Storing and processing vast amounts of data is a big challenge. Whether you are tracking user activity, monitoring system health across thousands of devices, or analyzing high-frequency financial transactions, you are dealing with millions of records, all of which must be processed efficiently. A traditional row-based storage system could buckle under the strain and incur heavy costs.
Bitmaps offer a powerful solution to this challenge by allowing you to compress data into minimal memory and enabling fast and efficient analysis.
In this blog post, we’ll explore the advantages of using bitmap operations for large-scale data analytics and demonstrate how they can significantly reduce memory usage and compute time. We’ll also discuss how this methodology can be integrated with Databricks and show how complex queries can be easily handled with minimal overhead. By the end, you'll see how bitmaps can be a game-changer in various scenarios that require large-scale data processing.
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