PySpark data engineering
PySpark Data Engineering
Large-scale data processing in Python on Databricks, Microsoft Fabric and AWS.
PySpark is the Python interface to Apache Spark, the engine behind most large-scale data processing. We write, test and tune PySpark pipelines for batch and streaming workloads on Databricks, Microsoft Fabric, AWS and other Spark platforms.
What we cover
- DataFrame and Spark SQL pipelines
- Structured Streaming
- Testing and data quality
- Performance tuning
- Delta Lake tables
- Packaging and CI/CD
Requirement-led advice · Documented handover
- Advice based on your requirements
- Works with your existing stack
- Documented, maintainable delivery
What Is PySpark?
PySpark lets you use Apache Spark from Python. Spark splits data and work across many machines, so the same code can process gigabytes on a laptop or terabytes on a cluster. Most PySpark code uses the DataFrame API and Spark SQL, which Spark optimises before running; Structured Streaming applies the same model to continuous data.
PySpark runs on Databricks, Microsoft Fabric notebooks, Azure Synapse, Amazon EMR, AWS Glue and self-managed clusters. Writing it well means thinking about how data is partitioned, avoiding unnecessary shuffles, preferring built-in functions to Python user-defined functions, and testing transformations like any other code.
When PySpark is a good fit
- Data is too large for a single machine or a single database query.
- Your team works in Python and wants one language for engineering and data science.
- You use Databricks, Fabric, Synapse, EMR or Glue.
- You need the same logic for batch and streaming data.
What We Build With PySpark
Typical PySpark work.
Batch ETL
Clean, join and aggregate large datasets on a schedule.
Streaming pipelines
Process event and IoT data continuously.
Lakehouse layers
Build bronze, silver and gold Delta tables.
Data quality checks
Validate rows and flag issues before publishing.
Feature engineering
Prepare training data for machine learning.
Code migration
Convert SQL, pandas or Scala jobs to PySpark.
Performance fixes
Speed up slow or failing Spark jobs.
Reusable libraries
Shared transformation functions with tests.
PySpark Services We Deliver
Each piece of work has a clear output, so you know what you will receive.
Pipeline design
Plan data layers, partitioning and job structure.
Deliverables: design document, data model
Pipeline development
Readable, modular PySpark code with configuration.
Deliverables: code, notebooks or packages
Testing
Unit and data tests with pytest and sample data.
Deliverables: test suite, CI integration
Performance tuning
Analyse Spark UI and plans, fix skew and shuffles.
Deliverables: tuning report, faster jobs
Migration
Port jobs from SQL, pandas, Scala or older Spark versions.
Deliverables: migrated code, comparison results
Packaging and release
Python packages, bundles and CI/CD for Spark jobs.
Deliverables: package, release pipeline
The PySpark Toolkit
Libraries and platforms we use.
APIs
- DataFrame API
- Spark SQL
- Structured Streaming
- pandas API on Spark
Storage formats
- Delta Lake
- Parquet
- Apache Iceberg
- JSON and CSV
Platforms
- Databricks
- Microsoft Fabric notebooks
- Amazon EMR and AWS Glue
- Azure Synapse Spark
Engineering practice
- pytest
- Adaptive query execution
- Spark UI analysis
- Git and CI/CD
Listing a feature here does not mean every project uses it. We recommend what fits your requirements, skills, licensing and running costs.
How PySpark Fits Your Stack
PySpark is the main engineering language on Databricks and in Microsoft Fabric notebooks. Jobs are often orchestrated by Azure Data Factory or Databricks jobs and feed Power BI.
Our wider Python work, from APIs to automation, is covered on the Python page. For dedicated engineers, see Hire Data Engineers.
Implementation Considerations
- Prefer built-in functions over Python UDFs; use pandas UDFs when custom logic is needed.
- Watch for data skew and large shuffles in joins.
- Partition and cluster tables for the queries that matter most.
- Keep notebooks thin and move logic into tested modules.
- Test with small, representative datasets in CI.
- Pin library and runtime versions to avoid surprise changes.
How We Work With PySpark
The same four steps we use across our technology work, adapted to your scope.
Discover
Goals, users, constraints and the systems PySpark must work with.
Select
Confirm PySpark is the right fit and agree the architecture and scope.
Build and integrate
Deliver in short iterations with testing, reviews and documentation.
Operate and improve
Monitor, support and improve the solution as your needs change.
PySpark Questions, Answered
What is PySpark?
The Python API for Apache Spark, used to process large datasets across a cluster.
Is PySpark slower than Scala Spark?
For DataFrame and SQL code, performance is very similar because Spark optimises both. Python UDFs can be slower, so we avoid them where possible.
Where can PySpark run?
On Databricks, Microsoft Fabric, Azure Synapse, Amazon EMR, AWS Glue and self-managed Spark clusters.
How do you test PySpark code?
With pytest, small sample datasets and data quality checks run in CI.
Can you speed up our slow Spark jobs?
Yes. We study the Spark UI and query plans, then fix skew, shuffles, partitioning and configuration.
Can PySpark handle streaming data?
Yes, with Structured Streaming, using the same DataFrame API as batch jobs.
Working With Large-Scale Data?
Tell us about your data, platform and performance needs, and we will suggest an approach.
Product names and logos belong to their respective owners. Mentioning a technology does not imply a partnership, certification or endorsement.