Join 📚 Kevin's Highlights
A batch of the best highlights from what Kevin's read, .
The core Azure technologies used to implement data engineering workloads include:
• Azure Synapse Analytics
• Azure Data Lake Storage Gen2
• Azure Stream Analytics
• Azure Data Factory
• Azure Databricks
Data engineering in Microsoft Azure - Training
wwlpublish
🌟 Introducing the top 5 Python testing tools that every developer should have in their toolkit 🌟
1️⃣ Pytest:
A powerful and easy-to-use testing framework.
Say goodbye to test classes
and hello to simple test functions! 👋
2️⃣ Unittest:
The classic.
It's built-in, it's reliable, and it's been around for ages.
A true Python staple. 🐍
3️⃣ Mock:
Isolate and rule the world! 🌍
Replace parts of your system under testing with mock objects
and focus on what really matters.
4️⃣ Tox:
Test your code in various Python environments.
Ensure compatibility and make your code versatile! 🌐
5️⃣ Hypothesis:
Property-based testing made easy.
Generate test cases automatically
and put your code through its paces. 🏃
Sarah Floris, MS’ Post
Sarah Floris, MS
The data engineering lifecycle
shifts the conversation
away from technology and
toward the data itself and the end goals that it must serve.
The stages of the data engineering lifecycle are as follows:
• Generation • Storage • Ingestion • Transformation • Serving
Fundamentals of Data Engineering
Reis, Joe;Housley, Matt;
...catch up on these, and many more highlights
Readwise