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Axibase Enterprise Reporter (AER) is a unified IT reporting solution for performance monitoring and capacity planning based on linked data and self-service concepts. The linked data architecture implemented in AER allows it to deliver reporting capabilities on top of underlying monitoring systems simultaneously, without copying the data. AER is pre-integrated with IBM Tivoli, Microsoft System Center Operations Manager, HP Openview and Performance Manager, BMC ProactiveNet, VMWare vCenter, Oracle Enterprise Manager, SAP HANA, NetApp OnCommand, WhatsUp, Dynatrace, Entuity and other solutions. In addition, AER provides the universal adapter for integration with any monitoring system or a custom data source that supports JDBC connectivity. Leveraging AER as a single point of access to IT infrastructure metrics, systems administrators and application support teams are able to execute and automate performance monitoring and capacity planning tasks with minimal effort.
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Shapelets
Shapelets
Powerful computing at your fingertips. Parallel computing, groundbreaking algorithms, so what are you waiting for? Designed to empower data scientists in business. Get the fastest computing in an all-inclusive time-series platform. Shapelets provides you with analytical features such as causality, discords and motif discovery, forecasting, clustering, etc. Run, extend and integrate your own algorithms into the Shapelets platform to make the most of Big Data analysis. Shapelets integrates seamlessly with any data collection and storage solution. It also integrates with MS Office and any other visualization tool to simplify and share insights without any technical acumen. Our UI works with the server to bring you interactive visualizations. You can make the most of your metadata and represent it in the many different visual graphs provided by our modern interface. Shapelets enables users from the oil, gas, and energy industry to perform real-time analysis of operational data. -
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Plutoshift
Plutoshift
Plutoshift's Operational Data Platform (ODP) delivers automated performance monitoring and predictive analysis for physical infrastructure to drive efficiency, reduce cost and waste, and help build more resilient sustainable businesses. Extracting and processing raw data for analysis is often the biggest challenge to delivering value. Plutoshift ingests, processes, and curates all your infrastructure, business, and even third-party data, creating a foundation for generating insights. We automate and maintain the entire data pipeline without the need for additional IT resources. Plutoshift provides organizations with an always-on view of their operations at the workflow, site, and enterprise level. Operators have an immediate view of trends and KPIs for every process and asset. With configurable alerts, response tracking, and contextual reports, teams can quickly identify problems, prevent downtime, and uncover the root cause of issues.
