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However, these databases tend to sacrifice support for complex SQL queries at any scale. Instead, these database makers have offloaded complex analytics onto application code and their developers, who have neither the skills nor the time to constantly update queries as data sets evolve.
In small browser text areas you will be able to write Python, SQL or R code and orchestrate theses transformations with drag-n-drop. I'd have love to speak about tools offering to translate human langage to SQL like sequel.ai LinkedIn team decided to migrate to a lambdaarchitecture and got 94% uplift in performance.
Optimized for point lookups, analytics, mutations, etc. Data streamed in is queryable in conjunction with historical data, avoiding need for LambdaArchitecture. Figure 1 below shows a standard architecture for a Real-Time Data Warehouse. Analytics storage and query engine for pre-aggregated event data.
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