Best Data Virtualization Software of 2024

Integrate with existing toolchain to automate self-service provisioning, refresh enterprise workloads, and integrate with existing tools. Through a rich set APIs and automation, data scientists can achieve high-performance data delivery and re-use. Any cloud data can be recovered at any time, at any scale, and beyond legacy solutions. Reduce the business impact of ransomware and cyber attacks by quickly recovering with immutable backups. Unified platform to protect, secure, keep, govern, and recover your data whether it is on-premises or cloud. Actifio's patented software platform turns data silos into data pipelines. Virtual Data Pipeline (VDP), provides full-stack data management - hybrid, on-premises, or multi-cloud -- from rich application integration, SLA based orchestration, flexible movement, data immutability, security, and SLA-based orchestration.

It unifies information across silos and scattered data for visibility across multiple sources in a single environment; whether in the cloud, spread across siloed databases, on instruments, in Big Data stores, or within various spreadsheets/documents, Enterprise Enabler can integrate all your data so you can make informed business decisions in real-time. By creating logical views from data starting at the source. This allows you to reuse, configure, test and deploy all your data in one integrated environment. You can analyze your business data as it happens to maximize the use and minimize costs, improve/refine business processes, and optimize the use of your assets. Our implementation time to market is between 50-90% shorter. We connect your sources so that you can make business decisions based upon real-time data.

The core technology that enables modern data integration and data management. Connect disparate, structured and unstructured data sources quickly. Catalog your entire data ecosystem. The data is kept in the source and can be accessed whenever needed. Adapt data models to the consumer's needs, even if they come from multiple sources. Your back-end technologies can be hidden from end users. You can secure the virtual model and use it to consume standard SQL and other formats such as SOAP, REST, SOAP, and OData. Access to all types data is easy. Data integration and data modeling capabilities are available. Active Data Catalog and self service capabilities for data and metadata discovery and preparation. Full data security and governance capabilities. Data queries executed quickly and intelligently. Real-time data delivery in all formats. Data marketplaces can be created. Data-driven strategies can be made easier by separating business applications and data systems.

AtScale accelerates and simplifies business intelligence. This results in better business decisions and a faster time to insight. Reduce repetitive data engineering tasks such as maintaining, curating, and delivering data for analysis. To ensure consistent KPI reporting across BI tools, you can define business definitions in one place. You can speed up the time it takes to gain insight from data and also manage cloud compute costs efficiently. No matter where your data is located, you can leverage existing data security policies to perform data analytics. AtScale's Insights models and workbooks allow you to perform Cloud OLAP multidimensional analysis using data sets from multiple providers - without any data prep or engineering. To help you quickly gain insights that you can use to make business decisions, we provide easy-to-use dimensions and measures.

Adaptive data virtualization™, a technology that allows enterprises to run existing applications on modern cloud storage warehouses without rewriting them or reconfiguring them, is Adaptive Data Virtualization™. Datometry HyperQ™, a cloud database management software, allows enterprises to adopt new cloud databases quickly, reduce ongoing operating expenses, and develop analytic capabilities to accelerate digital transformation. Datometry HyperQ virtualization software makes it possible to run any existing application on any cloud database. This allows applications and databases to interoperate. Enterprises can now choose the cloud database they prefer, without needing to rip, replace, or rewrite existing applications. Runtime compatibility with legacy data warehouse functions can be achieved through Transformation and Emulation. Transparent deployments on Azure, AWS, or GCP clouds. Applications can continue to use existing JDBC and ODBC connectors. Connects to the major cloud data warehouses Azure Synapse Analytics and AWS Redshift as well as Google BigQuery.

Oracle's server virtualization products are optimized for performance and efficiency. They support x86 architectures and a variety workloads like Linux, Windows, and Oracle Solaris. Oracle offers hypervisor-based solutions as well as virtualization built into hardware and Oracle operating system to provide the best solution for your entire computing environment.

VMware Cloud Director is a cloud service-delivery platform that's used by many of the most well-known cloud providers around the world to manage and operate successful cloud-service businesses. Cloud providers can deliver secure, efficient, elastic cloud resources to thousands upon thousands of IT teams around the globe using VMware Cloud Director. You can use VMware Cloud Director to build your cloud infrastructure. A policy-driven approach helps enterprises have isolated virtual resources and independent role-based authorization and fine-grained management." to "A policy driven approach to compute storage, networking, and security ensures that tenants have securely isolated virtual ressources, independent role based authentication, fine-grained management of their public cloud services." You can stretch data centers across sites and geographic locations; you can monitor resources from a single pane of glass that has multi-site aggregate views.

