Future-proofing your data: Get Gen AI-ready with managed data engineering services

In the fast-paced world of big data and AI, organizations are racing to harness the power of generative AI. But there’s a crucial element that’s often overlooked: the robust data foundation that Gen AI needs to thrive. This is where data engineering as a managed service comes into play, laying the groundwork for innovation while handling the complexities of data infrastructure. By freeing organizations to focus on groundbreaking advancements rather than getting bogged down in data management complexities, this approach is making businesses Gen AI-ready. This blog post will explore this concept, delving into how data engineering as a managed service is shaping the future of AI integration in business operations.

Current data engineering landscape

Data has become the lifeblood of business operations. Yet, the data engineering landscape is growing increasingly complex. Companies are grappling with an unprecedented explosion of data from diverse sources, including IoT devices, social media and customer interactions. The speed of this evolution will even increase in the coming years, with an expected rise from around 149 zettabytes in 2024 to more than 394 zettabytes in 2028.

As data volumes grow and business needs evolve, organizations face increasing challenges in managing their data operations effectively. From dealing with complex data ecosystems to ensuring regulatory compliance, the hurdles are numerous and often overwhelming.

New approaches for data engineering: From DataOps to Gen AI

Data engineering is evolving rapidly with new and innovative methods to respond to more complex data:

Data mesh architecture is changing how organizations ingest, use and manipulate data from a centralized architecture to a decentralized one.
Real-time data processing, analytics and the explosive growth of generative AI offers new business opportunities—but also add layers of complexity to data operations.
Emerging DataOps practices are connecting data scientists and engineers, enabling faster deliverables and fewer conflicts amongst themselves.
AI-aided automatic data pipelines are transforming complex patterns and quality. AI tools would replace the constantly needed manual tuning, maintenance and error handling and AI-powered pipeline automation has been identified as one of the most significant trends for 2025.
According to ISG, more than half of enterprises will embrace DataOps by 2026. Orchestrating data integration and processing with DataOps will improve data quality and validity.
Healthy data pipelines are a prerequisite to leveraging generative AI properly. As ISG states: “Healthy data pipelines are necessary to ensure data is ingested, processed and loaded in the required sequence to generate business insights and AI”.

Data engineering as a managed service is shaping the future of AI integration in business operations.

Getting AI-ready with managed data engineering

Organizations leveraging data engineering as a managed service ensure their data foundation and infrastructure are primed for the transformative power of Gen AI, which is now a critical strategic imperative.

We recommend considering a set of requirements:

1. Ensure your data pipelines support Gen AI's unique needs
Gen AI models require large-scale, high-quality, and diverse datasets.
Ensure that your data engineering solution can ingest, process, and manage petabyte-scale structured and unstructured data from varied sources efficiently and reliably.
Determine if data engineering as a managed service offers synthetic data generation pipelines, invaluable for augmenting Gen AI training and overcoming data scarcity or privacy concerns.

2. Design the architecture for extreme scalability and adaptability
Look for solutions offering composable infrastructure to dynamically allocate and configure infrastructure specifically for Gen AI workload needs.

3. Integrate Gen AI-powered tools
Employing Gen AI for data engineering environment management and optimization is a powerful strategy. Explore if Gen AI recommends optimal resource configurations for cost and performance based on analyzing Gen AI workload patterns.
Investigate if your data engineering managed service leverages Gen AI to assist with writing and optimizing data pipeline code for complex Gen AI needs, automating Infrastructure as Code (IaC) for rapid environment provisioning and used for advanced anomaly detection and intelligent data cleansing.

4. Ensure Gen AI expertise within the data engineering as a managed service team
Ensure the team has expertise in the fundamentals of deep learning and the architecture of large language models (LLMs) and other Gen AI models, and promote close collaboration between the data engineering and the Gen AI/ML research teams to grasp their data and infrastructure needs.

5. Implement security and ethical governance for Gen AI data
Given Gen AI's potential and the sensitivity of its data, robust security and ethical governance are crucial. Ensure your data engineering as a managed service provider has security policies to protect the large datasets used for Gen AI training and inference.

Key components of data engineering as a managed service

Modern managed data engineering needs to address various challenges. We propose a checklist of core services to keep your data pipeline healthy and running:

End-to-end pipeline mastery for Gen AI: Manages the entire pipeline lifecycle, with AI-assisted data pipelines that help generate business insights. This includes integrating Gen AI to help with writing and optimizing pipeline code tailored for complex Gen AI needs.
Seamless hybrid and multi-cloud integration: Provides expertise across the full cloud environments, with architecture for extreme scalability and adaptability built with composable infrastructure for Gen AI workloads.
Intelligent automation leveraging Gen AI: Uses automation tools and leverages Gen AI for intelligent automation. This involves automating IaC, advanced anomaly detection, intelligent data cleansing, and recommending optimal resource configurations.
Scalable architecture for future growth: Solutions scale with data volume, supporting the ingestion and management of petabyte-scale structured and unstructured data from varied sources efficiently for Gen AI models. Gen AI-ready data architectures integrate traditional data warehouses/lakes with essential Gen AI components like vector databases and feature stores.
Proactive monitoring and support: Providing round-the-clock, automated monitoring and rapid response to any issues, ensuring your data pipelines always run smoothly, 24/7.
Robust compliance and ethical governance, including Gen AI data: Stays ahead of regulations and implements strong security. Crucially, it provides specific security measures to protect large Gen AI datasets, ensuring ethical governance.

Conclusion: A proactive and forward-thinking approach to managed data engineering services is crucial to becoming Gen AI-ready and leading the AI revolution, emphasizing extreme scalability, optimized pipelines, Gen AI-powered tools, specialized expertise and robust ethical security.

The question is: Are you ready to navigate the Gen AI shift?

Evaluating your situation always requires consideration of your specific business and technology context. Based on Atos’s experience in managing cloud and modern infrastructure we believe that an initial discussion would typically address questions such as:

Do you foresee using Gen AI soon?
Do you feel comfortable with your data foundation?
Have you already built a reliable system that can support the growth of your data flow?
Are your data streams really compliant?
Does your company have time, experience and resources in managing the required data pipelines?

Would you like to explore more? Reach out to us and discuss how we can build your data future with Gen AI-ready data engineering as a managed service!

Posted on: 04/07/25