Hey there, fellow data enthusiasts! I recently stumbled upon a Reddit post that resonated with me, and I thought it was worth exploring further. The original poster asked if DataOps is a viable career path, and I’d like to share my thoughts on the matter.
From what I understand, DataOps is all about managing data pipelines, onboarding new data sources, writing automation scripts, and ensuring SLAs are met. It’s like DevOps, but for data. The poster has been doing this type of work for a while, but they’re unsure if it’s a recognized career path.
In my opinion, DataOps is an essential part of the data engineering landscape. As data becomes increasingly important for businesses, the need for efficient data management and processing will only grow. DataOps professionals will play a crucial role in ensuring that data is accurate, reliable, and accessible to those who need it.
So, should you focus on learning PySpark and other Big Data tools to become a data engineer, or should you explore DataOps further? I think both paths have their merits. Data engineers are in high demand, and having skills in PySpark and other Big Data tools can open up many job opportunities. However, DataOps is a specialized field that requires a unique set of skills, and having expertise in this area can set you apart from others.
Data platform engineering jobs are also on the rise, which is another indication that DataOps is a growing field. I think it’s worth exploring both options and seeing which one aligns better with your career goals and interests.
What do you think? Do you have any experience in DataOps or data engineering? Share your thoughts in the comments!