The capability to process data instantly as it is generated, crucial for applications like IoT.

The ability of a system to continue operating despite failures or errors, ensuring reliability in data pipelines.

A model where components located on networked computers communicate and coordinate their actions to achieve a common goal.

An open-source platform to programmatically author, schedule, and monitor workflows, essential for data orchestration.

A network of physical devices connected to the internet, enabling data collection and exchange.

A graph structure used in Airflow to represent tasks and their dependencies, ensuring orderly execution.

The accuracy and reliability of data across multiple nodes in a distributed system, critical for integrity.

The capability of a system to handle a growing amount of work, crucial for real-time data processing.

The process of obtaining and importing data for immediate use in a database or data pipeline.

Distributing workloads across multiple computing resources to optimize resource use and avoid overload.

The efficient allocation and utilization of resources in data processing to maximize performance.

Methods used to improve the efficiency and performance of data pipelines and processing systems.

A testing methodology that validates the complete flow of an application from start to finish.

Real-world examples analyzed to understand best practices and lessons learned in fault tolerance.

Standardized solutions to common problems in software architecture, guiding system design.

The process of identifying and resolving issues in Airflow workflows to ensure correct execution.

Specific areas in a system where failures are likely to occur, necessitating mitigation strategies.

Thorough evaluation of a system to ensure functionality, performance, and reliability.

The continuous flow of data generated by IoT devices, requiring real-time processing capabilities.

Designing systems to withstand and recover from failures, ensuring uninterrupted service.

Emerging technologies that enhance data processing capabilities, particularly in real-time environments.

The process of combining data from various origins into a unified system for analysis.

Issues faced in processing continuous data flows, including latency and data loss.

Established guidelines that enhance the reliability and performance of distributed architectures.

Practical use cases that demonstrate the effectiveness of theoretical concepts in industry settings.