Data Warehouse vs. Data Lake
Data Warehouse: Data warehouses are designed for processing and analyzing structured data. They follow a schema-on-write approach, meaning data must be structured before being ingested. Data warehouses are optimized for complex queries and reporting, making them suitable for business intelligence and decision support.
Data Lake: Data lakes, on the other hand, support structured and unstructured data in its raw form. They follow a schema-on-read approach, allowing users to apply the schema at the time of analysis. Data lakes are more suitable for handling large volumes of diverse data types and are well-suited for exploratory and advanced analytics.
What is Data Lake ?
In the fast-paced world of data science, managing and harnessing vast amounts of raw data is crucial for deriving meaningful insights. One technology that has revolutionized this process is the concept of Data Lakes. A Data Lake serves as a centralized repository that can store massive volumes of raw data until it is needed for analysis.
In this article, Let’s delve into the key points that shed light on how Data Lakes efficiently manage, and store raw data for later use, Data Lake architecture, and the Challenges of Data Lakes.
Table of Content
- What is a Data Lake?
- Different data processing tools
- Data Lake Architecture
- Data Warehouse vs. Data Lake
- Challenges of Data Lakes
- Values of Data Lakes
- Conclusion