DATA ENGINEERING
Java Juggernaut: The important thing to data engineering mastery
Once we take into consideration data engineering, the primary programming skills that typically come to mind are SQL and perhaps Python. SQL is that this well-known language for querying data, deeply ingrained on the planet of knowledge and pipelines. Python, however, has turn out to be quite powerful in data science and is now making its mark within the evolving field of knowledge engineering. But, is that this common belief accurate? Are SQL and Python really a very powerful programming skills for Data Engineers? In this text, I’ll share my experiences on this topic, aiming to assist young professionals determine the perfect skills to benefit from their time and energy.
In today’s data engineering, we handle a large amount of knowledge. The essential job is determining methods to gather, change, and store this huge load of knowledge each day, hour, and even in real-time. What makes it trickier is ensuring different data services can easily run on various systems without worrying about what’s happening underneath.
Within the last 15 years, smart folks have provide you with distributed computing frameworks to cope with this data overload. Hadoop and Spark are two big names on this game. Because each these frameworks are mainly built using JVM (Java Virtual Machine) languages (Hadoop uses Java, and Spark uses Scala), many data and software experts imagine that Java and Scala are the best way forward in data engineering.
Furthermore, the power of JVM applications to be portable makes them a wonderful selection for data applications operating across diverse systems and environments. You may develop data pipelines that seamlessly run on various cloud and native setups, allowing you to scale your systems up or down without concerns concerning the underlying infrastructure.
Now that we’ve explored the advantages of Java and Scala, or more broadly, JVM-based data applications, in handling big data, the subsequent logical query is: what do…