What Tools Should Data Engineers Know In 2024 - 100 Days Of Data Engineering

Summary

The video script discusses the multitude of tools and skills necessary for a successful career as a data engineer. It emphasizes the importance of understanding programming languages like SQL and Python, working with Linux, and mastering version control with Git. The speaker also highlights the significance of working with databases, cloud data platforms, and ETL/data pipelines, as well as the evolving nature of data engineering tools. The video serves as a guide for those looking to break into the field, stressing the value of a solid foundation in both tools and best practices for data management and processing.

Takeaways

• 🛠️ The landscape of data engineering tools is vast and constantly evolving, requiring adaptability and continuous learning.
• 🔧 Core programming languages and technologies like SQL, Python, and Linux are fundamental to a data engineer's skill set.
• 📚 Understanding the basics of object-oriented programming and writing efficient functions is essential for effective data engineering.
• 🖥️ Familiarity with version control systems like Git is crucial for managing code and collaborating with teams.
• 🔐 Knowledge of secure file transfer protocols (SFTP) and encryption tools (PGP) is necessary for data security and compliance.
• 💾 Working with databases, both traditional RDBMS and NoSQL, is a key responsibility of data engineers for data extraction and manipulation.
• 🌐 Cloud data platforms and warehouses like Snowflake, Databricks, and BigQuery are becoming increasingly important in modern data engineering.
• 🔄 Data orchestration and pipeline tools such as Airflow and Azure Data Factory help automate and manage data workflows.
• 🔧 A basic understanding of containerization (Docker) and orchestration (Kubernetes) can be beneficial, even if managed by a devops team.
• 🚀 The ability to choose the right tool for the job, whether it's a data warehouse, ETL, or data pipeline solution, is a valuable skill for data engineers.
• 🎯 Focusing on building a solid foundation in data engineering principles and tools can lead to a successful and adaptable career in the field.

Q & A

What are some of the core programming languages and technologies a data engineer should be familiar with?

A data engineer should have a strong understanding of SQL, Python, and Linux. They should also be comfortable working with Bash scripts and have a basic knowledge of networking.

How have the tools used in data engineering evolved over time?

Data engineering tools have changed significantly over the years. Initially, engineers had to manually manage solutions like Hadoop and Spark by setting up their own infrastructure. Nowadays, cloud-based services like Databricks, Athena, and others have simplified the process.

What is the importance of version control in data engineering?

Version control is crucial for managing code changes, collaborating with other engineers, and maintaining a record of the development process. Familiarity with tools like Git is essential for any data engineer.

What are some of the basic technical tools and skills that a data engineer should possess?

Basic technical skills for a data engineer include understanding SFTP for secure file transfers, using PGP for encryption, and having a foundational knowledge of object-oriented programming and writing functions in Python.

How do different databases play a role in data engineering?

Data engineers often interact with various databases, both traditional relational databases like PostgreSQL and MySQL, as well as NoSQL databases like MongoDB. Understanding how to pull data from these sources and manipulate them is a key part of the job.

What is the role of cloud data platforms and warehouses in data engineering?

Cloud data platforms and warehouses like Snowflake, Databricks, and Big Query are used to build data lakes or data warehouses. They offer different architectures and features compared to traditional databases, and a data engineer must understand these differences to effectively use them.

Why is it important for a data engineer to understand both tools and the underlying concepts?

Understanding both tools and concepts allows a data engineer to make informed decisions about which tools to use for specific tasks, optimize their work, and troubleshoot issues effectively. It also helps them adapt to new technologies and stay current in the field.

What are some orchestration and ETL tools that a data engineer might use?

Orchestration and ETL tools like Airflow, SSIS, Azure Data Factory, and Informatica are used to automate data workflows, extract data from various sources, transform it into the desired format, and load it into target systems.

How does a data engineer decide which cloud platform to learn?

A data engineer should consider the popularity and prevalence of cloud platforms in the job market, as well as the specific needs of the companies they want to work for. AWS is often a safe bet due to its widespread use, while Azure may be preferred by large enterprises.

What additional tools might a data engineer need to know for containerization and infrastructure management?

For containerization, a data engineer might need to understand Docker and Kubernetes. For infrastructure management, tools like Terraform can be useful. However, these are often managed by devops teams, so data engineers might not need to be as deeply knowledgeable in these areas.

What advice would you give to someone looking to break into the field of data engineering?

Focus on building a strong foundation with the core tools and technologies, and don't feel rushed to learn everything at once. It's more important to understand the concepts and how the tools fit into the bigger picture. As you gain experience, you'll naturally learn more advanced tools and techniques.