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Eclipse Dataspace Components on AWS: Cost optimization strategies
architect

Eclipse Dataspace Components on AWS: Cost optimization strategies

When you deploy Eclipse Dataspace Components (EDC) connectors on AWS, one of the first challenges you face is predicting and controlling the cost of the required infrastructure. Without clear benchmarks, it is difficult to make informed decisions about workload sizing, environment configuration, and long-term investment. Part 1 of this 3-part blog series covered the fundamentals

AWS Architecture Blog ·
Eclipse Dataspace Components on AWS: Architecture patterns in production
architect

Eclipse Dataspace Components on AWS: Architecture patterns in production

Running Eclipse Dataspace Components (EDC) connectors in production on AWS requires deliberate architecture decisions around isolation, managed services, and security layering. In Part 1 of this series, we covered the fundamentals of data space architectures and EDC per the International Data Space Association’s (IDSA) standards. If you are new to EDC, we recommend starting there.

AWS Architecture Blog ·
Eclipse Dataspace Components on AWS: Data sharing fundamentals
architect

Eclipse Dataspace Components on AWS: Data sharing fundamentals

This three-part blog series offers a comprehensive guide to implementing Eclipse Dataspace Components (EDC) on AWS. The first installment lays the theoretical groundwork, covering IDSA standards, the Dataspace Protocol (DSP), and the essential architecture of EDC. The second part focuses on practical deployment patterns suitable for production, utilizing AWS services such as Amazon Elastic Container Service (ECS) and Amazon Aurora. The series ultimately aims to facilitate successful EDC implementation on AWS.

AWS Architecture Blog ·
13 hands-on demos to build on Gemini Enterprise Agent Platform
architect

13 hands-on demos to build on Gemini Enterprise Agent Platform

The Gemini Enterprise Agent Platform now offers 13 demos designed to showcase its capabilities for building, scaling, governing, and optimizing AI agents. Users can use the Agents CLI with various coding agents to create intelligent agents efficiently. Demos include building basic agents, event-driven workflows, and securing agent lifecycles, among others. Detailed tutorials guide users through concepts from initial setup to deployment and optimization, emphasizing practical applications and the ease of integration in enterprise environments. Start building agents using this versatile platform today.

Cloud Blog ·
Level Up Your Column-level Security: Using IAM Data Governance Tags in BigQuery
architect

Level Up Your Column-level Security: Using IAM Data Governance Tags in BigQuery

BigQuery introduces data governance tags, providing an enhanced method for securing sensitive information in response to evolving data complexities. These tags enable users to create a hierarchical tag tree for column-level security, allowing for global application and automatic disaster recovery across regions. The implementation involves three steps: creating the tag key and values, attaching tags to columns via JSON schema, and defining access policies. This system offers flexibility and enhances data protection, positioning it as a significant upgrade from previous policy tags. Future updates will further improve these capabilities.

Cloud Blog ·
Interview: Dan Cherowbrier, CTO, Formula E
architect

Interview: Dan Cherowbrier, CTO, Formula E

For the tech chief at the electric vehicle racing organisation, innovation extends from everything digital to all the technology elements that make the growing motorsport operation a success

ComputerWeekly.com ·
Guide to AI Tokenomics: Eleven Principles for Token Efficient Software Engineering
architect

Guide to AI Tokenomics: Eleven Principles for Token Efficient Software Engineering

Optimizing token consumption is essential for enhancing AI coding assistants’ speed and accuracy. Users should adopt structured habits to ensure an efficient feedback loop. Key strategies include starting with basic models, utilizing reusable skills, automating tasks with scripts, delegating output-heavy jobs to sub-agents, and being specific with context. Additionally, users should shift testing early, update rules as needed, avoid uncontrolled loops, and initiate new sessions for different topics. Effective token management balances user direction and automation, ultimately improving productivity while managing costs.

Cloud Blog ·