The Role of AI in Data Engineering

Artificial intelligence (AI) has transformed data engineering, changing how organizations manage, process, and leverage their data assets. Traditionally, data engineering relied on manual tasks like data collection, storage, and processing.

According to Gartner, by 2025, 60% of data engineering tasks will be automated, driven by AI’s ability to enhance workflows, data quality, and real-time data processing capabilities. 

In this article, we explore how AI drives these advancements, backed with examples of tools and technologies.

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Data Engineering

Historically, data engineering is a sequence of manual processes driving data flow from source to destination. Standard workflow involved:

1. Data ingestion Data is ingested from multiple, heterogeneous sources
2. Data cleaning to ensure data quality, accuracy and consistency
3. Data transformation through methods such as normalization or feature engineering for analysis and processing
4. Data storage and management in scalable infrastructure, e.g., repositories or data bases,
5. Monitoring and governance to maintain quality, security, and compliance

Today, AI is embedded into each of these processes, in two main forms:

• Machine Learning (ML) for tasks like pattern detection, anomaly spotting, or predictive modeling
• Natural Language Processing (NLP) for text parsing, entity extraction, and automated translations.

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Data Ingestion 

Automated Data Ingestion

Collecting data from diverse sources is becoming more efficient with ML-based mapping techniques of data flows. Tools such as AWS Glue or Talend capture schema inconsistencies and offer fixes. Cloud-native streaming services like AWS Kinesis Data Analytics, which integrates with TensorFlow or Amazon SageMaker, provide extended capabilities, enabling data to be categorized and partially transformed before it even lands in storage. 

Real-Time Data Processing

With the big data era, where data is streamed continuously from IoT sensors, social media channels, and enterprise systems, real-time analytics becomes crucial. Several use cases can benefit from a rapid feedback loop to enhance decision making such as fraud detection or dynamic pricing or real-time recommendation. Platforms like Apache Kafka and AWS Kinesis now integrate with ML models to filter and classify streaming data on the fly. Retail giants like Amazon use these tools to power dynamic pricing algorithms, adjusting prices in milliseconds based on live market trends. 

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Data Cleaning

Data quality is non-negotiable for reliable analytics and informed decision-making. 

Large datasets can be automatically profiled to find anomalies and inconsistencies using tools like Talend Data Quality and H2O.ai. These tools can also suggest corrective actions.

• Talend Data Quality overlaps with the broader Talend suite, adding advanced machine learning features about automation and rules.
• H2O.ai is an AI/ML-driven platform including AutoML. H2O.ai can perform advanced analytics, anomaly detection, and ML-based transformations.

Techniques like Natural Language Processing (NLP), clustering algorithms, and rule-based systems can be applied to automate data cleansing tasks. For example:

• Deduplication: Libraries like Dedupe (Python) or AWS Entity Resolution uses ML to cluster data records and detect near-duplicates even when fields do not match exactly.
• Standardization: NLP techniques, e.g., spaCy, OpenNLP, Hugging Face Transformers can parse unstructured text fields and transform them into standardized formats, e.g., converting “NYC” and “New York City” into a consistent format.
• Normalization: Tools like dbt (Data Build Tool) or Apache Spark can automatically convert disparate data types or measurement units to maintain consistency in data warehouses or data lakes. 

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Transformation

Extract, Transform, Load - ETL 

Traditional ETL required meticulous scripting. AI alters this by automating repetitive tasks and optimizing pipeline code. workflows by automating repetitive tasks. Databricks and Azure Data Factory embed ML to optimize code execution, while Informatica CLAIRE analyzes metadata to suggest pipeline improvements.

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Data storage and Management 

As data volumes explode, AI-driven metadata management tools like Alation, Collibra and Databricks Unity Catalog tag and document assets automatically, improving discoverability. Meanwhile, cloud providers like AWS and Azure apply ML to storage tiering. AWS S3 Intelligent-Tiering analyzes access patterns to shift data to cost-effective storage, saving enterprises millions annually. 

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Monitoring and Governance

Pipeline Orchestration 

Complex data environments are often distributed across different nodes, using systems like Apache Spark clusters, Kubernetes containers, or hybrid cloud infrastructures. 

Pipeline orchestration in such environments can be tricky requiring dynamic job scheduling, precise resources allocation, and workloads rerouting based on real-time demands. Such sophisticated orchestration can rely on reinforcement learning (RL) available in AI-driven Frameworks like Kubeflow or Apache Airflow with machine learning plugins. This approach can prevent bottlenecks, reduce operational costs, and dynamically scale with workload spike.

Proactive Governance

AI is turning reactive monitoring into proactive governance. Tools like Monte Carlo use ML to detect pipeline anomalies before they escalate, while Prefect dynamically adjusts workflows using reinforcement learning. In finance, firms like JPMorgan Chase integrate Splunk and PagerDuty to predict and mitigate fraud detection system failures.

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Conclusion

AI is already making a difference in data engineering practices. It is unlikely to replace traditional data engineer roles entirely, but certainly reshaping the data landscape and paving the way for innovation.