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For those new to Databricks Certified Data Engineer Professional exam, it’s a certification you need. In this article, I’ll break down exactly how and why. 1. Why take the Databricks Certified Data Engineer Professional exam? 2. Why should you take the exam if you have a background in Data Engineering? 3. What the exam is looking for in terms of your knowledge of Databricks? view it now 4. How to prepare for the Databricks Certified Data Engineer Professional exam. As a recent cert
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My experience as a Data Engineer was instrumental in my growth as a Data Scientist. For my Master’s thesis project, I used a Hadoop cluster to analyze social media data and developed a scalable Big Data pipeline. I’ve also worked with Apache Spark, Apache Hive, and Apache Oozie. During my internship at SAS, I helped a team with data cleaning, data preprocessing, and ETL for data science projects. It was challenging to handle large datasets, so I received an intern certification for Data Prep
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Inspiringly, I believe that cloud data professionals have a special mission to work on data platform as a whole, from conceptualization to execution. The cloud data platform is a huge deal, the kind of data that never ends, never gets old, no matter how the technology changes. It is the only platform that can meet the demand, with the only way to do it is through innovation. This task of the cloud data professional is not an easy one, but it is a big responsibility, and I have the courage to take this challenge. It means that my work will
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Databricks Certified Data Engineer Professional (DCEP) is the highest credential in Databricks’ professional certification program. With the certification, you can gain the confidence to make data-driven decisions in the enterprise. According to the official website of Databricks, Databricks Certified Data Engineer Professional exam is a standard certification requirement for the certification of Databricks’ professionals. The exam, developed by Databricks, is based on the Cloud Data Engineering Professional exam (D