Strategies for Fitting a Data Lake into a Modern Data Architecture

McKnight Consulting Group

Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake a place for unmodeled and vast data will be provisioned widely in 2019. Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.
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Imagine that you are starting a family and you want to stay on top of your finances so you can manage your budget for your growing family. Because you have too many accounts for banking, loans, subscriptions, and bills to keep track of, you decide to sign up for a service that aggregates all your accounts in one place. When you register your account online, you follow the instructions carefully. You create a strong, unique password and set up multifactor authentication. You enter your bank account and loan information, home address, and other personal information. Your spouse also signs up, and you create a joint family account that combines your information.

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