Quit Wasting Time! Prior to using the tools, learn these data engineering fundamentals.

Any building's foundation is its strongest point. Everything else will fall apart if there isn't a solid foundation. The same is true for data engineering; you must comprehend the foundations of the most advanced tools before utilizing them.

It's tempting to go right into products like Airflow, dbt, BigQuery, and Spark in today's fast-paced digital industry. However, you'll find it difficult to fix problems, improve performance, or adjust to new technologies if you don't grasp the fundamental ideas underlying them.

Here's a friendly reminder: Invest in your foundations first if you want to advance in data engineering. Let's dissect it. 🚀

The Appropriate Sequence for Data Engineer Education
You will become a better problem solver and more flexible engineer if you learn the basics before you learn the tools. This is how you go about it:

Prior to Airflow, Orchestration
Learn about workflow orchestration, including how jobs are planned, dependencies are managed, and errors are handled, before you dig into Apache Airflow. Regardless of the tool you use, concepts like task scheduling and directed acyclic graphs (DAGs) are ubiquitous.

Data Modeling Before dbt DBT (data build tool) is a powerful tool for data transformation, but you won't be able to create effective models if you don't first grasp data modeling, including the differences between star and snowflake schemas, normalization and denormalization, and fact and dimension tables.

Data Warehousing Prior to BigQuery BigQuery is a robust data warehouse, but are you aware of the distinctions between a database and a data warehouse? Before utilizing cloud-based solutions, educate yourself about OLAP vs. OLTP, columnar storage, partitioning, and indexing.

Including Prior to Airbyte
Data integration concepts, such as batch versus real-time processing, API-based versus file-based ingestion, and ETL versus ELT, should be understood before utilizing tools like Airbyte, which make data ingestion simpler.

Version Control Prior to Git
Learn the fundamentals of version control before using Git, including how code repositories operate, branching techniques, and the significance of commit history.

Operating Systems Prior to Linux
Data engineers must be familiar with Linux, but before learning how to use it, learn the basics of the operating system, including memory allocation, file systems, and process management.

Containers Prior to Docker
Learn about containerization before adopting Docker, including its benefits for data engineering operations, its differences from virtual machines, and its necessity.

Basics of Networking Before VPC
Understand the fundamentals of networking, including IP addresses, DNS, subnets, firewalls, and data transfer across networks, before delving into Virtual Private Cloud (VPC) configurations.

Code for Infrastructure (IaC) Prior to Terraform
Terraform simplifies infrastructure management, but you risk misusing it if you don't grasp the fundamentals of Infrastructure as Code (IaC), such as declarative versus imperative configuration and provisioning versus configuration management.

Prior to Spark, Distributed Computing
Learn the basics of distributed computing, including parallel processing, data shuffling, and partitioning, before utilizing Apache Spark, which is excellent for processing enormous amounts of data.

Processing Streams Prior to Kafka
Learn about stream processing, including how real-time data flows, event-driven architectures, and the distinction between batch and streaming processing, before beginning to work with Kafka.

Why Essentials Are More Important Than Tools