Use Cases of Distributed Cache
a. Machine Learning Algorithms
Machine learning algorithms that require access to large datasets or libraries can leverage Distributed Cache to speed up iterative data processing, reducing the overall time for model training.
b. Data Transformation Jobs
In transformation jobs where multiple tasks need to reference the same lookup tables or configuration settings, having these files in the cache can significantly speed up the process.
c. Sessionization Analysis in Web Logs
For analyses that involve grouping page hits into sessions, Distributed Cache can store user session data locally, helping to process large logs more efficiently by reducing the need to query a central database for each hit.
What is the importance of Distributed Cache in Apache Hadoop?
In the world of big data, Apache Hadoop has emerged as a cornerstone technology, providing robust frameworks for the storage and processing of vast amounts of data. Among its many features, the Distributed Cache is a critical yet often underrated component. This article delves into the essence of Distributed Cache, its operational mechanisms, key benefits, and practical applications within the Hadoop ecosystem.