Latest News- Agentic AI For Data Engineering Market: Solutions is expected to lead the Component segment during 2025-2029

The Agentic AI For Data Engineering Market is being driven by Increasing demand for real-time data processing and analytics

The Agentic AI For Data Engineering Market is expected to grow at a CAGR of 39.2% during 2024 and 2029. During this period, the market is also expected to show a growth of USD 3768.7 million. The agentic AI for data engineering market is undergoing a transformative shift towards autonomous data pipelines, driven by the intricacy and expansiveness of modern enterprise data. This development signifies a substantial progression from conventional extract, transform, load (ETL) techniques, which frequently necessitated manual intervention and configuration. Agentic AI systems now possess the ability to autonomously monitor data streams, recognize abnormalities, and dynamically modify pipeline configurations to ensure peak performance and data accuracy. This automation encompasses self-optimizing ETL processes, which learn from historical operational data and adapt their transformation rules and loading strategies in real time. The objective is to establish a data infrastructure that necessitates minimal human intervention, thereby decreasing operational expenses and expediting data accessibility for analytics and decision-making. 

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Which Factors Are Causing a Surge in Market Growth?

The market is segmented based on

• Component
  • Solutions
  • Services
• Deployment
  • Cloud
  • On-premises
• End-user
  • BFSI
  • Healthcare
  • Others
• Geography
  • North America
    • Canada
    • US
  • Europe
    • Germany
    • UK
    • France
  • APAC
    • China
    • India
    • Japan
  • South America
    • Brazil
  • Middle East and Africa

According to Technavio, There are several factors that are causing the market to flourish during the forecast period, which are as follows: 

• Increasing demand for real-time data processing and analytics
• Automation and optimization of data pipeline efficiency
• Enhanced data governance, quality, and security

However, the market also witnesses some limitations, which are as follows:

• Data governance and compliance complexities
• Interoperability and integration with legacy systems
• Talent scarcity and skill gaps

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Market Scope in Agentic AI For Data Engineering Market Research Report

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Research Analysis Overview

In the realm of Agentic AI for Data Engineering, key elements include managing data engineering workflows, ensuring data versioning, implementing CI/CD pipelines, and orchestrating with Kubernetes. Monitoring dashboards, performance tuning, and scalability solutions are crucial for optimizing costs. Automation frameworks, data cataloging, and data lineage promote transparency. Model explainability, statistical modeling, anomaly detection, and data profiling enhance data discovery. Data preparation, transformation, enrichment, validation, and cleansing ensure data quality. Unit, integration, system testing, user acceptance, code review, deployment automation, and infrastructure as code further strengthen the data engineering process.

Market Research Overview

The Agentic AI For Data Engineering Market is a significant segment of the global Technology Hardware, Storage, and Peripherals Market, encompassing data pipeline automation, machine learning models, ETL process optimization, and data integration tools. According to Technavio, this market is driven by the increasing volume, velocity, veracity, and variety (4Vs) of data. The global Technology Hardware, Storage, and Peripherals Market, which includes manufacturers of cellular phones, personal computers, servers, electronic components, and peripherals, as well as data storage components and other related equipment, is projected to grow due to the escalating demand for data storage solutions, primarily driven by the expansion of data centers.. Industries are leveraging the products belonging to the market for customer engagement, transactional notifications, and promotional offers.

Technavio Research
Jesse Maida
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