When your data outgrows traditional databases, you need infrastructure that scales. We design and build data processing systems that handle terabytes reliably.

What We Offer

Batch Processing Pipelines — Hadoop and Spark jobs for large-scale data transformation, aggregation, and analysis. From raw ingestion to polished datasets ready for analytics.

Stream Processing — Real-time data processing with Kafka Streams and Spark Streaming. React to events as they happen rather than waiting for batch windows.

Data Lake Architecture — Design and implementation of data lake infrastructure on AWS, including S3 storage strategies, EMR cluster management, and cost optimisation.

ETL Development — Extract, transform, and load pipelines that reliably move data between systems. Schema evolution, data quality checks, and error handling built in.

Technologies

Processing Frameworks: Apache Spark, Apache Hadoop, Apache Flink

Streaming: Apache Kafka, Kafka Streams, Kafka Connect

Storage: HDFS, AWS S3, Delta Lake, Parquet, Avro

Orchestration: Apache Airflow, AWS Step Functions

Cloud: AWS EMR, AWS Glue, AWS Athena

Our Approach

Big data projects fail when they focus on technology before understanding the data. We start with your actual data challenges—volume, velocity, variety—and choose tools that fit, not the other way around.

Discuss Your Project

If you have a challenge in this area, we'd be happy to discuss how we can help.

Get in Touch