Too many companies are using data coming from various sesors but have no idea how to manage it on a centralized platform to gather useful insights in everyday business.
With the increasing use of IoT sensor data, an ELT pipeline using Apache Spark Streaming and Databricks offers a scalable approach for handling sensor data. Databricks’ distributed computing and integration with Kafka and Delta Lake enable seamless ingestion, storage, and transformation. Delta Lake ensures data integrity with ACID transactions and schema enforcement. The pipeline processes real-time data, categorizes sensor readings, and prepares it for analytics. Processed data is stored in Delta Lake, making it accessible for BI tools and predictive models. This architecture supports real-time insights, anomaly detection, and predictive maintenance. For detailed understanding of architecture, please refer to our medium blog post (https://medium.com/@hamzamalik_33775/building-an-elt-pipeline-using-spark-streaming-and-databricks-for-iot-sensor-data-230c49014a5b)
