Articles
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1 month ago |
onlinelibrary.wiley.com | Lulu Cheng |Houhong Chen |Jiajie Deng |Qiyu Cai
Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Dec 7, 2024 |
dev.to | Lulu Cheng
Many data platforms today are built bottom-up, starting with collecting data that "might be useful later" and gradually patching together solutions as needs arise. This approach inevitably leads to fragmented implementation, mounting cost and technical debt. Data system design requires specialized expertise across data modeling, distributed systems, security, and compliance.
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Nov 13, 2024 |
dev.to | Lulu Cheng
Today, we have access to highly scalable data tools handling massive volumes that would have been unthinkable just a few years ago. LLMs process billions of parameters, streaming platforms handle millions of events per second, and data warehouses that can easily scale to petabytes. Ironically, while individual tools are more scalable than ever, organizations find themselves struggling with increasingly unscalable data platforms.
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Nov 8, 2024 |
dzone.com | Lulu Cheng
Encryption is one of the most effective data security strategies, alongside access control, software updates, and network segmentation. However, adding encryption to an existing tech stack can be challenging. Data pipelines aren’t typically designed to handle encryption and decryption, leading to additional migration work, operational complexity, and higher compute costs. Data encryption presents its own challenges in modern times.
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Oct 27, 2024 |
dev.to | Lulu Cheng
Infrastructure and internal platform adoption don’t typically happen organically. In the best-case scenario, adoption happens through partnerships with product teams to enable feature development or provisioning automation. However, in many cases, particularly in the worst-case scenario, platforms are forcibly “adopted” by product teams through migrations. To set the stage, migrations are bad.
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