Data
Data Engineering
Data engineering is a set of activities around building and maintaining a company's data pipeline systems.
Intro to Data Engineering
Data engineering is the process of designing, building, and maintaining the infrastructure that supports data storage, processing, and analysis. It involves a range of technologies and techniques, including Data Modeling, ETL (extract, transform, load) processes, and data pipelines. Data engineers work closely with data scientists, analysts, and other stakeholders to ensure that data is accurate, accessible, and secure.
What is Data Engineering?Lindsey answers questions to explain what data engineering is, including these: Can you give me an overview first? How do you relate to the other data teams? What part of the business does your work impact? Why do you need to do all this to data? What are all the data sources? Ok, but what product do you create?
A Beginner’s Guide to Data EngineeringThis a terrific introductory guide that covers: What is data engineering? Why is it challenging? How can it help you or your organization scale?
Related Skills
- ROI for Data Work
- MLOps Platforms
- Prioritization for Data Work
- Structuring Data Teams
- Effective Dashboards
- SQL
- Machine Learning
- Data Science Career Ladders
- Neural Networks
- Analytics
- Analysis Documentation
- Data Infrastructure
- Cohort Analysis
- Data Tools
- ETL
- Data Soft Skills
- Data Dictionary
- Data Governance
- Data Roadmaps
- Event Data
- Personalization
- OCR
- Data Warehouse
- Deep Learning
- Sampling Algorithms
- Data Intuition
- Linear Regressions
- Data Cleaning