Topics Shaping Data Engineering in 2025

Dear Reader…

Following on from the comparison on the Top Five Modern Data Platforms last week - we were curious as to what topics are top of mind for Data Professionals in 2025. From cloud-native architectures to AI-driven workflows, the role of the data engineering in enterprise is rapidly evolving. To get a better sense of how this is unfolding, we analysed discussions across Reddit, Discord technical forums, and blogs to uncover the most frequently discussed topics on data engineering today. Some readers will easily guess some of these, others might surprise you. Let’s take a dive into what is top of mind in the industry in 2025…

🔝Ten Insights from Online Communities

And the winner is…

1. ETL/ELT Processes and Modern Tooling

The shift from traditional ETL (Extract-Transform-Load) to ELT (Extract-Load-Transform) continues to dominate conversations. Tools like dbt and Apache Airflow are leading the charge, enabling modular pipeline design and SQL-centric workflows. However, debates persist about dbt’s limitations in handling complex orchestration scenarios. Meanwhile, Apache Spark remains a heavyweight for both batch and streaming workloads, with PySpark adoption outpacing Scala. Engineers are also refining best practices around idempotency and partitioning strategies to handle increasingly large datasets.

The shift from ETL to ELT has fundamentally streamlined data engineering workflows by leveraging the computational power of modern cloud platforms. ELT eliminates the need for intermediate transformation layers, allowing raw data to be loaded directly into centralised systems like Snowflake or BigQuery. This approach simplifies pipeline design, accelerates data ingestion, and enables analysts to work with raw datasets immediately, fostering faster time-to-insights. Additionally, ELT promotes flexibility by allowing iterative transformations directly within the data warehouse using SQL-based tools like dbt, empowering analytics teams to refine business logic without reloading data. The scalability of cloud-native architectures further supports ELT workflows by handling massive datasets efficiently while reducing operational overhead.

However, ELT introduces new challenges that require careful management. Performing transformations within cloud warehouses can lead to high compute costs as data volumes grow, and storing raw data increases the risk of downstream errors if quality checks are not rigorously implemented. While SQL-based transformations are sufficient for many use cases, highly complex logic may still require external processing frameworks like Spark or Python, leading to hybrid workflows. Additionally, governance becomes more complex as raw data must adhere to stricter compliance policies for privacy regulations like GDPR or CCPA. Despite these drawbacks, ELT remains the preferred approach for modern organizations seeking agility and scalability in their data engineering practices.

2. Cloud Platforms and Managed Services

Cloud platforms such as AWS, Azure, and Google Cloud are at the core of modern data architectures. Managed services like AWS Glue, BigQuery, and Snowflake are reducing operational overhead while sparking debates about cost-effectiveness. Serverless solutions like AWS Lambda are frequently compared to dedicated compute clusters for data ingestion tasks. Infrastructure-as-code tools such as Terraform and Kubernetes are also hot topics, especially for hybrid and multi-cloud deployments.

3. Real-Time Data Streaming and Processing

Real-time capabilities have become non-negotiable in 2025. Apache Kafka, Apache Flink, and Spark Structured Streaming dominate discussions on this front. Engineers are grappling with challenges like exactly-once processing guarantees and stateful stream aggregation while integrating streaming data with batch systems. Additionally, Delta Lake and Apache Iceberg are gaining traction as unified table formats that enable ACID transactions on data lakes.

4. Data Warehousing and Modeling

Despite the rise of data lakes, traditional data warehouses like Snowflake and BigQuery remain highly relevant due to their separation of storage and compute as well as their support for semi-structured data formats. Dimensional modelling is experiencing a surprising resurgence as engineers debate its role in decentralised architectures like data mesh. Techniques such as Data Vault 2.0 and anchor modelling are also gaining favour for their ability to support agile schema evolution.

5. AI/ML Integration in Data Workflows

Artificial intelligence is reshaping how data engineers work. Generative AI tools now assist with schema inference, pipeline optimization, and even natural language querying of datasets. Retrieval-Augmented Generation (RAG) workflows that integrate large language models (LLMs) with enterprise data are particularly popular, requiring tight integration with vector databases like Pinecone. Tools like MLflow and Kubeflow are also becoming standards for managing machine learning model deployment.

