Modern data architecture is under pressure to deliver greater ROI. However, as data use cases (such as generative AI, machine learning, and geospatial insights) have grown in complexity, the data stack itself has become more complex and disjointed, driving up costs and slowing processes. This results in a gap between the data infrastructure you’ve invested in and the ROI you need from today’s analytics use cases.
Some companies are implementing a unified data platform architecture to address this gap. This architecture provides an integrated set of capabilities to manage data as well as analytics development and deployment. In its simplest form, the unified platform is capable of ingesting diverse data from multiple sources; it incorporates pipeline services, a data storage and/or virtual or semantic layer (the data environment), an analytics layer, and a consumption layer. It is scalable to support massive amounts of data and is often hybrid or cloud-based. Often there are many more services as part of the unified platform.
View this webinar to hear from TDWI and Alteryx as they discuss the following topics:
Data and analytics trends requiring a new approach to data management
What the unified data architecture is all about
Key requirements for the unified architecture
Examples of analytics use cases that require a unified architecture
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