What are the best programming languages in Data Engineering?


Python is one of the most popular programming tools used in Data Science, but also well known when it comes to Data Engineering.

But why and for what projects Python is used by Data Engineers?

First, this is easy to learn. This is a great programming tool if you are a non-technical

There are many uses of what you can make with Python and this list popular will give you an overview of what you can make:

  • Python to code small-scale ETL (Extract - Transform - Load) pipelines: moving data from one place to another - an essential component of every data systems.
  • Python is Data Engineers easiest way to create ETL pipelines by just extending classes of AIrflow’s DAG and Operator objects.
  • Python for API interaction
  • Python for automation
  • Python for Business Intelligence work

Operating Systems

These are the operating systems that make the pipelines tick.

This permit to navigate around different configurations, access control methods.

A data engineer is expected to know the ins and outs of infrastructure components.


SQL Databases - Relational databases

SQL databases are used at the end of the pipeline when the pipeline is close to the end product.

SQL is useful only when all the previous data engineering work has been coded.

This is not a tool to create a whole data pipeline because you’ll be extremely limited and you might create incomprehensible data queries.

However, SQL can be integrated into Big Data frameworks which will allow Data Engineers to be more productive.

These are programming tools such as:

  • MySQL
  • Microsoft SQL Server
  • PostgreSQL
  • Oracle Live SQL

NoSQL Databases - Non-relational databases

What is the difference between NoSQL and SQL databases?

NoSQL Databases have been created to answer the limitations of SQL. In fact, SQL databases having more than two decades of existence have to face the dawn of the big data…

These are programming tools such as:

  • MongoDB
  • Cassandra
  • Redis
  • Google Bigtable
  • Couchbase.


BigData Tools / Warehousing

Apache Spark

Apache Spark is an open source project that has been built and is maintained by a community of developers.

You want to use it to build reliable data pipelines with a massive amount of data.

The main benefit of Apache Spark is simple: it is FAST. At least 10x times faster than Hadoop MapReduce.

Before Spark, you would have covered wide workloads with separate distributed systems with all the necessary tasks in the production of data analysis pipelines - this is something you can do all-in-one with Spark which makes it highly accessible.

Furnished with great built-in libraries, Spark possessed additional libraries for real-time processing.

Today, Spark has the largest open source community in big data in terms of contributors and organizations.

Even great companies use this programming language at a massive scale. Some of their names are Netflix, Yahoo or eBay.



Kafka is a great tool if you want greatly simplifies working with data streams.

However, remember that Kafka can get complex at scale.


Hadoop Environment

Hadoop Environment is composed of several programming languages:

MapReduce: Processing/Computation layer
Writing distributed applications - efficient processing of large amounts of data (multi-terabyte data-sets).
- Large clusters (thousands of nodes)
- Commodity hardware in a reliable, fault-tolerant manner
To use MapReduce, you will have to run it on Hadoop which is an Apache open-source framework.

HDFS (Hadoop Distributed File Systems): the Storage layer
HDFS is designed to be deployed on low-cost hadware.
This is High throughput access to application data and suitable for applications having large datasets.

This list is not exhaustive but this is a good overview of the programming languages used in Data Engineering. We can also list these ones:

  • Java
  • Hive

What technologies do you use? Are you familiar with some of them? Let us know!