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

The Engineer Guy 2.0

30 May 202417:55

Summary

TLDRThe 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.

Outlines

00:00

😀 Introduction to the Data Engineering Journey

The speaker, Kash, introduces himself as a Data Engineer at JP Morgan in the UK and discusses his journey of landing the job in the UK. He provides a link in the description and a button for viewers to watch his previous video detailing how he got the job. Kash then outlines the roadmap he followed to become a Data Engineer, including the skills and technologies he learned, and promises to share a comprehensive guide for aspiring Data Engineers in the video.

05:00

👨‍🏫 Essential Skills for Aspiring Data Engineers

Kash delves into the essential skills required for a Data Engineer role, emphasizing the importance of learning programming languages like Python, Scala, or Java. He mentions the significance of understanding data processing frameworks, big data environments, and technologies used in the field such as Spark, Hadoop Distributed File System (HDFS), YARN, and resource managers. Kash advises viewers to follow a particular roadmap that includes learning these technologies to enhance their employability as Data Engineers.

10:01

🔍 Exploring Data Storage Systems and Big Data Engineering

The speaker discusses the necessity of understanding various data storage systems and databases, including relational and non-relational databases like MySQL, MongoDB, Oracle, and others. He highlights the importance of data warehousing and technologies like Hive, which is part of the big data ecosystem. Kash also mentions cloud computing and the need to be familiar with cloud services, especially Amazon Web Services (AWS), as they are increasingly in demand for Data Engineers.

15:01

🚀 Advanced Topics for Data Engineers

Kash introduces advanced topics such as real-time data processing and the technologies involved, including Apache Kafka, Apache Flink, and others. He explains the importance of these technologies in providing insights and value to businesses through immediate data analysis. The speaker also touches on the concept of data pipelines and the orchestration of workflows using tools like Airflow, which is crucial for managing data workflows and dependencies.

📘 Broadening Knowledge in Data Engineering

The speaker emphasizes the importance of having a broad knowledge base in Data Engineering, including understanding data modeling, ETL pipelines, system design, and the use of tools like Apache Airflow for workflow management. He also mentions the need to learn about cloud computing and the significance of being familiar with at least one cloud service provider. Kash encourages viewers to focus on learning these technologies to increase their chances of landing a job in the field.

🌐 Additional Tools and Soft Skills for Data Engineers

Kash suggests that aspiring Data Engineers should also learn about additional tools and technologies such as Apache Airflow for data orchestration, as well as soft skills like communication. He explains that good communication skills are essential for understanding technical discussions and explaining complex concepts in simple terms. The speaker also advises on the importance of continuous learning and staying updated with the latest tools and technologies in the field.

Mindmap

Keywords

💡Data Engineer

A Data Engineer is a professional who specializes in the infrastructure that supports the collection, storage, and management of large volumes of data. In the video, the speaker discusses their journey and the skills required to become a Data Engineer, emphasizing the importance of this role in handling big data environments.

💡Data Processing Frameworks

Data Processing Frameworks refer to the tools and technologies used to process and analyze large datasets. The video mentions frameworks like Apache Spark, which is used for big data processing, and highlights the necessity for a Data Engineer to understand these frameworks to manipulate and derive insights from data.

💡Hadoop Ecosystem

The Hadoop Ecosystem encompasses a set of open-source tools and utilities designed to handle distributed storage and processing of big data. The script discusses components of the Hadoop ecosystem such as HDFS and YARN, which are critical for a Data Engineer to manage distributed computing and storage tasks.

💡Python Programming

Python is a high-level programming language widely used for general-purpose programming, and it is essential for Data Engineers due to its simplicity and the powerful libraries it offers for data manipulation. The video script suggests learning Python as one of the key steps in preparing for a Data Engineering role.

💡Data Modeling

Data Modeling is the process of creating a representation of data structures and their relationships in a database. The script touches on the importance of understanding data modeling for a Data Engineer, as it is crucial for designing and optimizing the way data is stored and retrieved.

💡ETL Pipeline

ETL stands for Extract, Transform, and Load, which is a process used to aggregate data from different sources, transform it into a suitable format, and load it into a database or a data warehouse. The video script explains the concept of ETL pipelines as a fundamental aspect of a Data Engineer's responsibilities.

💡Apache Airflow

Apache Airflow is an open-source tool used for orchestrating complex data workflows. The script mentions Airflow as a crucial tool for managing ETL pipelines, emphasizing its role in the workflow management for Data Engineers.

💡Cloud Computing

Cloud Computing refers to the delivery of computing services, including storage, processing power, and databases, over the internet. The video discusses the growing importance of cloud computing in the field of Data Engineering, as more companies are moving their infrastructure to cloud platforms.

💡Data Warehousing

Data Warehousing is the process of collecting, storing, and managing large amounts of data in a centralized repository. The script highlights the significance of understanding data warehousing concepts for a Data Engineer, especially in the context of big data and analytics.

💡Real-time Data Processing

Real-time Data Processing involves processing data as it is generated or received, without any significant delay. The video script discusses the importance of real-time data processing technologies like Apache Kafka and Apache Storm, which are used for immediate data analysis and decision-making.

💡Data Analytics

Data Analytics is the process of examining data sets to draw conclusions about the information they contain. The speaker in the video suggests that having a basic understanding of data analytics can be beneficial for a Data Engineer, as it complements their technical skills with analytical thinking.

Highlights

Introduction of the speaker, Kash, a Data Engineer at JP Morgan in the UK.

The speaker has recently moved to the UK and shares his job acquisition journey in a previous video.

The video discusses a roadmap for aspiring Data Engineers.

Emphasis on learning programming languages, specifically Python, Scala, or Java for Data Engineering roles.

Importance of understanding Data Processing Frameworks and Big Data Environments in the IT industry.

Discussion on Apache Spark as a prevalent tool for Data Processing.

Explanation of HDFS (Hadoop Distributed File System) as part of the Big Data ecosystem.

Introduction to YARN as a resource manager in the distributed computing environment.

The necessity of knowledge in storage systems and databases for Data Engineers.

Mention of relational databases like MySQL, PostgreSQL, and Oracle.

Highlighting the need to understand NoSQL databases such as Cassandra, MongoDB, and others.

Introduction of Data Warehousing and the concept of Data Lakes.

The significance of cloud computing and platforms like Snowflake in modern data engineering.

Real-time Data Processing as an emerging field in data engineering.

Apache Kafka and other technologies for real-time data streaming and processing.

The role of ETL (Extract, Transform, Load) pipelines in data engineering workflows.

Airflow as an open-source tool for orchestrating and managing data pipelines.

Importance of understanding Cloud Services such as AWS, GCP, and Azure for Data Engineers.

The necessity of overall knowledge in Data Modeling and System Design for Data Engineers.

Introduction of Scope as a technology for data import and export between different systems.

Emphasis on the importance of communication skills in the IT industry and interviews.

The video concludes with advice on continuous learning and preparation for technical interviews.