#data engineering

This image displays a syllabus for data engineering. Big data, cloud platforms, databases, data warehousing, ETL, programming, data pipelines, data modeling, real-time processing, and data security are some of the subjects covered. These subjects are key for developing data engineering skills. https://iabac.org/blog/what-is-the-syllabus-for-data-engineering

Dear community, As Kafka continues to shape modern data architectures, it's crucial for professionals to delve into scenario-based questions to deepen their understanding and application. Whether you're a seasoned Kafka developer or just starting out, here are some key scenarios to ponder: 1️⃣ **Scaling Challenges**: How would you design a Kafka cluster to handle a sudden surge in incoming data without compromising latency? 2️⃣ **Fault Tolerance**: Describe the steps you would take to ensure high availability in a Kafka setup, considering both hardware and software failures. 3️⃣ **Performance Tuning**: What metrics would you monitor to optimize Kafka producer and consumer performance in a high-throughput environment? 4️⃣ **Security Measures**: How do you secure Kafka clusters against unauthorized access and data breaches? What are some best practices? 5️⃣ **Integration with Ecosystem**: Discuss a real-world scenario where Kafka is integrated with other technologies like Spark, Hadoop, or Elasticsearch. What challenges did you face and how did you overcome them? Follow : https://algo2ace.com/kafka-stream-scenario-based-interview-questions/

Source: https://finance.yahoo.com/news/data-science-job-market-shrinking-122300456.html