Hi everyone! I'm looking to become a Data Engineer and I could really use some help figuring out what to study to excel in this field. Just to give you some insight, I don't have an IT background but I'm planning to dedicate about 3-4 hours a day to learning. So far, I've started with SQL and AWS. I've compiled a tech stack that I plan to follow in this order: SQL, Git & GitHub, Python, AWS, DataBricks, DBT, and Apache Airflow. I'm also considering certifications for AWS, DataBricks, DBT, and Airflow to boost my credentials. Here are my main questions:
1. Does the tech stack and order I've chosen seem good, or should I add or remove anything?
2. I'm a bit confused about certifications since both AWS and DataBricks offer similar ones. Should I pursue both, or just one? If it's just one, which would be more beneficial?
3. I've chosen AWS over GCP and Azure because I read that AWS has the largest market share among these.
I'm open to any additional suggestions outside of what I've asked. Thanks in advance!
2 Answers
It's good to see you focusing on certifications; they can really enhance your profile. As for the AWS vs. DataBricks certs, if you have to choose, I’d recommend going with AWS first due to its wide recognition in the industry. Eventually, a DataBricks cert could be valuable, especially if you work often with big data tools. And yes, AWS is a popular choice for a reason—it holds a significant market share!
It looks like you’re on the right track! I’d say getting highly proficient in SQL is crucial, and it's great that you're starting there. You might want to look into learning Pandas and Jupyter Notebooks—they're fantastic for data manipulation and visualization. Also, getting a feel for Spark is helpful, especially since DataBricks is built on it. Best of luck on your journey!
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