Essential Data Engineering Roadmap: Skills, Tools, and Technologies to Master

Data engineering has become one of the fastest‑growing careers as data volumes and demands increase.

According to the 2021 Stack Overflow survey, data engineers rank among the top five highest‑paid professionals, behind SRE and DevOps engineers.

If you want to become a data engineer, here are key resources to keep.

💻 Fundamentals

Many IT and R&D roles require deep knowledge of Linux. Beneficial fundamentals include:

Basic terminal usage

Shell scripting

Git and GitHub

Computer networking basics

👩‍💻 Programming Basics

General programming knowledge is essential. The specific language is less important than understanding paradigms and best practices.

Python

Java

Go

PHP

🧪 Testing

Unit testing

Functional testing

📊 Database Fundamentals

A solid grasp of SQL, data normalization, and ACID transactions is required.

SQL basics

OLTP vs OLAP

Horizontal and vertical scaling

Relational databases

MySQL / MariaDB

PostgreSQL

Non‑relational databases

Document stores: MongoDB, Elasticsearch

Wide‑column: Apache Cassandra, Apache HBase

Graph: Neo4j

Key/Value: Redis, Memcached

🏠 Data Warehouses

Snowflake

PrestoDB

Apache Hive

📦 Object Storage

Cloud storage services

⚡ Data Processing

Apache Pig

Apache Arrow

Hybrid processing

Apache Spark

Apache Beam

Streaming

Materialize – streaming database for real‑time analytics

Apache Kafka

Apache Storm

📩 Message Queue Processing

RabbitMQ

Apache ActiveMQ

RocketMQ

💽 Cluster Computing

Apache Hadoop and HDFS

MapReduce

⏲ Workflow Scheduling

Apache Airflow

Apache Oozie

📺 Monitoring Data Pipelines

Prometheus

Datadog

👨‍💻 Infrastructure as Code

Containers: Docker

Orchestration: Kubernetes, Docker Swarm

Provisioning: Terraform

Automation: Ansible

🛫 CI/CD

GitHub Actions

Jenkins

Conclusion

This article is inspired by the open‑source data‑engineer roadmap repository.