Programming Language
A data engineer writes code to move, transform and serve data, and to build the platforms that do it. SQL (covered in SQL Fundamentals) is the language of transformation; a general-purpose language is needed for everything around it: ingestion scripts, orchestration, APIs, custom connectors, tests and infrastructure.
You do not need all of these. The practical path in 2026 is Python first, solid SQL alongside it, and then one compiled language when your work requires it.
How the languages divide the work
| Language | Main role in data engineering | Data tools written in it |
|---|---|---|
| Python | Glue, orchestration, transformation, ML handoff: the default | Airflow, Dagster, Prefect, dbt Core, pandas, PySpark API |
| Java | The engine room of big data on the JVM | Kafka, Flink, Trino, Iceberg, Hadoop, Cassandra, Elasticsearch |
| Scala | Spark internals and Spark-heavy teams | Spark core, parts of Kafka |
| Go | Cloud-native infrastructure and network services | Kubernetes, Docker, Terraform, Prometheus, Grafana Loki, etcd |
| Rust | New high-performance engines and Python tooling | Polars, DataFusion, delta-rs, Vector, uv, ruff, dbt Fusion engine |
Two trends shape the picture: Python is the interface to almost every engine (PySpark, Polars, DuckDB, Flink’s PyFlink), and the engines underneath are increasingly written in Rust or C++ and connected through Apache Arrow, so Python users get native speed without leaving Python.
Choosing what to learn next
- You build pipelines and models: deepen Python and SQL. That covers most data engineering jobs.
- You work on Kafka, Flink or JVM services: learn Java. Reading Java stack traces and configs is useful for anyone running these systems.
- You maintain a large Scala Spark codebase: learn Scala; for new Spark projects, PySpark and Spark SQL are now the common choice.
- You build platform tooling, operators or CLIs: learn Go.
- You build engines, connectors or performance-critical libraries: learn Rust.
Learning Resources
- Stack Overflow Developer Survey: yearly data on language usage
- Fundamentals of Data Engineering, Joe Reis and Matt Housley (O’Reilly): chapter on the data engineering toolkit
- Apache Arrow: the columnar format that lets these languages share data without copying