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9 Data Engineering Skills to Master in 2024

As we step into 2024, we partnered with Database Trends & Applications (DBTA.com) to take stock of the rapidly evolving landscape. Data Engineers play a key role in making data ready to use for any data-driven application. The excitement for Generative AI in executive boardrooms last year and the race to leverage LLMs have caused a surge in demand for data engineering.

These forces are not only reshaping the data engineering landscape but also raising the bar for skills required in the field. We put together a list of top data engineering skills to adapt and thrive in 2024:

  1. Unstructured Data Management: With LLMs come documents, reports, web pages, slides, and other unstructured data sources, challenging data engineers to adapt beyond traditional structured data.
  2. Hallucination & Data Drift Prevention: False or misleading output generation can present significant challenges. Engineers should adopt techniques to detect changes in data distribution, setting thresholds for acceptable variation, and preprocessing data.
  3. Multi-pattern data: Data engineers must know how to orchestrate across multiple systems and platforms for integration, data products, data quality, data mastering, monitoring, etc.
  4. Data Lineage: Data lineage is critical in tracing the source and evolution of data, so you’re prepared when things go awry.
  5. Code Generators & Co-pilots: Engineers must incorporate these tools judiciously into their workflow, enhancing productivity without compromising code quality or deviating from organizational norms.
  6. Metadata-powered Data Architecture: Data Engineers today should think of metadata first as a way to build scalable solutions.
  7. Automating Maintenance: The traditional approach of manually managing and fixing data pipelines is becoming impossible to scale.
  8. Cloud & Hybrid Strategies: Cloud adoption continues at a rapid pace, but there is a growing number returning to the data center for cost savings. Data engineers must be adept at navigating and optimizing these hybrid environments.
  9. Advanced Analytics and Machine Learning Integration: Data engineers need to be skilled at integrating machine learning models with existing data pipelines.

The future of data engineering is bright, and those who invest in these skills will undoubtedly be at the forefront of this exciting and dynamic field. To read more about DBTA’s take on the role of data engineers in 2024 and dive into the skills you need to succeed, read the full report here.

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