The Growing Importance of Graph Databases for AI
By: AWS View more from AWS >>
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Graph Database Buyers' Guide
By: Aerospike
Type: Buyer's Guide
With the growth of cloud computing, distributed databases, and now AI/ML, the role of graph databases has evolved to include more operational workloads. These workloads introduce greater requirements on graph databases, including scaling to terabytes and beyond, low-latency transactions, high data throughput, enterprise-grade availability, and disaster recovery.
This guide will help guide you to make an informed decision about which approach to graph databases best suits your requirements. Download this guide to learn:
- The key criteria to consider when selecting a graph database
- How analytical vs. transactional use cases make a difference
- Some of the vendors in this space that you should consider
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TechTarget’s Enterprise Strategy Group (ESG): SBOM security
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Type: ESG White Paper
Software bills of material (SBOMs) are foundational to risk management and cybersecurity.
In this white paper, expert-level analysts from TechTarget’s Enterprise Strategy Group (ESG) explore the methodologies Amazon Neptune and Intuit use to secure a software bill of materials (SBOM).
Download your copy of the ESG white paper now to unlock insight into SBOM security.
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Databases play a crucial role in the creation and deployment of AI applications. With AI development becoming a top priority for most software organizations, understanding AI databases is no longer optional for those striving to maintain a competitive edge.
This guide explores how to match the right database solution to your specific AI needs. You will learn:
- AI data management fundamentals
- A framework for comparing AI database alternatives
- The pros and cons of popular AI database alternatives
- Practical strategies to succeed with AI databases
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