The integration of AI and ML into data workflows is reshaping the day-to-day responsibilities of data engineers, enabling them to focus on higher-value tasks while automating repetitive processes. AI-powered tools are now streamlining critical functions like data cleansing, transformation, and pipeline orchestration. For example, platforms such as Databricks and Apache Airflow are incorporating AI to predict pipeline failures, optimise workflows, and automate feature engineering. Additionally, AI-driven real-time monitoring ensures data quality by detecting anomalies and correcting inconsistencies as they occur, which is vital for maintaining reliable datasets in fast-paced environments.

This shift also blurs traditional role boundaries between analytics and AI/ML engineers. Data engineers are increasingly expected to manage hybrid workflows that combine SQL-based analytics with Python-driven ML tasks. Tools like dbt and Secoda leverage AI to automate transformations and metadata management, enabling engineers to collaborate more effectively with data scientists and analysts. Moreover, generative AI is being used to create self-healing pipelines and automate query generation, further enhancing efficiency. While these innovations empower engineers to innovate rather than troubleshoot, they also require upskilling in areas like ML model deployment and AI-native platforms.

6. Data Quality and Observability

Ensuring data quality has emerged as a top priority for engineering teams. Tools such as Great Expectations, Monte Carlo, and Datafold are enabling automated anomaly detection and lineage tracking across pipelines. The concept of "data contracts" is gaining traction as a way to enforce schema consistency across teams—especially in event-driven architectures where real-time reliability is critical.

7. Data Governance and Security

With increasing regulatory pressures such as GDPR and CCPA, discussions around governance have intensified. Role-based access control (RBAC), encryption-in-transit, and data masking are essential practices in this space. The principles of data mesh—decentralizing ownership while maintaining centralized governance—are frequently debated in forums alongside case studies highlighting both successes and pitfalls.

8. Programming Languages and Frameworks

SQL remains the lingua franca of analytics engineering, while Python continues to dominate scripting tasks across pipelines. However, there’s a growing bifurcation in skill sets: analytics engineers focus heavily on SQL-centric tools like dbt, while platform engineers prioritize languages like Scala (for Spark optimizations) or Go (for high-performance services). Rust is also emerging as a contender for building efficient data-intensive systems.

9. Emerging Architectures and Trends

Unified platforms such as Databricks Unity Catalog and Snowpark (from AWS) are simplifying workflows but raising concerns about vendor lock-in—a hot topic among engineers wary of losing flexibility. Data mesh implementations are maturing with discussions focusing on federated governance models that treat datasets as products. Meanwhile, edge computing is gaining attention for enabling low-latency preprocessing in IoT scenarios using technologies like WebAssembly (Wasm).

10. Career Development and Community Dynamics

As the job market grows more competitive, communities are buzzing with advice on upskilling in areas like AI/ML integration or earning cloud certifications from AWS or Azure. With Twitter/X losing its prominence as a discussion hub, Slack groups (e.g., Data Engineering Things) and Discord servers have become the go-to spaces for collaboration—though fragmentation remains an issue. Ethical concerns around AI automation displacing junior roles have also sparked heated debates on some online forums.

∞ A Field in Flux

The day-to-day workflows of data professionals are undergoing a profound transformation, driven by advancements in AI/ML integration, real-time processing demands, and the increasing complexity of modern data ecosystems. AI has become a critical enabler, automating repetitive tasks such as data mapping, pipeline orchestration, and anomaly detection - enabling you to focus on higher-value activities like designing scalable architectures and optimising data models.

In addition, the rise of hybrid workflows is reshaping the skillsets required for data engineering. As AI tools like dbt and Secoda blur the lines between analytics engineering, and machine learning, engineers are increasingly expected to manage end-to-end pipelines that integrate ML tasks alongside traditional data transformations. While these advancements bring incredible opportunities for efficiency and innovation, they also demand up skilling in AI-native platforms and governance strategies to ensure compliance with privacy regulations. The result is a more dynamic role for data engineers—one that balances automation with strategic oversight in an increasingly AI-driven landscape.

That’s a wrap for this week

Happy Engineering Data Pro’s