Читать книгу Data Lakes For Dummies - Alan R. Simon - Страница 23

Take a look around your organization today. Chances are, you have dozens or even hundreds of different places to go for reports and analytics. At one time, your company probably had the idea of building an enterprise data warehouse that would provide data for almost all the analytical needs across the entire company. Alas, for many reasons, you instead wound up with numerous data marts and other environments, very few of which work together. Even enterprise data warehouses are often accompanied by an entire portfolio of data marts in the typical organization.

Great news! The data lake will finally be that one-stop shopping place for the data to meet almost all the analytical needs across your entire enterprise.

Enterprise-scale data warehousing fell short for many different reasons, including the underlying technology platforms. Data lakes overcome those shortfalls and provide the foundation for an entirely new generation of integrated, enterprise-wide analytics.

Even with a data lake, you’ll almost certainly still have other data environments outside the data lake that support analytics. Your data lake objective should be to satisfy almost all your organization’s analytical needs and be the go-to place for data. If a few other environments pop up here and there, that’s okay. Just be careful about the overall proliferation of systems outside your data lake; otherwise, you’ll wind up right back in the same highly fragmented data mess that you have today before beginning work on your data lake.