A naukri.com initiative
Dzone
3w
385
Image Credit: Dzone
Scalable, Resilient Data Orchestration: The Power of Intelligent Systems
- Data orchestration is crucial for AI/ML models as it ensures a continuous flow of quality data from multiple sources.
- Orchestrators manage tasks in Directed Acyclic Graphs (DAGs), connecting subsystems through triggers and events.
- Data orchestration differs from data pipelines as it spans multiple components and utilizes execution flows based on state machines.
- Key traits of a good data orchestration design include responsiveness to triggers, modularity, scalability, and serial/parallel task execution.
- Retry mechanisms and reliable restart capabilities are essential features that prevent unnecessary processing churn and ensure consistent data processing.
- Transactional execution and auditability are vital for managing data orchestration in various use cases, such as AI/ML models.
- Trending practices include leveraging object storage over databases, experimenting with file formats like Parquet, and prioritizing data over metadata in streams for better performance.
- Data orchestration systems are vital for enabling private AI/ML systems and must be scalable, resilient, and efficient in storing and retrieving data.
- Engineers should focus on system capabilities rather than tool popularity, understanding the principles that make a data orchestrator effective over time.
- Data orchestration plays a crucial role in fueling innovation and driving progress by ensuring a steady flow of quality data for intelligent systems.
Read Full Article
23 Likes
For uninterrupted reading, download the app