The Best Free Tools & Platforms to Practise Data Engineering Skills in 2025/26
Data engineering has rapidly become one of the most critical disciplines in technology. Every business, from financial services to healthcare to e-commerce, relies on robust data pipelines to move, transform, and store information efficiently. Without skilled data engineers, the modern data-driven economy would grind to a halt.
The challenge for job seekers? Employers don’t just want to see academic credentials. They want hands-on evidence that you can build and manage data workflows, integrate sources, optimise performance, and deploy solutions at scale.
Fortunately, you don’t need expensive software licences or premium courses to gain practical experience. A wealth of free tools and platforms allow you to practise and master the essential skills of a data engineer. In this vlog-style guide, we’ll cover the best free resources you can use in 2025 to build portfolio-ready projects and boost your job prospects.
Why Practising Data Engineering Skills Matters
The UK market for data engineers is growing fast. Employers want candidates who:
Know the tools: Spark, Hadoop, Airflow, Kafka, SQL, and cloud platforms.
Can build pipelines: Moving raw data from source to warehouse or lake.
Optimise workflows: Managing storage costs and processing times.
Understand data security: Ensuring governance, compliance, and privacy.
Show real projects: Hiring managers love GitHub repos with working examples.
Hands-on practice is the best way to develop these competencies. Luckily, you can do it all for free.
1. Apache Spark (Free & Open Source)
Apache Spark is the cornerstone of modern data engineering.
Key Features
Large-scale distributed data processing.
APIs in Python, Scala, R, and Java.
Support for streaming, machine learning, and SQL.
Why It’s Useful
Spark is widely used in production systems. Running Spark locally or on free cloud tiers gives you invaluable experience with big data.
2. Apache Hadoop Ecosystem
While Spark has overtaken Hadoop in popularity, Hadoop remains fundamental.
Key Features
Hadoop Distributed File System (HDFS).
MapReduce framework.
YARN resource manager.
Why It’s Useful
Practising Hadoop helps you understand the backbone of big data systems, a skill still requested in many UK roles.
3. Apache Kafka
Kafka is the standard for real-time data streaming.
Key Features
Distributed publish-subscribe messaging system.
High throughput and low latency.
Free to run locally via Docker or binaries.
Why It’s Useful
Streaming skills are highly marketable. Practising Kafka topics, producers, and consumers gives you an edge.
4. Apache Airflow
Airflow is the most popular orchestration platform for data pipelines.
Key Features
Define workflows as Directed Acyclic Graphs (DAGs).
Integrates with Spark, BigQuery, Redshift, and more.
Free to run locally or on Docker.
Why It’s Useful
Employers look for Airflow experience in nearly every data engineering job description.
5. dbt (Data Build Tool)
dbt is an open-source tool for data transformation.
Key Features
SQL-based modelling framework.
Free local development environment.
Integrates with Snowflake, BigQuery, Redshift, and Postgres.
Why It’s Useful
dbt is a modern standard for transforming raw data into analytics-ready models.
6. PostgreSQL
Postgres is one of the most powerful free relational databases.
Key Features
ACID-compliant relational database.
Advanced features like JSONB, CTEs, and window functions.
Strong community and free learning resources.
Why It’s Useful
SQL is at the heart of data engineering. Postgres is an excellent platform for practising queries, schema design, and optimisation.
7. MySQL
MySQL remains one of the most widely deployed databases.
Key Features
Open-source with a huge user base.
Great for learning SQL fundamentals.
Easy to install and run locally.
Why It’s Useful
While Postgres is more advanced, MySQL is a solid starting point and still common in legacy systems.
8. MongoDB Community Edition
For NoSQL practice, MongoDB is free and widely used.
Key Features
Document-oriented database.
Schema flexibility.
Free Atlas tier for cloud practice.
Why It’s Useful
Understanding NoSQL is vital for modern, unstructured data handling.
9. Google BigQuery Sandbox
BigQuery is Google’s serverless data warehouse.
Key Features
Free sandbox mode with no credit card required.
10 GB storage and 1 TB query processing per month free.
Ideal for SQL-based analytics practice.
