How to: MLOps: Data Engineering (DataOps)
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1. [The state of data quality in 2020 – O’Reilly](https://www.oreilly.com/radar/the-state-of-data-quality-in-2020/)
1. [Why We Need DevOps for ML Data](https://tecton.ai/blog/devops-ml-data/) 
1. [Data Preparation for Machine Learning (7-Day Mini-Course)](https://machinelearningmastery.com/data-preparation-for-machine-learning-7-day-mini-course/)
1. [Best practices in data cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data.](https://www.researchgate.net/publication/266714997_Best_practices_in_data_cleaning_A_Complete_Guide_to_Everything_You_Need_to_Do_Before_and_After_Collecting_Your_Data)
1. [17 Strategies for Dealing with Data, Big Data, and Even Bigger Data](https://towardsdatascience.com/17-strategies-for-dealing-with-data-big-data-and-even-bigger-data-283426c7d260)
1. [DataOps Data Architecture](https://blog.datakitchen.io/blog/dataops-data-architecture)
1. [Data Orchestration — A Primer](https://medium.com/memory-leak/data-orchestration-a-primer-56f3ddbb1700)
1. [4 Data Trends to Watch in 2020](https://medium.com/memory-leak/4-data-trends-to-watch-in-2020-491707902c09)
1. [CSE 291D / 234: Data Systems for Machine Learning](http://cseweb.ucsd.edu/classes/fa20/cse291-d/index.html)
1. [A complete picture of the modern data engineering landscape](https://github.com/datastacktv/data-engineer-roadmap)
1. [Continuous Integration for your data with GitHub Actions and Great Expectations. One step closer to CI/CD for your data pipelines](https://greatexpectations.io/blog/github-actions/)
1. [Emerging Architectures for Modern Data Infrastructure](https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/)
1. [Awesome Data Engineering. Learning path and resources to become a data engineer](https://awesomedataengineering.com/)
1. Data Quality at Airbnb [Part 1](https://medium.com/airbnb-engineering/data-quality-at-airbnb-e582465f3ef7) | [Part 2](https://medium.com/airbnb-engineering/data-quality-at-airbnb-870d03080469)
1. [DataHub: Popular metadata architectures explained](https://engineering.linkedin.com/blog/2020/datahub-popular-metadata-architectures-explained)
1. [Financial Times Data Platform: From zero to hero. An in-depth walkthrough of the evolution of our Data Platform](https://medium.com/ft-product-technology/financial-times-data-platform-from-zero-to-hero-143156bffb1d)
1. [Alki, or how we learned to stop worrying and love cold metadata (Dropbox)](https://dropbox.tech/infrastructure/alki--or-how-we-learned-to-stop-worrying-and-love-cold-metadata)
1. [A Beginner's Guide to Clean Data. Practical advice to spot and avoid data quality problems (by Benjamin Greve)](https://b-greve.gitbook.io/beginners-guide-to-clean-data/)
1. [ML Lake: Building Salesforce’s Data Platform for Machine Learning](https://engineering.salesforce.com/ml-lake-building-salesforces-data-platform-for-machine-learning-228c30e21f16)
1. [Data Catalog 3.0: Modern Metadata for the Modern Data Stack](https://towardsdatascience.com/data-catalog-3-0-modern-metadata-for-the-modern-data-stack-ec621f593dcf)
1. [Metadata Management Systems](https://gradientflow.com/the-growing-importance-of-metadata-management-systems/)
1. [Essential resources for data engineers (a curated recommended read and watch list for scalable data processing)](https://www.scling.com/reading-list/)
1. [Comprehensive and Comprehensible Data Catalogs: The What, Who, Where, When, Why, and How of Metadata Management (Paper)](https://arxiv.org/pdf/2103.07532.pdf)
1. [What I Learned From Attending DataOps Unleashed 2021 (byJames Le)](https://jameskle.com/writes/dataops-unleashed2021)
1. [Uber's Journey Toward Better Data Culture From First Principles](https://ubr.to/3lo9GU8)
1. [Cerberus - lightweight and extensible data validation library for Python](https://docs.python-cerberus.org/en/stable/)
1. [Design a data mesh architecture using AWS Lake Formation and AWS Glue. AWS Big Data Blog](https://aws.amazon.com/blogs/big-data/design-a-data-mesh-architecture-using-aws-lake-formation-and-aws-glue/)
1. [Data Management Challenges in Production Machine Learning (slides)](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46178.pdf)
1. [The Missing Piece of Data Discovery and Observability Platforms: Open Standard for Metadata](https://towardsdatascience.com/the-missing-piece-of-data-discovery-and-observability-platforms-open-standard-for-metadata-37dac2d0503)
1. [Automating Data Protection at Scale](https://medium.com/airbnb-engineering/automating-data-protection-at-scale-part-1-c74909328e08)
1. [A curated list of awesome pipeline toolkits](https://github.com/pditommaso/awesome-pipeline)
1. [Data Mesh Archtitecture](https://www.datamesh-architecture.com/)
1. [The Essential Guide to Data Exploration in Machine Learning (by NimbleBox.ai)](https://nimblebox.ai/blog/data-exploration)
1. [Finding millions of label errors with Cleanlab](https://d