Feeling overwhelmed by a big data snow storm?

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Introduction

Data engineers can be a key part of your data team by helping you deliver data-driven insights faster. Having a dedicated data engineer handle tasks like researching reliable data sources and preparing the data for your needs lets your subject matter experts focus on what they do best. Researchers, data scientists, and analysts don’t have to spend their valuable time on the burdens of big data.

Example Project

Let’s assume an analytics project requires historical maximum air temperatures, by day, for a specific city in the United States.

With some quick Google searches, it’s easy to find that weather observations are publicly available and free to use as part of NOAA’s Global Historical Climatology Network Daily (GHCN-D) dataset. The dataset contains observations from 106,200 weather stations with daily measurements of precipitation, temperature, and other conditions for 260 years. There are several options to access the data, including the NOAA website and Amazon Web Services (AWS) Open Data.

With the multiple data access options and the large amount of data, it can be overwhelming to find the data you are looking for, much like finding a few specific snowflakes in a snowstorm.

A data engineer can help your team find the fastest and cheapest way to explore and process the data.

Conclusion

While this is a specific example of how to use one publicly available dataset, the process and techniques apply to all big datasets. Data engineers have expertise in reducing processing time and costs for all datasets, allowing you to find those few valuable snowflakes faster.