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The Data Engineering Toolkit: Infrastructure, DevOps, and Beyond

Simon Späti

Remember when data scientists spent 80% of their time wrestling with data wrangling instead of building models? I’d argue that today’s data engineers face similar challenges, but with the added complexity of infrastructure setup.

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Top 11 GenAI Powered Data Engineering Tools to Follow in 2025

Analytics Vidhya

What will data engineering look like in 2025? How will generative AI shape the tools and processes Data Engineers rely on today? As the field evolves, Data Engineers are stepping into a future where innovation and efficiency take center stage.

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Love and hate - Excel files and data engineers

Waitingforcode

Even though data engineers enjoy discussing table file formats, distributed data processing, or more recently, small data, they still need to deal with legacy systems. By "legacy," I mean not only the code you or your colleagues wrote five years ago but also data formats that have been around for a long time.

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On time with data engineering systems - timeline of the data

Waitingforcode

Timely and accurate data is a Holy Grail for each data practitioner. To make it real, data engineers have to be careful about the transformations they make before exposing the dataset to consumers, but they also need to understand the timeline of the data.

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The Ultimate Guide to Apache Airflow DAGS

With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code.

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Top 20 Data Engineering Project Ideas [With Source Code]

Analytics Vidhya

Data engineering plays a pivotal role in the vast data ecosystem by collecting, transforming, and delivering data essential for analytics, reporting, and machine learning. Aspiring data engineers often seek real-world projects to gain hands-on experience and showcase their expertise.

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Top 10 Data Engineering & AI Trends for 2025

Monte Carlo

Here’s where leading futurist and investor Tomasz Tunguz thinks data and AI stands at the end of 2024—plus a few predictions of my own. 2025 data engineering trends incoming. Small data is the future of AI (Tomasz) 7. The lines are blurring for analysts and data engineers (Barr) 8. Table of Contents 1.

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Airflow Best Practices for ETL/ELT Pipelines

Speaker: Kenten Danas, Senior Manager, Developer Relations

ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0

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A Guide to Debugging Apache Airflow® DAGs

In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs.

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Apache Airflow® 101 Essential Tips for Beginners

Due to its widespread adoption, Airflow knowledge is paramount to success in the field of data engineering. It is a versatile tool used in companies across the world from agile startups to tech giants to flagship enterprises across all industries.