Sharding, Replication, Partitioning, Horizontal Scaling, Vertical Scaling, High Availability, Failover, Disaster Recovery, Hadoop, HDFS, Spark SQL, Hive, Pig, Presto, Flink, Kafka, Zookeeper, MapReduce, Columnstore, Rowstore

IBM cuts thousands of jobs as it pushes deeper into AI and cloud computing
techspot.comยท7h
๐Ÿ’พDatabase
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The psychological cost of having an RSS feed
refp.seยท4hยท
Discuss: Hacker News
๐Ÿ“ˆTime Series
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Upgrading the homelab
preview.redd.itยท14hยท
Discuss: r/homelab
๐ŸŒDistributed Systems
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PostgreSQL 18 โ€“ Virtual Generated Columns
hashrocket.comยท17hยท
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๐Ÿ’พDatabase
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Google wants to build solar-powered data centers โ€” in space
semafor.comยท1d
๐ŸŒDistributed Systems
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Lazy loading isn't the magic pill to fix AI Inference
tensorfuse-docs.mintlify.devยท1dยท
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๐ŸŒDistributed Systems
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Thoughts on "Static Retrival Revisited"
curiouscoding.nlยท1d
โšกQuery Optimization
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CloudEvents as a Data Product
codesimple.blogยท2d
๐Ÿ”Query Languages & APIs
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Trusted enterprise AI at scale depends on robust cybersecurity
nordot.appยท48m
๐ŸŒDistributed Systems
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What Is Serverless? A Beginnerโ€™s Guide to AWS Lambda & Event-Driven Architectures
dev.toยท1dยท
Discuss: DEV
๐ŸŒDistributed Systems
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flowengineR: A Modular and Extensible Framework for Fair and Reproducible Workflow Design in R
arxiv.orgยท1d
๐Ÿ—๏ธData Engineering
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Enhanced Anomaly Detection in Cryogenic Storage Unit Operations via Multi-Modal Data Fusion and Predictive Analytics
dev.toยท1dยท
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๐Ÿ“ˆTime Series
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Indexing sparse vectors with Turso
turso.techยท22h
๐ŸงญVector Databases
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3 reasons you should host your own Matrix server
xda-developers.comยท23h
๐ŸŒDistributed Systems
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TCP vs UDP: Choosing the Right Protocol for Your Node.js Application
dev.toยท1dยท
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๐ŸŒDistributed Systems
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System Design Explained Like a Human โ€” 25 Core Concepts with Real Examples and Tools Part -1
dev.toยท4dยท
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๐ŸŒDistributed Systems
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Grafana Mimir 3.0 release: performance improvements, a new query engine, and more
grafana.comยท2d
๐Ÿ”ชDatabase Sharding
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The data lakehouse evolution
dev.toยท6dยท
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๐Ÿ—๏ธData Engineering
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Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis, Solution, and Interpretation
arxiv.orgยท17h
๐Ÿ”€Transformers
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