How to become Big Data Engineer in 2023 | CoderMong

Becoming a Big Data Engineer in 2023 requires a combination of technical skills, experience, and an understanding of the latest technologies and trends in the field. Here are some steps you can take to become a Big Data Engineer:

Start by learning the basics of Big Data: Understand the concepts of distributed systems, data warehousing, and data processing. Learn about the different types of data storage systems and the various data processing frameworks such as Hadoop, Spark, and Flink.

Learn about the different types of data: Understand the differences between structured, semi-structured, and unstructured data, and how to work with each type of data.

Learn about data warehousing: Understand the concepts of data warehousing and data modeling, and learn how to use data warehousing technologies such as Hive, Impala, and Presto.

Learn about data processing frameworks: Understand the concepts of distributed computing and learn how to use data processing frameworks such as Hadoop, Spark, and Flink.

Learn about data storage systems: Understand the different types of data storage systems such as HDFS, S3, and Kafka, and how to work with each system.

Learn about data security: Understand the different types of data security such as encryption, authentication, and authorization, and learn how to implement them in your data processing pipeline.

Learn about data governance: Understand the concepts of data governance and data quality, and learn how to implement them in your data processing pipeline.

Learn about data visualization: Understand the concepts of data visualization and learn how to use data visualization tools such as Tableau, Power BI, and Looker.

Learn about data integration: Understand the concepts of data integration and learn how to use data integration tools such as Apache Nifi, Talend, and Informatica.

Learn about cloud computing: Understand the concepts of cloud computing and learn how to use cloud platforms such as AWS, Azure, and GCP for Big Data Engineering.

Learn about containerization: Understand the concepts of containerization and learn how to use technologies such as Docker and Kubernetes for Big Data Engineering.

Learn about stream processing: Understand the concepts of stream processing and learn how to use technologies such as Kafka and Flink for stream processing.