How I Reduced My Query’s Run Time From 30 Min. To 30 Sec. In 1 Hour
The query optimization steps a senior data engineer took to reduce the process time of a query processing 1 billion+ rows.
The query optimization steps a senior data engineer took to reduce the process time of a query processing 1 billion+ rows.
Beginner-Friendly Python Web Scraping Projects.
LQL, Python and Audit Strategies For Diagnosis & Triage. Part 1: Logging Query Language LQL can be used in the Logs Explorer to fetch real-time data on Google Cloud products like Cloud Functions and Virtual Machines as well as non-GCP resources like resources connected to Amazon Web Services’
1 multistep job search prompt to objectively evaluate your candidacy before you even think about hitting “apply.”
Leverage a subtle bash framework to execute data pipelines in the background, saving time for mission-critical tasks.
One function you gloss over has the power to save you hours of development time — and preserve data accuracy.
Proactive strategies covering how not to annoy your senior data scientists, engineers or developers from a senior’s perspective.
Never write another schema, save on storage costs and more.
Why matching row counts are the “silent killer” of data integrity.
Prioritizing clearer visualizations with a project that delivers a very personal, very real ROI.
Highlighting a GitHub repo that might lead to your next remote data science opportunity — no matter where you live.
Optimize your GitHub portfolio to increase discoverability and discover the new tool to push your repo to the masses.
Creating real-time email alerts in Python with the Reddit and Gmail APIs.
Delve into the subtle technical and situational factors that determine whether your SQL query runs, stalls or fails.
Eliminate a tedious data governance chore after understanding privacy taxonomies and a simple BigQuery API implementation.
Explaining state: How to maintain it, why pipeline runs need to be identical and breaking down an abstract term.