Batch Processing vs Real-Time Quiz & Flashcards

Questions and Answers

Loading data as soon as it is generated without grouping

Real-time Data Processing (Streaming) ingestion

Improve Efficiency

Handles large-scale data and complex models efficiently Signup and view all the answers

Data parallelism, model parallelism, hybrid approaches Signup and view all the answers

When it is generated and recognized by the ingestion layer Signup and view all the answers

Distributed Machine Learning and AI Signup and view all the answers

To handle data growth, complex models, and real-time requirements Signup and view all the answers

Loading data as soon as it is generated Signup and view all the answers

To provide extremely current data for real-time insights Signup and view all the answers

Study Notes

Involves collecting and processing data in large chunks or batches at scheduled intervals.
Best suited for handling significant volumes of data collectively during off-peak times.
Longer processing times compared to real-time processing; ideal where immediacy is not critical.
Commonly used in online analytics, ETL processes, and data warehousing.
Job execution is sequential; tasks are completed one after another.
Benefits include efficiency in dataset processing, scalability, and better resource optimization.
Example technologies: Apache Hadoop, Apache Spark.

Monthly financial reporting is typical; financial institutions compile large volumes of transactional data for accurate reporting and statement generation.

Processes continuous streams of data in real-time, enabling immediate analysis and action.
Contrasts with batch processing by allowing data to be analyzed as it flows in, instead of at scheduled intervals.
Characterized by real-time analysis and the ability to accommodate ongoing data generation.
Provides low latency with minimal delays, ensuring quick decision-making.
Critical for applications requiring instant insights and timely responses.

Processing Approach: Batch processing accumulates data over time; real-time processing deals with data immediately as generated.
Data Volume: Batch processing is ideal for large datasets; real-time processing is suited for streaming data.
Latency: Batch processing has longer latencies; real-time processing provides almost instant results.
Use Case Applications: Batch processing is used for non-time-sensitive tasks, while real-time processing is critical for applications needing rapid response to data input.