Working on the first Data-Lake-as-a-Service in the world gave us the perspective of how Data Lakes enable companies to understand correlations between existing and new external data in ways traditional BI tools cannot. The rise of Docker & containerized microservices has paved the way for significantly faster deployment, less overhead, easier migration & faster performance for big data apps.
A Data Lake is more of a concept than a technology. It starts from the idea that having data of various types and formats in one place (especially unstructured
data from an entire organization) allows correlations across data sources. Those correlations generate insight, which might not be visible from a single source. At
its core, it is a storage strategy designed to support the new big data oriented analytics.
A Data Lake promotes the accumulation of data in its original format, from many different sources. Correlations across various data sources have been shown to
generate far more insight than a single data source can do on its own. Used wisely, it can help businesses make more informed decisions and also help build smarter automated processes that increase efficiency or improve customer experience.
In this talk we’ll give answers to the following questions:
1. Why should you start looking into a Data Lake?
2. Is it hard to build a Data Lake?
3. What are the main features that a Data Lake should bring in?
4. What’s the role of the microservices in the big data world?
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