The biggest challenge in business today are independent stores of data. Natural and data lakes both provide a centralization of resources, but any information you want requires an analysis of existing data that is scattered across many systems.
Why would I need a data lake?
A bank has multiple segments including portfolios and individual offerings while customers have personal, mortgage and auto-insurance accounts as well. Imagine scenarios where data is stored in NoSQL databases and data-marts, resulting in most of it becoming siloed.
If you need all the relevant information for a customer, you have to work across silos with independent processes, reconcile them, apply ETL logic and translate them into meaningful information for the bank teller and customer.
Data lakes could be the next step in data-harmonization but converting to them, still remains an issue. Often asked by stakeholders, investors, and executives, data lakes will be useful for the following reasons:
Could lakes become toxic? Think about the discovery process and how if not managed, could become a hard process to find what you need. You could avoid this by maintaining metadata and by implementing best practices that we will discuss in more detail in ‘Data Lakes – Keep It Clean.’
Here is a summary of what has been discussed on data lakes by Intricity101
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