Big data technologies’ quick development has brought attention to the necessity of a smooth transition between real-time data analytics and batch processing systems. Since HDFS (Hadoop Distributed File System) based data lakes provide scalable and affordable storage for vast amounts of heterogeneous data, they have emerged as a key component of present-day data architectures. However, when interacting with dynamic, real-time data operations, HDFS’s static nature frequently poses difficulties. In order to enable real-time data input, transformation, and analysis within HDFS-based data lakes, this article examines how streaming databases can help close the gap.

Streaming databases can depend on HDFS-based data lakes to efficiently handle, process, and store large volumes of streaming data. This dependency arises because HDFS-based data lakes are designed to store and manage big data in a distributed manner, while streaming databases specialize in real-time processing and querying. 

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