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Data Engineering for Fraud Prevention - Angela Ramirez
Manage episode 379045046 series 2831626
Content provided by DataTalks.Club. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataTalks.Club or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
We talked about:
- Angela's background
- Angela's role at Sam's Club
- The usefulness of knowing ML as a data engineer
- Angela's career path
- Transitioning from data analyst to data engineer/system designer
- Best practices for system design and data engineering
- Working with document databases
- Working with network-based databases
- Detecting fraud with a network-based database
- Selecting the database type to work with
- Neo4j vs Postgres
- The importance of having software engineering knowledge in data engineering
- Data quality check tooling
- The greatest challenges in data engineering
- Debugging and finding the root cause of a failed job
- What kinds of tools Angela uses on a daily basis
- Working with external data sources
- Angela's resource recommendations
Links:
- LinkedIn: https://www.linkedin.com/in/aramirez1305/
- Twitter: https://twitter.com/angelamaria__r
- Github: https://github.com/aramir62
- Previous podcast talk: https://twitter.com/i/spaces/1OwGWwZAZDnGQ?s=20
Free ML Engineering course: http://mlzoomcamp.com
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
183 episodes
Share
Manage episode 379045046 series 2831626
Content provided by DataTalks.Club. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataTalks.Club or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
We talked about:
- Angela's background
- Angela's role at Sam's Club
- The usefulness of knowing ML as a data engineer
- Angela's career path
- Transitioning from data analyst to data engineer/system designer
- Best practices for system design and data engineering
- Working with document databases
- Working with network-based databases
- Detecting fraud with a network-based database
- Selecting the database type to work with
- Neo4j vs Postgres
- The importance of having software engineering knowledge in data engineering
- Data quality check tooling
- The greatest challenges in data engineering
- Debugging and finding the root cause of a failed job
- What kinds of tools Angela uses on a daily basis
- Working with external data sources
- Angela's resource recommendations
Links:
- LinkedIn: https://www.linkedin.com/in/aramirez1305/
- Twitter: https://twitter.com/angelamaria__r
- Github: https://github.com/aramir62
- Previous podcast talk: https://twitter.com/i/spaces/1OwGWwZAZDnGQ?s=20
Free ML Engineering course: http://mlzoomcamp.com
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
183 episodes
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