Reliability Scalability Maintainability Data Engines as developers see it; database, ccaches, search index, stream processing, batch processing. How the data is distributed in disks encoding data Reliability - anticipate faults and can tolerate them. Faults - component failure. failure - system failure. hardware faults. software faults. human errors. telemetry using interfaces, decoupling, testing Scalability - Load parameters - request to web server - read or write to database - cache hit rate - active users could be average case or small number of extreme cases Twitter example : 4.6K to max 12k writes per user, but 300K reads per user. So work is done pushing writes to individual users caches at write time so read time can be faster. write times become a challenge when they involve so much leg work. still done within 5 seconds. Now twitter does a hybrid model where most tweets follow above approacch, but celebrity tweets are sent at read time. Performance : throughpu...
About me : Hello there, I'm Ananya Jayakumar - 33 years old when I start this blog , 5 months into my pregnancy , 10 years experience in tech : Operations / Data analysis / ETL / dash-boarding / managing teams / Solving problems. Pregnancy - Slowing your career I just completed a personally fulfilling year onboarding a team and training them to perform, tackling fraud, building dashboards and creating automated ETL behind the dashboard. But since I'm going on maternity leave, I'm training folks on my job - making it easy to replace myself and jeopardizing a promo that I'd otherwise been well placed for and also having to coming back from maternity leave - and starting from scratch as if all the stuff I did before the leave does not count. It's also a time when my company is getting acquired, the business and products are changing and not the best time to be planning maternity. I feel like I have potential to be in a better place. Pregnancy - the baby's most i...
< 2 years back - need to brush up> This was a good intro to relational modeling, data warehousing with fact and dimensions. using group sets and cubes for faster analytics, slicing, dicing, drill down and roll up. The project was a whole bunch of transformations converting a relational model to a data warehouse model. There were also a bunch of methods used to create schemas repetitively. When I brush up this course, I will update the learning here along with code snippets.
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