Notes about Cutting-Edge Technologies and Everything

  Database management systems continue their move to the cloud — a move that is producing an increasingly complex landscape of vendors and offerings. This Magic Quadrant will help data and analytics leaders make the right choices in a complex and fast-evolving market. Strategic Planning Assumptions By 2025, cloud preference for data management will substantially reduce the vendor landscape while the growth in multicloud will increase the complexity for data governance and integration. By 2022, cloud database management system (DBMS) revenue will account for 50% of the total DBMS market revenue. These DBMSs reflect optimization strategies designed to support transactions and/or analytical processing for one or more of the following use cases:     Traditional and augmented transaction processing     Traditional and logical data warehouse     Data science exploration/deep learning     Stream/event processing   ...

While traditional data warehouses often took years to build, deploy, and reap benefits from, today's organizations want simple, agile, integrated, cost-effective, and highly automated solutions to support insights. In addition, traditional architectures are failing to meet new business requirements, especially around high-speed data streaming, real-time analytics, large volumes of messy and complex data sets, and self-service. As a result, firms are revisiting their data architectures, looking for ways to modernize to support new requirements. DMA is a modern architecture that minimizes the complexity of messy data and hides heterogeneity by embodying a trusted model and integrated policies and by adapting to changing business requirements. It leverages metadata, in-memory, and distributed data repositories, running on-premises or in the cloud, to deliver scalable and integrated analytics. Adoption of DMA will grow further as enterprise architects look at overcoming data challeng...

Gartner defines a data management solution for analytics (DMSA) as a complete software system that supports and manages data in one or many file management systems, most commonly a database or multiple databases. These management systems include specific optimization strategies designed for supporting analytical processing — including, but not limited to, relational processing, nonrelational processing (such as graph processing), and machine learning or programming languages such as Python or R. Data is not necessarily stored in a relational structure, and can use multiple data models — relational, XML, JavaScript Object Notation (JSON), key-value, graph, geospatial and others. Our definition also states that: A DMSA is a system for storing, accessing, processing and delivering data intended for one or more of the four primary use cases Gartner identifies that support analytics (see Note 1). A DMSA is not a specific class or type of technology; it is a use case. A DMSA ma...

Evaluated Vendors And Inclusion Criteria Forrester included 14 vendors in the assessment: Alibaba, AWS, Exasol, Google, Hortonworks, Huawei, IBM, MarkLogic, Micro Focus, Microsoft, Oracle, Pivotal, Snowflake, and Teradata. Each of these vendors has ( see Figure 1 ): A comprehensive CDW offering. Key components of the CDW include the provisioning, storing, processing, transforming, and accessing of data. The CDW should provide features to secure data, enable elastic scale, provide high availability and disaster recovery options, support loading and unloading of data, and provide various data access tools. A standalone data warehouse service running in the public cloud. Vendors included in this evaluation provide a CDW service that organizations can implement or use independent of analytics, data science, and visualization tools. The service should not be technologically tied to or bundled with any particular application or solution. Data warehouse use cases. The CDW service shoul...

Data science and machine-learning platforms enable organizations to take an end-to-end approach to building and deploying data science models. This Magic Quadrant evaluates 16 vendors to help you identify the right one for your organization's needs. Details >> (Provided by Alteryx here )

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