Entity Resolution Service - Bringing Petabytes of Data Online for Instant Access
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2.5B+ ids, 2ms latency, 15K+ TPS and Petabytes of data.These numbers outline the challenges with eBay’s Entity Resolution Service (ERS). ERS provides a temporal map between anyid-anyid. The technology stack of ERS has Hadoop as the batch layer, Couchbase as cache layer, Spring Batch to load data to Couchbase and Rest API at Service layer. In our presentation we will take you through the journey from conceptual to production release. It’s a great story and we would like to share with you!
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We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
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• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
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QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
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- Zugriff auf den Datenordner im Cache des Browsers (unter Verwendung von OPFS)
- Verständnis der Unterschiede zwischen Einzel- und Mehrbenutzerszenarien
- Nutzung der Client Clocking-Funktion
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Big Data Analytics Quick Research Guide by Arthur Morgan
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
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At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
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Join Christoph and Marc as they demonstrate how to simplify the troubleshooting process in HCL Nomad Web, ensuring a smoother and more efficient user experience.
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- Accessing the console
- Locating and interpreting log files
- Accessing the data folder within the browser’s cache (using OPFS)
- Understand the difference between single- and multi-user scenarios
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- 1. Entity Resolution Service Bringing Petabytes of Data Online for Instant Access Gurpreet Singh, eBay Ying Zhang, eBay
- 2. Problem Statement 2 eBay maintains hundreds of millions of accounts* across our properties and partners, that are sometimes unstructured, in different formats, different character sets, and are changing over time. Identifying which accounts belong to the same person enables us to personalize each customer's experience, deliver great customer service, and fight fraud. ERS brings xID along with device information online.
- 4. xID 4 Links User Credentials and transient identities to recognize a customer: –Hundreds of millions of user accounts. –20+ different linking strategies with the flexibility of adding more. –Billions of cookies, browser fingerprints, all mobile identities. –Customer recognition across sessions, sites, browsers, devices.
- 5. xID Modular Solution 5 Sources Edges Graph Table Account Entity 102832 10921 236896 10921 786273 10921 324324 23987 349709 73652 152631 73652 543273 37726
- 6. SOURCES – Overall Data Flow 6
- 9. ERS 9 Enables access to consolidated customer Identity –RESTFul service with ~2ms response time –Optimized access to customer identities –10K+ requests per second – 70 Nodes Couchbase Cluster with 11 TB memory in 3 data centers
- 10. Challenges 10 LARGE DATASET SKILLSET COMPLEX TIMELINE FUTURE GROWTH Raw clickstream + PII ~ in PB 20+ edge types 100+ billion edges
- 11. Challenge 11 LARGE DATASET SKILLSET COMPLEX TIMELINE FUTURE GROWTH Graph Join Process Geo Redundancy Requirement
- 12. Challenge 12 LARGE DATASET SKILLSET COMPLEX TIMELINE FUTURE GROWTH Downstream waiting Infrastructure Deliver it ASAP!
- 13. Challenge 13 LARGE DATASET SKILLSET COMPLEX TIMELINE FUTURE GROWTH NoSQL knowledge Near Real Time Processing
- 14. Challenge 14 LARGE DATASET SKILLSET COMPLEX TIMELINE FUTURE GROWTH More business Usecases Data Volume Data Variability Data Velocity
- 15. Key Take Away’s 1 5 • Optimize CB data load to prevent negative service impact • Reduce JSON size to increase CB Active Resident Ratio • Geo-Redundancy by data replication • Enterprise BigData solutions are complex (Velocity, Volume and Variability)
- 16. THE END