In streaming processing, input data is always from unbounded data sources, like Kafka. However, for batch processing, input data comes from bounded data sources, like HDFS .

Batch vs. Streaming Ingestion: Handling both batch data (periodically collected and sent) and streaming data (real-time, continuous flow). Data Processing . Once ingested, the raw data needs to be ...

MENLO PARK, Calif., April 08, 2025--The new DeltaStream Fusion Unified Analytics Platform empowers organizations to process both real-time and historical data in one place seamlessly.

Organizations must address fundamentals, like governance and visibility, to ensure long-term success with AI agents ...

1. Treating Data Streaming Like Accelerated Batch Processing. One costly mistake in adopting data streaming is treating it like accelerated batch processing.

📈 A scalable, production-ready data pipeline for real-time streaming & batch processing, integrating Kafka, Spark, Airflow, AWS, Kubernetes, and MLflow. Supports end-to-end data ingestion, ...

Here's how data streaming can reduce AI's environmental impact while making it more powerful, responsive, and efficient.

Santosh has Developed event-driven data pipelines using technologies like Azure Event Hub, Databricks, and Synapse Analytics ...

Confluent's announcement represents a significant step in its strategy to position data streaming as the foundation for enterprise AI development. By unifying batch and streaming processing, the ...

Real-time processing complicates tasks such as data loading, transformation, backfilling and schema changes. “All the data management problems we have already faced in batch, we are now solving ...