5 Major Challenges Data Engineers Face When Working with AI Tools & Systems
Data Operations
Article
Feb 18, 2025
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Revefi team
In this blog, we highlight five key obstacles that data engineers encounter while handling AI systems.
Artificial Intelligence (AI) has become an integral part of modern business, powering applications ranging from fraud detection to cloud computing. As a result, the global cloud computing market will surpass $1 trillion by 2028.
Their role is essential in providing AI systems with well-organized, accurate, and high-quality data. However, even with ongoing improvements in AI and data engineering technologies, experts in this field continue to face significant obstacles.
…44% of SMBs use cloud infrastructure or hosting services, compared to 66% of small tech companies and 74% of enterprises.” - TechRepublic Report
In this blog, we highlight five key obstacles that data engineers encounter while handling AI systems.
1. Data Quality & Consistency
Ensuring data quality and consistency are met round-the-clock remains one of the biggest challenges in modern AI-driven projects. Vast amounts of structured and unstructured data are required to make accurate predictions or deliver intelligent insights. However, real-world data is often incomplete, inconsistent, and cluttered, which can significantly impact the performance of AI models.
Incomplete data can create biased AI models, while inconsistent formats from multiple sources demand extensive cleaning. Duplicate records introduce redundancies, which can distort model training. Additionally, extreme outliers and anomalies negatively impact overall accuracy.
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2. Performance Due To Scalability Issues
With the increase in AI applications, the demand for efficient and scalable data pipelines becomes increasingly vital. Data engineers frequently face challenges in expanding infrastructure to support large-scale AI models that rely on real-time or near-instant data processing.
Inefficient data pipelines create bottlenecks, which ultimately increase the overall processing time and latency. Additionally, managing computational and storage resources remains a challenge, as balancing workload distribution is crucial for maintaining efficiency.
3. Integrating Disparate Data Sources
Data engineers often work with data from multiple sources, including databases, APIs, streaming services, and third-party providers. Integrating these disparate data sources while maintaining consistency and reliability poses significant challenges. As AI-based applications continue to become more complex, and robust, the challenge associated with diverse data formats like CSV, JSON, XML, and Parquet begins to rear its ugly head.
Further adding to the misery is the fact that APIs and third-party data sources impose strict rate limits and licensing restrictions, which only complicates the integration process even more! Addressing these challenges is crucial for seamless data flow, ensuring AI models receive accurate, timely, and well-structured information for optimal performance.
4. Focus on Data Security & Compliance
Data engineers frequently deal with sensitive and proprietary information, and are hence subjected to strict regulatory laws like GDPR (Europe), CCPA (multiple countries), and HIPAA (USA). Accidental or unintentional exposures of sensitive data are often the cause of major data breaches. However, the legal, financial, and reputational damage suffered as a consequence can be far reaching.
Meeting security, privacy, and regulatory compliance benchmarks adds a layer of complexity to most AI systems, and implementing effective anonymization and masking techniques to protect PII while preserving analytical value continues to be a nightmare for Data Engineers.
5. AI Model Lifecycle Management
Data engineers are increasingly responsible for managing the AI data model lifecycle by ensuring that training data remains relevant and that models do not degrade over time due to data drift. Data drift occurs when real-world data distributions change, leading to model inaccuracy.
Some of the most persistent challenges in AI Model lifecycle management includes:
Shifts in data distributions, such as data drift and concept drift, which can compromise model performance over time. Keeping models updated with fresh data while minimizing downtime presents a significant challenge.
Maintaining consistency across different datasets and model versions is essential for reproducibility, making version control a critical factor.
Additionally, integrating MLOps effectively demands a strong infrastructure that supports seamless machine learning workflows, enabling continuous integration and deployment (CI/CD) for optimal performance and scalability.
Conclusion
The global AI application industry is bursting at the seams, and is projected to soar to new heights.
As Data Engineering is becoming a foundational pillar for AI success, it comes with significant challenges. Ensuring data quality, scalability, seamless integration, security, and AI model lifecycle management are critical hurdles data engineers must overcome.
Advanced AI solutions, such as Revefi’s AI Data Engineer (or RADEN for short) equips data engineers with automation tools to streamline repetitive tasks, enhance efficiency, and improve collaboration. Utilizing Raden enables Data Engineering professionals to expand their expertise, take on greater responsibilities, and refine their technical skills.
By integrating AI-driven technologies, data engineers can shift their focus to strategic projects, data architecture, and innovation, positioning themselves as industry leaders in the rapidly evolving field of data management.
