Join 📚 Kevin's Highlights
A batch of the best highlights from what Kevin's read, .
Here are some tips
that have helped me
overcome my negative thoughts:
*Set realistic goals* :
Unrealistic goals can lead to feelings of inadequacy and failure.
*Focus on your own progress* :
Focus on your own progress and celebrate your own achievements.
*Practice self-compassion* :
It’s normal to make mistakes and have setbacks.
*Surround yourself with positive people* :
Spend time with supportive and encouraging people
rather than those who are negative and critical.
*Seek support* :
consider seeking support from a therapist or counselor.
*Remember your "why"* :
focus on the bigger picture.
*Practice mindfulness* :
Don’t get lost in the past or future.
Staying present in the moment can reduce your self-doubt.
*Learn to appreciate yourself* :
Celebrate your progress and milestones.
Never Feeling Enough
Swirling Visions
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
When we unpack the common threads of how various people define data engineering, an obvious pattern emerges:
a **data engineer**
*gets data, stores it, and prepares it for consumption*
by **data scientists**, **analysts**, and others.
We define data engineering and data engineer as follows:
**Data engineering** is
the *development*, *implementation*, and *maintenance*
of **systems** and **processes** that take in raw data
and produce high-quality, consistent information
that supports downstream use cases,
such as analysis and machine learning.
**Data engineering** is
the intersection of
*security*,
*data management*,
*DataOps*,
*data architecture*,
*orchestration*, and
*software engineering*.
A **data engineer**
*manages the data engineering lifecycle*,
beginning with getting data from source systems and
ending with serving data for use cases,
such as analysis or machine learning.
Fundamentals of Data Engineering
Reis, Joe;Housley, Matt;
...catch up on these, and many more highlights
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