Alter Everything
148: Present & Future of Data Engineering
Dec 20, 2023
Megan Dibble, a data operations expert, and Nick Schrock, founder of Dagster Labs, delve into the dynamic world of data engineering. They clarify the distinctions between data engineering and data analytics, and introduce the hybrid role of analytics engineer. The discussion uncovers the evolution of roles in data engineering, spotlighting the shift towards a software engineering mindset. They also tackle challenges like vendor fatigue, the need for quality data, and strategies for effective data orchestration, emphasizing its vital role in decision-making.
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Quick takeaways
- Data engineering has evolved into a crucial discipline over the last decade, becoming essential for managing the complexity of modern data systems.
- The emergence of orchestration tools is vital for integrating diverse data technologies, addressing the challenge of vendor fatigue in the industry.
Deep dives
Understanding Data Engineering vs. Data Analytics
Data engineering and data analytics are closely related yet distinct fields, each with different focuses. Data analysts typically work on business problems outlined by stakeholders, conducting ad hoc analyses to extract insights from existing datasets and translating those insights into dashboards. Conversely, data engineers concentrate on the foundational layer of managing the flow of data, built on software engineering principles, to store and retrieve data efficiently. Emerging roles like analytics engineers blur these lines by bridging the gap, but fundamentally, data engineering prioritizes maintaining and developing robust data pipelines over business-oriented tasks.
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