How Daikin Applied Americas builds consistent data pipelines at scale with Genie Code
Agentic data engineering is changing how pipelines are builtDaikin Applied Americas...
The rapid advancement in AI and large language models (LLMs) is making agentic systems viable for automating complex tasks like data engineering, pushing demand for more efficient data pipeline solutions.
Agentic data engineering streamlines data management, reduces human error, and accelerates the transition from raw data to actionable insights, crucial for businesses seeking competitive advantages in an AI-driven economy.
The traditional, manual approaches to data pipeline development are being augmented and eventually replaced by autonomous agentic systems, shifting focus from manual coding to intelligent orchestration.
- · Databricks
- · Organizations with complex data needs
- · AI/ML development teams
- · Data engineering platform providers
- · Manual data engineering service providers
- · Companies slow to adopt automation
- · Legacy data integration tools
Increased efficiency and reduced cost in data pipeline management for early adopters.
A shortage of skilled agentic system developers emerges as demand outstrips supply.
Data infrastructure becomes a 'black box' for many organizations, raising new concerns about observability and explainability.
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Read at Databricks Blog