How System Work
Data from various sources, including databases, applications, and IoT devices, is ingested into the Data Lake. The Hadoop Distributed File System (HDFS) serves as the storage layer, providing a distributed and fault-tolerant file system that can handle large-scale data storage. Data is stored in its raw form, eliminating the need for upfront data transformations or predefined schemas, which allows for flexibility and scalability.
Once the data is ingested into the Data Lake, Spark Streaming and Kafka Streams come into play. Spark Streaming enables real-time data processing and analytics by dividing the data into micro-batches and applying transformations, calculations, and machine learning algorithms. It provides near real-time insights and analysis, allowing organizations to make timely decisions based on the most up-to-date information.
Kafka Streams complements Spark Streaming by providing a scalable stream processing framework. It allows for the integration of real-time data streams with batch data, enabling continuous processing and analysis. Kafka Streams provides fault tolerance and scalability, ensuring that organizations can handle high-throughput data streams reliably.