data-engineer-roadmap
Data-Science-Roadmap
Our great sponsors
data-engineer-roadmap | Data-Science-Roadmap | |
---|---|---|
68 | 4 | |
11,939 | 2,853 | |
1.3% | - | |
0.0 | 8.7 | |
over 2 years ago | 5 months ago | |
- | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
data-engineer-roadmap
- Pitanje za data engineering?
-
How should I start learning/implementing DevOps in data engineering projects?
In DevOps tools I've worked with GitHub + Jenkins, GitLab + k8s, and I'm now primarily working in the Argo Stack. Depending on where you're at technically, you might use something different. IaC is a ust as well, maybe some config management. Generally I've found that as a Data Engineer with a lot of infra/CICD knowledge, I generally get pigeonholed into those positions on a team, so be prepared for that. I really like this roadmap for DevOps , so you can see where your tech skills are at currently, and what you may need to learn. On top of that, you'll need to learn some data tools. Airflow + dbt is hot right now, Argo is sometimes used in MLOps, Azure Data Stack (I'm not familiar with it) seems common, and probably Spark in almost all cases. You can also checkout in visualization tools probably further down the line, I generally stick to something free when learning on my own, Superset or Google Data Studio (Might be Looker Studio now? Not sure, it's been a while). Here's a roadmap for DE too. I love these roadmaps for getting started, but don't let them distract you from exploring a path more appropriate to what you want to achieve. Generally I've found that as a Data Enigneer with a lot of infra/CICD knowledge, I generally get pigeonholed into those positions on a team
- What is roadmap to enter into data engineering?
- Need help on Data Engineering Roadmap
-
Woman interested in data engineering with Python background
Anyways, sorry bit of a rant - I land somewhere in the middle. I would say take formal classes and resources when you can. If you have access to a free course a semester, that's incredible in my opinion. If I were in your shoes, I would follow a roadmap and see if there are courses that check off a box in that roadmap. So for example, you know you need to learn CS fundamentals - see if you can take a DSA class or something. Or take a class on databases. Or an OOP or databases class. I would take those classes if I had the opportunity just because I didn't when I was in college. No one course will check every box for sure.
- 1 Year Development Plan
- How to utilise SQL/Data engineering skills
-
Got my first DE role as a JR
I don't remember all of the name of the courses but I think this roadmap can put you in the right direction https://github.com/datastacktv/data-engineer-roadmap
- What things must I master as a data engineer?
-
What do you do professionally and how much do you earn?
You can follow this roadmap https://github.com/datastacktv/data-engineer-roadmap I have already replied some redditors with suggestions, you can read them.
Data-Science-Roadmap
- Road map data science/ machine learning
- Mudança de carreira- programação
-
Career Roadmaps
Data science: https://github.com/Moataz-Elmesmary/Data-Science-Roadmap
- Data-Science-Roadmap: Data Science Roadmap from A to Z
What are some alternatives?
golang-developer-roadmap - Roadmap to becoming a Go developer in 2020
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
developer-roadmap - Interactive roadmaps, guides and other educational content to help developers grow in their careers.
stat-cookbook - :orange_book: The probability and statistics cookbook
adventofcode - :christmas_tree: Advent of Code (2015-2023) in C#
Data-Science-Cheatsheet - A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.
materialize - The data warehouse for operational workloads.
awesome_time_series_in_python - This curated list contains python packages for time series analysis
Apache HBase - Apache HBase
Portfolio-Guide - A guide and summary to my projects and case studies.
awesome-opensource-data-engineering - An Awesome List of Open-Source Data Engineering Projects
www.mlcompendium.com - The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.