Why It’s Useful
BigQuery is central to many data engineering roles in analytics-driven companies.
10. Snowflake Free Trial
Snowflake is one of the fastest-growing cloud data platforms.
Key Features
£300 worth of free credits (30-day trial).
Cloud-native, elastic data warehouse.
Strong community and free resources.
Why It’s Useful
Snowflake is widely used in UK enterprises. Even short-term free access gives valuable experience.
11. AWS Free Tier for Data Engineering
Amazon provides free access to key services:
S3: 5 GB free storage.
Redshift: Free trial for data warehousing.
Glue: ETL service with free tier.
Why It’s Useful
AWS dominates the UK market, and S3 + Glue skills are highly sought after.
12. Azure Data Services (Free Tier)
Microsoft offers free access to:
Azure Data Lake Storage.
Azure Synapse trial.
Data Factory: ETL service.
Why It’s Useful
Azure is the backbone of many corporate UK infrastructures.
13. Google Cloud Free Data Tools
Google’s free tier covers:
BigQuery Sandbox.
Cloud Storage free tier.
Pub/Sub free tier.
Why It’s Useful
Great for practising event streaming and analytics.
14. Kaggle
Kaggle isn’t just for data science—it’s also a fantastic platform for data engineering practice.
Key Features
Free hosted Jupyter notebooks.
Free GPU/TPU access.
Datasets for pipeline building.
Why It’s Useful
You can practise ETL pipelines and transformations on real data without worrying about infrastructure.
15. Google Colab
Colab is a free Jupyter notebook environment with cloud execution.
Key Features
Python-friendly, with libraries pre-installed.
Free GPU access.
Great for experimenting with Pandas and PySpark.
Why It’s Useful
Colab is perfect for practising data transformations and ML-adjacent workflows.
16. Apache NiFi
NiFi is an open-source tool for automating data flows.
Key Features
Drag-and-drop interface.
Support for streaming and batch processing.
Free to download and run.
Why It’s Useful
NiFi is excellent for practising integration between multiple data sources.
17. Talend Open Studio
Talend provides a free open-source edition of its ETL tool.
Key Features
Drag-and-drop interface for building pipelines.
Large set of connectors.
Free to download and use.
Why It’s Useful
Talend is still popular in many enterprises.
18. Pentaho Community Edition
Pentaho is another free ETL and data integration tool.
Key Features
Visual designer for workflows.
Free community edition.
Integration with Hadoop and Spark.
Why It’s Useful
Great for building end-to-end ETL projects.
19. dbt Cloud Free Tier
Beyond the local version, dbt Cloud offers a free developer account.
Key Features
Hosted environment with scheduling.
Free for individuals.
Supports modern warehouses.
Why It’s Useful
dbt Cloud is a great way to practise scheduling and deploying transformations.
20. Data Engineering Communities & Forums
Learning is easier when shared. Join:
Reddit (r/dataengineering).
LinkedIn groups.
DataTalks.Club community.
Slack & Discord channels.
Why It’s Useful
Communities help you troubleshoot, share projects, and find job leads.
How to Use These Tools Effectively
Start with SQL: Use Postgres or BigQuery Sandbox to practise queries.
Build ETL Pipelines: Combine dbt or Airflow with Postgres.
Try Streaming: Run Kafka locally or experiment with Pub/Sub.
Experiment in the Cloud: Use AWS, Azure, or GCP free tiers.
Work on Real Data: Use Kaggle datasets to simulate workflows.
Document Projects: Push to GitHub, blog on LinkedIn, and show recruiters.
Expand Gradually: Move from batch jobs to streaming and orchestration.
Final Thoughts
Data engineering is the engine room of modern analytics. Employers want more than theory—they want proof of practical skill. With the free tools outlined here—from Spark, Kafka, and Airflow to BigQuery, Snowflake, and dbt—you can build the same kind of workflows used in real companies, entirely for free.
Consistency is key. Practise weekly, work on small projects, and build a portfolio. By the time you apply for jobs, you’ll have tangible evidence of your skills that will impress UK employers.
So don’t wait—pick one tool, download it, and start building your first data pipeline today.
