Your business keeps growing and, with it, the number of data sources that it draws from. The insights your team gets from these data sources continue to be useful, but it is increasingly harder to keep track of APIs, CSVs, and so on. Before too long, it will be more or less impossible to take on new data sources. It will become even more impractical to work with the sources you do have; there is too much manual reconciliation between disparate data sources. Something has got to change.
This section provides a roadmap and technical solution for escaping source hell. Here, we discuss the abstraction of a data lake. The actual idea itself is not too difficult to discuss, but there are a number of value propositions that come with this technique that may be immediately obvious. So, after taking time to investigate what data lakes and what source of engine would be suitable for my needs, we'll go down the list of what makes a data lake such a useful tool.
We discuss in detail what ELT is and why we believe it is a superior method for building data analytics ecosystems. We touch on a number of products out there and their pros and cons.
Lastly, we make time to explain the security and maintenance aspects of data lakes. It's our express goal to explain what a data lake is and what its employment entails.
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