CIO
DECEMBER 20, 2024
Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Ensure security and access controls.
CIO
NOVEMBER 19, 2024
The requirements for the system stated that we need to create a test data set that introduces different types of analytic and numerical errors. This data would be utilized for different types of application testing.
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CIO
OCTOBER 30, 2024
To overcome those challenges and successfully scale AI enterprise-wide, organizations must create a modern data architecture leveraging a mix of technologies, capabilities, and approaches including data lakehouses, data fabric, and data mesh. Another challenge here stems from the existing architecture within these organizations.
Tenable
DECEMBER 5, 2024
4, NIST released the draft Guidance for Implementing Zero Trust Architecture for public comment. Tenable has been proud to work alongside the NIST National Cybersecurity Center of Excellence (NCCoE) to launch the Zero Trust Architecture Demonstration Project.
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Embedding analytics in your application doesn’t have to be a one-step undertaking. Read more about how to simplify the deployment and scalability of your embedded analytics, along with important considerations for your: Environment Architecture: An embedded analytics architecture is very similar to a typical web architecture.
TechCrunch
MARCH 9, 2022
As organizations continue to build out their digital architecture, a new category of enterprise software has emerged to help them manage that process. The longer-term goal is to build more predictive analytics and modeling tools that leverage the “digital twin” that Ardoq builds of a network.
Cloudera
DECEMBER 2, 2024
Cloudera is committed to providing the most optimal architecture for data processing, advanced analytics, and AI while advancing our customers’ cloud journeys. Together, Cloudera and AWS empower businesses to optimize performance for data processing, analytics, and AI while minimizing their resource consumption and carbon footprint.
Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri
Data teams in large enterprise organizations are facing greater demand for data to satisfy a wide range of analytic use cases. Yet they are continually challenged with providing access to all of their data across business units, regions, and cloud environments.
Speaker: Veena Vasudevan and Jason Hughes
With data stored in vendor-agnostic files and table formats like Apache Iceberg, the open lakehouse is the best architecture to enable data democratization. By moving analytic workloads to the data lakehouse you can save money, make more of your data accessible to consumers faster, and provide users a better experience.
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Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data. The primary architectural principles of a true cloud data lake, including a loosely coupled architecture and open file formats and table structures.
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Every data-driven project calls for a review of your data architecture—and that includes embedded analytics. Before you add new dashboards and reports to your application, you need to evaluate your data architecture with analytics in mind. 9 questions to ask yourself when planning your ideal architecture.
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Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years. Download this TDWI Checklist report to understand: How your organization can make this transition to a modernized data architecture. The decision making around this transition.
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Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate database instances while meeting data privacy needs. When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital.
Speaker: Ahmad Jubran, Cloud Product Innovation Consultant
Many do this by simply replicating their current architectures in the cloud. Those previous architectures, which were optimized for transactional systems, aren't well-suited for the new age of AI. In this webinar, you will learn how to: Take advantage of serverless application architecture. And much more!
Speaker: speakers from Verizon, Snowflake, Affinity Federal Credit Union, EverQuote, and AtScale
Driving a self-service analytics culture with a semantic layer. Using predictive/prescriptive analytics, given the available data. Avoiding common analytics infrastructure and data architecture challenges. The impact that data literacy programs and using a semantic layer can deliver.
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