Data Engineering
Streaming and batch pipeline design, data warehouse modelling, and real-time analytics for teams that need reliable, observable data infrastructure.
Overview
Data Pipelines That Actually Work in Production
EulerHive builds data infrastructure that your analysts and ML teams can trust. From Kafka streaming pipelines and dbt transformations to Snowflake warehouse modelling and Airflow orchestration, we design systems that are observable, testable, and built to last.
Capabilities
What We Deliver
Core capabilities within Data Engineering.
Streaming Pipelines
Real-time data pipelines with Apache Kafka and Kafka Streams. Event-driven architectures, exactly-once semantics, and consumer group management at scale.
Batch Processing
Large-scale batch processing with Apache Spark and PySpark. Optimised for cost and performance on AWS EMR, Databricks, or self-managed clusters.
Data Warehouse Modelling
Dimensional modelling and dbt transformations for Snowflake and BigQuery. Layered architectures (staging, intermediate, marts) with full lineage and documentation.
Pipeline Orchestration
Workflow orchestration with Apache Airflow or Prefect. DAG design, dependency management, retry logic, and SLA monitoring.
Data Quality Frameworks
Automated data quality checks with Great Expectations or dbt tests. Schema validation, freshness checks, and anomaly detection integrated into your pipelines.
Real-Time Analytics
Sub-second analytics with ClickHouse, Apache Druid, or Redpanda. Designed for high-cardinality event data and interactive dashboard queries.
Stack
Technologies We Use
FAQ
Common Questions
Answers to what clients typically ask before engaging.
We have messy, unreliable data pipelines — where do you start?
We start with a data audit: mapping your current sources, transformations, and consumers. From there we prioritise the highest-impact fixes — usually data quality checks and observability — before rebuilding or replacing pipelines.
Do you work with both Snowflake and BigQuery?
Yes. We have deep experience with both. We help clients choose based on their existing cloud footprint, query patterns, and cost profile, and we build dbt projects that are largely portable between the two.
Can you help us move from batch to real-time processing?
Yes. We design incremental migration paths — typically starting with a Kafka layer in front of your existing batch jobs, then progressively replacing batch steps with streaming consumers as confidence grows.
More Services
Other Practice Areas
One integrated engineering team across four disciplines.
Product Engineering
Full-stack web and mobile development for startups building their first product and enterprises modernising legacy systems.
Explore Product EngineeringAI & Intelligent Systems
End-to-end ML pipeline development, LLM integration, and intelligent automation for businesses ready to move beyond demos.
Explore AI & Intelligent SystemsPlatform & DevOps
Infrastructure-as-code, Kubernetes, GitOps, and full-stack observability for engineering teams that need to move fast without breaking things.
Explore Platform & DevOps