Webinar: 6 Use Cases You Didn't Think Possible on the Data Lake (Sept. 24th)
Thursday, September 24th, 2026: 5:30 PM to 6:30 PM
Your lakehouse already holds the data. Iceberg and Delta solved openness, durability, and cost. So why does every workload with a real performance SLA still live somewhere else?
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Your lakehouse already holds the data. Iceberg and Delta solved openness, durability, and cost. So why does every workload with a real performance SLA still live somewhere else?
The moment a query is tied to a customer experience, a revenue event, or an on-call engineer at 2am, best-effort latency stops being acceptable.
Traditional lakehouse query engines scan too much data to hold sub-second responses, so teams do the only thing available to them: they copy the data out. Into Elasticsearch for log search. Into a time-series store for metrics. Into a key-value store or a serving warehouse for the customer-facing dashboard. Into a vector database for similarity search.
Each copy adds pipeline complexity, a second infrastructure bill, and one more reason the lakehouse is not actually the source of truth.
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