Data Engineering & Pipelines
Pipelines that are tested, observable, and boring — in the best sense.
Batch and streaming data pipelines engineered for correctness and predictable cost, with automated testing, lineage, and alerting so failures surface before your stakeholders notice.
The Unglamorous Work That Everything Depends On
Pipelines are invisible when they work and catastrophic when they do not. A silent failure that quietly stops loading yesterday's data does more damage than an outage, because nobody notices until a decision has already been made on stale numbers.
We build pipelines that fail loudly and recover cleanly. Every transformation is tested, every run is idempotent, and every table carries freshness checks. When something breaks — and eventually something always breaks — you hear about it from an alert, not from a confused executive.
We work in both batch and streaming, choosing based on how fresh the data genuinely needs to be. Real-time infrastructure carries real cost and complexity; a large share of what teams describe as real-time requirements is comfortably served by a fifteen-minute batch.
Service Inclusions
Tested Transformations
Schema tests, uniqueness and referential checks, and custom business-rule assertions running on every execution.
Idempotent by Design
Re-running any pipeline produces the same result. No duplicate rows, no manual cleanup after a retry.
Column-Level Lineage
Trace any figure in a dashboard back through every transformation to its source system.
Freshness Monitoring
Alerts on stale tables and volume anomalies, so silent failures surface within minutes rather than at month end.
Streaming Where It Earns It
Kafka, Kinesis, and Flink pipelines when latency genuinely matters — and honest advice when batch is sufficient.
Cost Observability
Query and compute cost tracked per pipeline, so warehouse spend stays visible instead of arriving as a surprise.
A Process Built for Clarity
No black boxes. No surprise invoices. Every project at Mornis Global follows a disciplined four-phase process designed to reduce risk and maximise value at every stage.
Source Assessment
Systems inventoried, extraction methods and constraints documented, volumes and change rates measured.
Modelling & Contracts
Target schema design and data contracts agreed with downstream consumers before build.
Ingestion Layer
Extraction and landing built with incremental loading, schema-drift handling, and retry logic.
Transformation Layer
dbt models with tests and documentation, delivered incrementally so value lands early.
Observability
Freshness checks, volume anomaly detection, lineage graph, and alert routing configured.
Handover
Runbooks, architecture documentation, and working sessions so your team can extend it confidently.
The Tech Stack
We select technologies based on performance, scalability, and long-term maintainability, not trends.
dbt
Specialized implementation of dbt in the Transformation space.
Airflow
Specialized implementation of Airflow in the Orchestration space.
Dagster
Specialized implementation of Dagster in the Orchestration space.
Kafka
Specialized implementation of Kafka in the Streaming space.
Snowflake
Specialized implementation of Snowflake in the Warehouse space.
Great Expectations
Specialized implementation of Great Expectations in the Data Quality space.
Real-World Impact
CloudScalers
The Challenge
“Event pipelines failed silently roughly twice a month. Finance discovered discrepancies during month-end close, by which point stale numbers had already reached the board.”
The Solution
We rebuilt ingestion as idempotent incremental loads, added dbt tests across every model, and configured freshness and volume alerting with clear ownership routing.
Key Performance Indicators
Common Inquiries
Everything you need to know about our specialized services.
Can You Trust Yesterday's Numbers?
Tell us where your data comes from and how it breaks. We will show you what tested, observable pipelines would change.