Denodo Platform 8.0 Features Automated Data Management with AI and ML
Latest version provides logical data fabric with end-to-end data management capabilities and augmented intelligence.
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Denodo Technologies Inc. has updated its Denodo Platform solution. The version 8.0 release is designed to accelerate hybrid/multicloud integration, automate data management with artificial intelligence (AI) and machine learning (ML), and improve performance with smart query acceleration. The platform provides intelligent recommendations, hyper performance, and platform-as-a-service (PaaS) support, which advances data virtualization into a logical data fabric.
“Data fabric is a hot, emerging market that delivers a unified, intelligent, and integrated end-to-end platform to support new and emerging use cases,” wrote Noel Yuhanna, vice president and principal analyst at Forrester Research, and author of The Forrester Wave: Enterprise Data Fabric, Q2 2020. That report also states, “Denodo is known for data virtualization, and over the years it has also evolved into a data fabric vendor. Denodo’s data fabric solution integrates key data management components, including data integration, data ingestion, data transformation, data governance and security, to support new and emerging use cases including customers 360, real-time and on-demand analytics, IoT analytics, and self-service analytics. In addition, Denodo’s AI/ML capabilities, as well as automation, continue to enhance its capabilities across data fabric components.”
Using data virtualization, the Denodo platform offers enterprise-grade data integration, management, and delivery comparable to legacy data integration techniques. The new release features agile data integration and governed data management designed to extend the platform into advanced hybrid and multicloud architectures and data science use cases.
Key functionalities of Denodo Platform 8.0 include:
- Hybrid cloud/multicloud integration: The new release enables organizations to accelerate data integration across different locations and leverages PaaS-style infrastructure automation and autoscaling support.
- Machine learning/data science automation: An ML-aided data catalog helps data scientists discover and prepare data for building complex data models and aids in supporting advanced analytics. With the integrated Apache Zeppelin Notebook, they can construct narratives combining queries with code, text, and graphics, and share it with fellow data scientists.
- API/microservices enhancements: New support for execution of GraphQL on top of logical data models promotes a no-code approach to the use of REST APIs and accelerates the adoption of advanced microservices architectures.
- Hyper performance: The newplatform maximizes query delegation and boosts performance to sub-second responses with AI-powered recommendations, advanced caching, and smart query acceleration.
- Unified web-based UI with single sign-on: Newly introduced interface unifies all design and runtime tools with single sign-on support facilitating rapid design, development, deployment, and management processes.
- Expanded cloud connectivity: The platform expands its widest array of adapters with new ones for Databricks Delta, Azure Synapse, Google BigQuery and cloud data stores such as AWS S3, Azure Data Lake Storage, and Google Cloud Storage.
For details visit www.denodo.com.