Data engineering encompasses SQL and Python skills, but Java and Scala are increasingly important in handling large amounts of data. Distributed computing frameworks like Hadoop and Spark, built on JVM languages, offer portability across systems and environments. Data pipelines in JVM-based applications can be developed using Java or Scala, with tools like Apache Maven for project management. Apache Airflow helps schedule and execute data pipelines, and CI/CD tools enable seamless development and deployment across different environments.
Data Engineering: Java Juggernaut
When it comes to data engineering, many professionals believe that SQL and Python are the most important programming skills. However, the landscape is evolving, and Java and Scala are becoming increasingly valuable in the field. Why? Because data engineering involves handling massive amounts of data and ensuring that different data services can run smoothly on various systems. Distributed computing frameworks like Hadoop and Spark, which are built using JVM languages, make Java and Scala essential skills in data engineering.
Practical Solutions with Java and Scala
In data engineering projects, data pipelines are typically developed using Java or Scala. These pipelines consist of multiple classes that use frameworks like Spark for reading, transforming, and writing data. Hive tables are often used as data sources. To manage projects effectively, tools like Apache Maven can be utilized. The ultimate goal is to build a Java application in the form of a jar file, which can be invoked using job and workflow management systems like Apache Airflow.
For more advanced practices, data pipelines can be scheduled for batch processing or triggered based on events using tools like Apache Airflow or AWS Lambda. Continuous Integration/Continuous Deployment (CI/CD) tools like Jenkins and GitHub Actions help with developing and deploying pipelines across different environments. This ensures a smooth and automated transition of pipelines throughout the development lifecycle.
Value of AI in Data Engineering
If you want to evolve your company with AI and stay competitive, Java and Data Engineering can be your advantage. AI can redefine your way of work by automating customer interactions and improving business outcomes. To get started with AI, follow these steps:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com or follow us on Telegram (t.me/itinainews) and Twitter (@itinaicom).
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Java and Data Engineering
- Towards Data Science – Medium
- Twitter – @itinaicom
