Episode 40 of Cloud Computing with Fexingo explores a quiet but critical battleground: data freshness. Lucas and Luna break down why stale data is costing companies millions in misinformed decisions, and how AWS, Azure, and GCP are now competing on real-time data infrastructure. The hosts examine a specific case — a mid-size logistics firm that cut forecasting errors by 40 percent after switching from batch to streaming analytics — and discuss the trade-offs: higher compute costs vs. better decisions. They also touch on the rise of 'freshness SLAs' in cloud contracts, the role of Apache Kafka and Amazon Kinesis, and why traditional data warehouses are losing ground to lakehouses with streaming ingestion. If your team still runs nightly ETL batches, this episode might change your architecture. No jargon, just a clear-eyed look at why milliseconds matter in modern infrastructure.
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