The Ultimate Big Data Engineering Roadmap: A Guide to Master Data Engineering in 2024

Summary

The speaker, Kash, a Data Engineer at JP Morgan in the UK, shares his journey and insights on becoming a data engineer. He outlines essential learning paths, including programming languages like Python, Scala, or Java, and frameworks like Spark for big data processing. He also covers distributed systems, databases, and real-time data processing technologies. Kash emphasizes the importance of tools like Apache Airflow for workflow management, cloud services, and communication skills. His roadmap includes learning about data modeling, ETL pipelines, and system design, suggesting resources for each and highlighting the value of data analytics skills.

Takeaways

• 😀 The speaker, Kash, is a Data Engineer at JP Morgan in the UK and has recently moved there.
• 🎓 Kash has a background in a Triple Honours IT degree from Una and transitioned into the Data Engineering role after college.
• 🛠️ For aspiring Data Engineers, learning a programming language is essential, with Python, Scala, or Java being in high demand in the IT industry.
• 🔥 Data Engineers should focus on learning data processing frameworks, particularly Spark, which is widely used for big data environments.
• 💾 Knowledge of Hadoop ecosystem components like HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator) is crucial for data engineers.
• 📚 Understanding of storage systems, including relational databases like MySQL, PostgreSQL, and Oracle, as well as NoSQL databases like Cassandra, MongoDB, and others, is important.
• 🏢 Data Warehousing concepts, such as data modeling and ETL (Extract, Transform, Load) pipelines, are key areas of focus for Data Engineers.
• 🌐 With the shift towards cloud computing, familiarity with cloud services like AWS (Amazon Web Services), GCP (Google Cloud Platform), and Azure is increasingly important.
• 🔍 Real-time data processing technologies are gaining traction, with tools like Apache Kafka, Apache Flink, and Apache Storm being used for analytics and insights.
• 📈 Data Engineers should be adept at using workflow management tools like Airflow for orchestrating and managing data pipelines.
• 💬 Strong communication skills are vital for Data Engineers, as they need to understand and respond effectively to technical inquiries during interviews and in the workplace.

Q & A

Who is the speaker in the video and what is their profession?

The speaker in the video is named Kailash, and they are a Data Engineer working at JP Morgan in the UK.

What is the main topic of the video?

The main topic of the video is to provide a roadmap for becoming a Data Engineer, including the skills and technologies one should learn.

What programming languages are recommended for someone aspiring to be a Data Engineer?

The recommended programming languages for aspiring Data Engineers are Python, Scala, and Java, with a focus on Python due to its high demand in the IT industry.

What is the significance of Spark in the context of Data Engineering?

Spark is significant in Data Engineering as it is widely used for processing large volumes of data due to its efficiency and is a key technology that data engineers should learn.

What are some of the big data technologies and frameworks that a Data Engineer should be familiar with?

A Data Engineer should be familiar with technologies and frameworks such as Hadoop Distributed File System (HDFS), YARN, and resource managers like Mesos, as well as data processing frameworks like Spark.

What is the importance of learning about storage systems for a Data Engineer?

Understanding storage systems is crucial for a Data Engineer as it involves knowledge of databases, both relational and non-relational, which are essential for managing and processing data efficiently.

What is Data Warehousing and why is it important for Data Engineers?

Data Warehousing is the concept of collecting and managing large amounts of data in a way that facilitates easy access and analysis. It is important for Data Engineers to understand as it is a key component in big data engineering.

What is the role of Apache Airflow in Data Engineering projects?

Apache Airflow is used for orchestrating and managing workflows in Data Engineering projects. It helps in scheduling and monitoring the data pipelines, ensuring the tasks are executed in the correct order and dependencies are managed.

What are some of the cloud services that a Data Engineer should have knowledge of?

A Data Engineer should have knowledge of at least one cloud service such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, as cloud computing is increasingly becoming a standard in the industry.

Why is learning about real-time data processing important for a Data Engineer?

Learning about real-time data processing is important as it allows Data Engineers to process and analyze data as it is generated, enabling faster insights and decision-making, which is valuable in many industries.

What is the role of communication skills in the context of a Data Engineer's job?

Communication skills are essential for a Data Engineer to effectively understand and respond to the needs of the team and stakeholders. It involves not only speaking fluently but also expressing complex technical concepts in simple terms.