Thoughts on Data Engineering in 2025
A look at the current state of data engineering and key trends shaping the field in 2025. Written by Claude.
Here are my thoughts on the state of data engineering in 2025:
Modern data engineering has evolved significantly from its ETL-focused roots into a discipline that sits at the intersection of software engineering, data infrastructure, and analytics enablement. Several key trends and shifts are shaping the field:
The Rise of Data Platforms We're seeing a move away from treating data pipelines as isolated flows toward building comprehensive data platforms. These platforms abstract away complexity while providing self-service capabilities to data consumers. The modern data platform typically includes:
- Data ingestion and synchronization layers (often powered by tools like Airbyte or Fivetran)
- Transformation and modeling layers (dominated by dbt)
- Quality monitoring and observability
- Access controls and governance
- Discovery and documentation
This platform approach helps organizations scale their data operations while maintaining reliability and governance.
Software Engineering Practices in Data Data engineering is increasingly adopting software engineering best practices. This includes:
- Version control for data transformations and pipeline code
- CI/CD for data pipelines
- Testing frameworks for data quality
- Observability and monitoring
- Infrastructure as code for data systems
This shift has been enabled by tools like dbt, which brought software engineering practices to data transformation, and data build tools that support testing and deployment automation.
The Metrics Layer Revolution The emergence of the metrics layer (or semantic layer) is addressing a long-standing pain point in analytics. Tools like MetricFlow, dbt Metrics, and Cube.js are helping organizations define metrics as code, ensuring consistency across all analytics tools and reducing redundant calculations. This represents a fundamental shift in how organizations manage their key business metrics.
Streaming and Real-Time Data While batch processing still dominates most use cases, real-time data processing is becoming increasingly important. Modern architectures often combine:
- Change Data Capture (CDC) for database replication
- Event streaming platforms for real-time events
- Stream processing for continuous transformations
- Real-time serving layers for immediate access
Tools like Debezium, Kafka, and Flink are making real-time architectures more accessible, though the complexity of operating these systems remains a challenge.
Data Quality and Observability Data quality has moved from an afterthought to a central concern. Modern data engineering emphasizes:
- Automated testing of data pipelines
- Continuous monitoring of data quality metrics
- Lineage tracking and impact analysis
- Alerting on anomalies and failures
Tools like Monte Carlo, Datafold, and Great Expectations are making it easier to implement comprehensive data quality frameworks.
The Rise of AI/ML Infrastructure Data engineers are increasingly responsible for building and maintaining the infrastructure that supports machine learning operations. This includes:
- Feature stores for ML feature management
- Model training pipelines
- Model deployment and serving infrastructure
- Model monitoring and observability
This convergence of data engineering and MLOps is creating new challenges and opportunities for the field.
Challenges and Pain Points Despite these advances, several challenges remain:
- The complexity of modern data stacks can be overwhelming
- Cost management of cloud data platforms
- Governance and security in distributed systems
- Skill gaps in emerging technologies
- Balancing speed of development with reliability
Looking Forward As we move through 2024 and into 2025, several trends are likely to continue:
- Further consolidation in the data tools market
- Increased focus on cost optimization
- Better integration between streaming and batch processing
- More automated data quality and testing tools
- Enhanced support for AI/ML workflows
The role of data engineers is also evolving. Beyond building pipelines, they're becoming platform engineers who enable self-service data capabilities while maintaining governance and reliability. Success in modern data engineering requires not just technical skills but also a deep understanding of business needs and data governance principles.
The field continues to mature, with better tools and practices emerging regularly. However, the fundamental challenges of managing complex data systems while ensuring quality, security, and accessibility remain at the heart of the discipline. Organizations that can effectively navigate these challenges while building scalable, reliable data platforms will be best positioned to derive value from their data assets.