In 2024, data engineering remains relevant and is one of the fastest-growing fields. We have seen the demand for data engineers rise exponentially in the past few years, and it is predicted to continue in 2024. Data engineers play a crucial role in building the data pipelines and infrastructure that fuel insights and innovation, promoting data-driven decision-making.
By definition, data engineering is a field of data that involves designing, developing, and managing the systems and architecture for collecting, storing, processing, and analyzing data.
In this article, we are going to explore a well-detailed roadmap for becoming a data engineer in 2024. I have broken down the steps into four stages, where each stage includes tools and technologies that you can learn to progress to the next step. I hope this will be helpful.
Stage 1: Mastering Data Engineering Fundamentals.
As advised in any other career, always start by getting your basics right. Here, start by developing an in-depth understanding of what data engineering entails and establish a robust programming foundation.
You can learn SQL and any of the following programming languages: Python, Scala, C++ or Java.
Learning Resources:
Stage 2:
Next, build hands-on experience in cloud computing and distributed frameworks.
Understand the core concepts of cloud computing, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).
Gain practical experience with leading cloud platforms such as AWS, Azure, or GCP. Learn to provision resources, manage storage, and deploy applications in a cloud environment.
Explore distributed computing frameworks like Apache Hadoop, Apache Kafka, Apache Flink, and Apache Spark. Understand their architecture and how they enable the processing of large datasets across clusters.
Stage 3:
Then, focus on data warehouses and stream data processing. Develop your skills in batch and streaming data processing.
Understand the principles of data warehousing, including data modeling, schema design, and optimization techniques.
Develop skills in batch data processing using tools like Apache Hive or Amazon Redshift for efficient data analysis.
Explore real-time data processing with streaming analytics platforms such as Apache Kafka and Apache Flink. Learn to derive insights from data in motion.
Stage 4:
Afterward, you can dive into testing NoSQL databases and workflow orchestration tools.
Explore NoSQL databases like MongoDB or Cassandra and learn best practices for testing and ensuring data integrity.
Master workflow orchestration tools such as Apache Airflow and Prefect. Understand how to design, schedule, and monitor complex data workflows.
When you have completed the roadmap, continue upskilling and learning about the evolution in the data engineering field. For now, focus on the following key areas:
1. Upgrading from ETL to ELT - Traditional Extract, Transform, Load (ETL) processes are shifting towards Extract, Load, Transform (ELT). ELT's advantages lie in storing raw data and performing transformations closer to analysis, enabling flexibility and scalability.
2. Cloud Dominance - Cloud platforms like AWS, Azure, and GCP have become the go-to choices for data infrastructure, offering robust tools, managed services, and scalability.
3. The Rise of Real-time Data Processing - Streaming analytics platforms like Apache Kafka and Flink enable real-time insights and applications, driving faster decision-making.
4. Automation and Democratization _ Tools like Airflow and Prefect automate data pipelines, while platforms like dbt democratize data analysis by making it accessible to business users.
Top comments (3)
This is an excellent and concise overview that serves as an excellent reference source for breaking into data engineering and upskilling to remain relevant.
All clouds and MongoDB is trash
Airflow, ELT - top
why?