Data Engineering
Streaming and batch pipeline design, data warehouse modelling, and real-time analytics for teams that need reliable, observable data infrastructure.
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.
What We Deliver
Core engineering capabilities within Data Engineering, built to enterprise reliability standards.
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.
Architectural Deep Dives & Tradeoffs
Core engineering decisions, technical tradeoffs, and production standards we apply across Data Engineering.
Batch vs. Streaming Pipelines: Architectural Tradeoffs
Streaming introduces distributed state management, checkpointing, and out-of-order event handling complexity. For many analytical workloads, a hybrid or micro-batch approach provides the optimal balance of data freshness, cost, and maintainability.
When to Introduce Apache Kafka
Kafka decouples producers from downstream analytical and operational consumers. With schema registries (Avro/Protobuf), it prevents upstream schema breaking changes from crashing downstream reporting and machine learning pipelines.
Modern Warehouse Modeling with dbt (Staging, Intermediate, Marts)
We implement strict layered DAGs: raw ingestion staging layers, business logic normalization in intermediate layers, and performant star-schema marts for BI tools and downstream reverse ETL.
Technologies We Use
Battle-tested frameworks, databases, and infrastructure tools powering Data Engineering.
Related Case Studies
See how our Data Engineering practice delivered production software for verified clients.
GharSelect: PropTech Discovery & Data Platform
10-stage regulatory data ingestion and normalization pipeline transforming raw Gujarat RERA filings into structured MongoDB datasets.
Engineering Playbooks & Guides
First-hand engineering articles relevant to Data Engineering.
Common Questions
Answers to what engineering leads and founders typically ask before engaging.
How do you fix messy or unreliable data pipelines?
We conduct a pipeline audit: mapping data lineage, identifying upstream drift, and deploying automated dbt and Great Expectations tests before refactoring ingestion layers.
Do you work with Snowflake, BigQuery, and Databricks?
Yes. We build vendor-portable dbt transformation models and optimize warehouse query performance and compute spend across Snowflake, BigQuery, and Databricks.
Can you help us migrate from batch jobs to streaming?
Yes. We implement event-driven architectures with Apache Kafka or Redpanda, transitioning consumers incrementally without downtime.
Other Practice Areas
One integrated engineering team across four software disciplines.
Product Engineering
Full-stack web and mobile development for startups building their first product and enterprises modernising legacy systems.
AI & Intelligent Systems
End-to-end ML pipeline development, LLM integration, and intelligent automation for businesses ready to move beyond demos.
Platform & DevOps
Infrastructure-as-code, Kubernetes, GitOps, and full-stack observability for engineering teams that need to move fast without breaking things.