Cloud-native data integration with IBM Cloud Pak data enables you to accelerate AI innovation AI-powered data integration from anywhere. Your AI and analytics can only be as good as the data they are powered by. IBM®, DataStage®, for IBM Cloud Pak®, for Data provides high-quality data through a container-based architecture. It combines industry-leading data integration, DataOps, governance, and analytics on one data and AI platform. Automation speeds up administrative tasks, helping to reduce TCO. AI-based design accelerators, out-of-the box integration with DataOps or data science services accelerate AI innovation. Multicloud integration and parallelism allow you to deliver trusted data across hybrid and multicloud environments. The IBM Cloud Pak for Data platform allows you to manage the data and analytics lifecycle. Data science, event messaging, and data warehousing are some of the services offered. Automated load balancing and parallel engine.

Clonetab has many options to meet the needs of each site. Although Clonetab's core features will suffice for most site requirements, Clonetab also offers infrastructure to allow you to add custom steps to make it more flexible to meet your specific needs. Clonetab base module for Oracle Databases, eBusiness Suite, and PeopleSoft is available. Normal shell scripts used to perform refreshes can leave sensitive passwords in flat file. They may not have an audit trail to track who does refreshes and for which purpose. This makes it difficult to support these scripts, especially if the person who created them leaves the organization. Clonetab can be used to automate refreshes. Clonetab's features, such as pre, post and random scripts, target instances retention options like dblinks, concurrent processes, and appltop binary copying, allow users to automate most of their refresh steps. These steps can be done once. The tasks can then be scheduled.

There are many products that can help companies do this. But if your priorities include creating a data-driven company and being as efficient as possible, Fraxses is the best distributed data platform. Fraxses gives customers access to data on-demand and delivers powerful insights through a data mesh (or data fabric architecture) solution. A data mesh is a structure that can be placed over diverse data sources, connecting them and enabling them all to work together in a single environment. The Fraxses data platform is decentralized, unlike other data integration and virtualization platforms. Although Fraxses supports traditional data integration processes, the future lies with a new approach where data is delivered directly to users without the need of a centrally managed data lake or platform.

Varada's adaptive and dynamic big data indexing solution allows you to balance cost and performance with zero data-ops. Varada's big data indexing technology is a smart acceleration layer for your data lake. It remains the single source and truth and runs in the customer's cloud environment (VPC). Varada allows data teams to democratize data. It allows them to operationalize the entire data lake and ensures interactive performance without the need for data to be moved, modelled, or manually optimized. Our ability to dynamically and automatically index relevant data at the source structure and granularity is our secret sauce. Varada allows any query to meet constantly changing performance and concurrency requirements of users and analytics API calls. It also keeps costs predictable and under control. The platform automatically determines which queries to speed up and which data to index. Varada adjusts the cluster elastically to meet demand and optimize performance and cost.

The Hammerspace Global Data Environment makes network share visible and accessible from anywhere in the world to remote data centers and public cloud. Hammerspace is the only global file system that leverages our metadata replication, file granular data services and transparent data orchestration. This allows you to access your data wherever you need it, when you need. Hammerspace offers intelligent policies that help you manage and orchestrate your data. Hammerspace provides intelligent policies to manage and orchestrate your data.

Red Hat JBoss Data Virtualization allows for the easy access to trapped data and makes it easily consumable, unified and actionable. Red Hat JBoss Data Virtualization allows data from multiple systems to appear as a collection of tables in a local table. Access to heterogeneous data stores in real time via standards-based read/write. Facilitates application development and integration by simplifying the access to distributed data. Data consumer requirements are used to integrate and transform data semantics. Secure security infrastructure provides centralized access control and auditing. Transform fragmented data into actionable data at the speed that your business requires. Red Hat provides support and maintenance for major JBoss versions over specified time periods.

Traditional methods of integrating mainframe, ETL, warehouses, and building connectors are not fast enough or efficient enough to be useful for businesses today. Data virtualization is a new way to store and create more data on the mainframe than ever before. Data virtualization is the only way to close the gap and make mainframe data more accessible to developers and other applications. You can map your data once and then virtualize it for access anywhere, anytime. Your data can scale to your business goals. Data virtualization on z/OS removes the complexity that comes with working with mainframe resources. Data virtualization allows you to combine data from many sources into one logical data source. This makes it easier to connect mainframe data to your distributed applications. Combine mainframe data with location, social networks, and other distributed data.

Real-time monitoring and alarming rain using rain gauges to alert staff or trigger operations staff about specific flooding or potential sewer overflow. The "Distributed Rainfall Modeling Technique" (DRMT) is a method of monitoring and analyzing rainfall amounts at different locations in order to determine the amount of rain in non-monitored locations. Combining rainfall gauge rainfall data with rainfall radar data can produce improved rainfall coverage maps. Analysis of recent rainfall records to develop rainfall intensity-duration curves for comparing with the area's design intensity-duration-frequency curves and define the return periods of observed events (forensic analysis). Develop new intensity-duration-frequency curves for designing drainage system infrastructure (sewers, channels, storage facilities). Data analysis and flow monitoring are used to determine rainfall versus stormwater runoff response curves in order to calibrate drainage system models.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.