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I am just curious on how the state of current data science/analytics workflows in many different companies is. I know every business person just thinks we have magic table were we get to do SELECT * FROM magic_table and get the desired results. Everything is aligned with previous reports, everyone understands the charts and we keep goging. But I would like you to take a moment to step away from the lines of code and dashboards to share our views on here.
I wonder about data stacks, meaning
The Stack Showdown:
Storage Solutions: Are you team Cloud (AWS, GCP, Azure) or do you swear by on-premise data warehouses? Data querying & ETL tools: using DataGrip, DBeaver, directly from python et al. using connectors, or maybe Airflow, Talend...? Processing Units: do you crunch numbers in local or have dedicated/per process servers? Visualization: How do you paint your data pictures? Tableau, Power BI, or something more exotic like PowerPoint updated manually? The Requirement Riddle: Besides tech, how do you approach a shiny new requirement or feature request? Do you have a battle-tested workflow, or is it more of a creative chaos situation? The Pain Points Parade: Let’s hear about those pesky pain points that drive you up the wall. Is it the ever-changing schema, non-updated tables, the elusive data quality, no one looking for your output despite being urgent for the last 2 days... or perhaps the dreaded “It worked on my machine” curse?
*If Genie just gave you one wish, *what would you ask for (data workflow related ofc)? Share your stories, tips, and tricks – and let’s learn from the community. Let’s keep it light, keep it fun, and who knows – you might just pick up a nifty new tool or hack along the way!
