![]() ![]() Lakes employ a "schema-on-read" approach. It is designed for batch processing and large-scale data storage of raw data from various sources, like sensors, logs, social media, streaming data, and more. It allows organizations to store and analyze diverse data types in their native formats. Understanding Data LakesĪ data lake is a centralized repository that stores vast amounts of structured, semi-structured, and unstructured data. We have also listed the steps for transitioning from a lake to a lakehouse effectively. In this article, we will explain data lakes and lakehouses in detail, list their key features, use cases, and compare the main differences between the two data storage systems. The rise of cloud computing has further fueled the adoption of lakehouse architectures, with cloud providers offering managed platforms with lakehouse capabilities that drive big data analysis and business intelligence efforts. It brings the best of both worlds together to provide a unified platform for storing, processing, and analyzing data. A data lakehouse is a storage technology that combines the scalability and flexibility of a data lake with the structure and reliability of a data warehouse. ⭐ TL DR As organizations strive to extract meaningful insights from their ever-increasing volumes of data, the lakehouse architecture is a compelling option for data storage and analysis. ![]()
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