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Java Juggernaut: The key to data engineering mastery
When we think about data engineering, the first programming skills that usually come to mind are SQL and maybe Python. SQL is this well-known language for querying data, deeply ingrained in the world of data and pipelines. Python, on the other hand, has become quite powerful in data science and is now making its mark in the evolving field of data engineering. But, is this common belief accurate? Are SQL and Python really the most important programming skills for Data Engineers? In this article, I’ll share my experiences on this topic, aiming to help young professionals figure out the best skills to make the most of their time and energy.
In today’s data engineering, we handle a massive amount of data. The main job is figuring out how to gather, change, and store this huge load of data every day, hour, or even in real-time. What makes it trickier is making sure different data services can smoothly run on various systems without worrying about what’s happening underneath.
In the last 15 years, smart folks have come up with distributed computing frameworks to deal with this data overload. Hadoop and Spark are two big names in this game. Because both these frameworks are mainly built using JVM (Java Virtual Machine) languages (Hadoop uses Java, and Spark uses Scala), many data and software experts believe that Java and Scala are the way forward in data engineering.
Moreover, the ability of JVM applications to be portable makes them an excellent choice for data applications operating across diverse systems and environments. You can develop data pipelines that seamlessly run on various cloud and local setups, allowing you to scale your systems up or down without concerns about the underlying infrastructure.
Now that we’ve explored the benefits of Java and Scala, or more broadly, JVM-based data applications, in handling big data, the next logical question is: what do…
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