Diving into Databases
As I continue to immerse myself in the field of Cybersecurity, I recently embarked on a new direction: Database Engineering. I made this choice to better understand what databases can do, how they operate, and how they, managed securely. By broadening my horizons in this direction, I hope it will give me a better picture of databases and security, leading me to a more comprehensive approach to Microsoft Azure solutions.
Even though I’m only halfway through my course, I’ve already gained interesting experiences and insights. For instance, using Python in Databricks has prompted me to refresh and deepen my knowledge of Python. What I find interesting is that Python is not only a tool for database engineering but also for cybersecurity. While it may not be standard practice to use Python for security purposes, I’ve discovered it is fully capable of performing a wide range of security tasks. Think, for example, of performing security analyses or implementing advanced security features such as encryption and data leak prevention. These insights are not only increasing my understanding of database engineering, but also broadening my knowledge and skills in the field of cybersecurity.
In the past, I sometimes struggled to understand why data flowed through multiple databases in a specific way. These steps may have seemed unnecessary or complicated at first glance. But as I learn more about Databricks and data lakes, I’m beginning to see the necessity and importance of these steps, especially in terms of data analysis and presentation. Now, as I look deeper into the field of database engineering, I find my perspective on data architectures shifting. Previous courses provided me with a basic understanding of databases (AZ-104 / AZ-500), which was helpful, but now seems somewhat simplistic compared to the information and knowledge I am currently uncovering.
One of the concepts I’ve come to understand is the Extract-Transform-Load process. This is the process of obtaining data from various sources, transforming this data into a suitable format, and finally loading this filtered data into an end-goal system such as Power BI. By better understanding these data flows, I now realize that each of the databases in this flow has a specific role and function. The first database, for example, receives the raw, unprocessed data. This data is then filtered and transformed in the next phase before it is finally presented in a more readable format. This structured approach ensures that the data that is ultimately analyzed and presented is reliable, accurate, and relevant.
During the Extract-Transform-Load process, the data is particularly vulnerable. It is in this phase that the data is moved, transformed, and stored, and these are all moments when security issues can arise. Whether it’s protecting against unauthorized access, ensuring integrity, or safely storing the data in the end database – every step requires a strong security approach.
It’s crucial to have comprehensive protection strategies across the entire process. This includes safeguarding the data itself, for instance by encrypting sensitive information, limiting access to the database on the database level, and defending against external threats on the network level. This approach to security not only helps to keep the data safe but also preserves the integrity of the entire data processing and storage system.
While these principles of data security were already known to me, my experience with database engineering has made me realize even more how crucial these security measures are in the context of data processing. It has given me a different perspective on the complexity and importance of security in this field.
I can say that the field of database engineering is an interesting and unique area. I look forward to the rest of my learning journey and am excited about the insights yet to come.
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