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Summary One of the perennial challenges posed by datalakes is how to keep them up to date as new data is collected. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset.
Summary Datalakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert.
Summary Building and maintaining a datalake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that datalakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics.
The fourth difference is the Lakehouse Architecture. Fluss embraces the Lakehouse Architecture. Fluss uses Lakehouse as a tiered storage, and data will be converted and tiered into datalakes periodically; Fluss only retains a small portion of recent data. What is the future roadmap for Fluss?
Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern datamanagement workflows. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley.
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