Reaching 20k+ readers on Medium and over 3k learners by email, I draw on my 4 years of experience as a Senior Data Engineer to demystify data science, cloud and programming concepts while sharing job hunt strategies so you can land and excel in data-driven roles. Subscribe for 500 words of actionable advice every Thursday.
[ETR #38] Powerful But Messy Data
Extract. Transform. Read.A newsletter from Pipeline Hi past, present or future data professional! As difficult as data engineering can be, 95% of the time there is a structure to data that originates from external streams, APIs and vendor file deliveries. Useful context is provided via documentation and stakeholder requirements. And specific libraries and SDKs exist to help speed up the pipeline build process. But what about the other 5% of the time when requirements might be structured, but your data isn’t? Unstructured data comes in many forms, including incomprehensible metadata from ioT devices; I have the most experience with textual data, so I can speak to how I recommend approaching this classification of data. Since I nearly always work with structured data at work, I’ll be speaking from my experience scraping web data, parsing text files and reading PDFs.
Finally, if you’re working with a particular type of data, understand what libraries are available to reduce the manual parsing that will be required. And remember, the only shape you don’t want your data in is (0,0). Thanks for ingesting, -Zach Quinn |
Extract. Transform. Read.
Reaching 20k+ readers on Medium and over 3k learners by email, I draw on my 4 years of experience as a Senior Data Engineer to demystify data science, cloud and programming concepts while sharing job hunt strategies so you can land and excel in data-driven roles. Subscribe for 500 words of actionable advice every Thursday.
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