Data lakes are data storage systems that allow data to be stored, managed and accessed in a way that is cost-effective and scalable. They can provide a significant competitive advantage for any organization by enabling data-driven decision-making, but they also come with challenges in architecture design. In this blog post, we will explore the different components of data lakes, including the data lake architecture. Before getting to learn about data lake architectural component, lets quickly recall what is a data lake.
A data lake is a data storage system that allows data to be stored, managed, and accessed in a way that is cost-effective and scalable. Data lakes are also more flexible than data warehouses since there isn’t a specific structure that data must follow within the data lake; users can upload data into any location they choose and tag it with metadata.
Data lakes allow data scientists, analysts, or anyone else who needs to search for data within the data lake through searching metadata instead of going directly into the relational data warehouse. This speeds up the time needed to find information because users don’t need to know how data is organized within the data warehouse.
The following represents different architectural components data lake:
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