Proven with Industry Leaders
Data engineering services, and what you own afterwards
Every engagement ends with something your organization owns and can operate. This is the work, and what stays with you.
| Work | What we build | What you own afterwards |
|---|---|---|
| Pipelines | Batch and real-time ingestion, including change data capture from core systems | Pipelines with tests, monitoring, and runbooks your team can operate |
| Streaming | Kafka and NiFi, on Cloudera or open source | Topics, schemas, and replication you can run and extend |
| Platform | Cloudera, Databricks, or Snowflake, on premises or on AWS, Azure, or Google Cloud | A configured platform with its security model and documentation |
| Lakehouse | Apache Iceberg tables, orchestration with Airflow, and transformation with dbt | Models and workflows in your own repository |
| Migrations | Legacy CDH or HDP to current Cloudera releases, and on premises to cloud | A staged cutover with a rollback plan and the controls rebuilt |
| Data quality | Checks at ingestion and transformation on the data that matters most | Quality rules and alerts with named owners |
Platforms we build on
Platform-aware, not platform-locked. We also build pipelines that work alongside Informatica, including in Cloudera environments.
Cloudera
Our lead data partner. Private Cloud Base, Public Cloud, and hybrid estates, with governance built on SDX.
Cloudera practiceDatabricks and Snowflake
When your data platform is one of these, we build on it. The platform recommendation follows your constraints, not our partnerships.
AWS, Azure, and Google Cloud
Cloud data services and the networking, identity, and storage underneath them, alongside on-premises systems.
Open source
Kafka, NiFi, Apache Iceberg, Airflow, and dbt, either within Cloudera or run on their own.
Platform migrations without reopening audit findings
Many regulated estates still run legacy CDH or HDP, past the end of standard support, or are moving workloads from on premises to cloud. The risk in a migration is less the data than the controls around it.
How we run a migration
- An inventory of jobs, tables, and dependencies before anything moves
- Staged cutover by workload, with the old path kept until each one is proven
- Ranger, Kerberos, and lineage controls rebuilt so the move does not reopen audit findings
- A rollback plan for every stage
Data quality and lineage built into the pipeline
Quality checks run where data enters and where it is transformed, not in a report after the fact. Lineage is captured as the pipeline runs, so a number in a report can be traced back to its source.
The rules, owners, and evidence around that data are the work of data governance consulting.
Data engineering, answered plainly.
Data engineering consulting
What does a data engineering consultant do?
A data engineering consultant designs and builds the systems that move data from where it is created to where analytics and AI use it: ingestion and change data capture, streaming, the lakehouse or warehouse, orchestration, and data quality checks. The result should be a platform your own team can operate, not one that depends on the consultant.
Data engineering consulting or an in-house team?
Most organizations need both. Your team owns the roadmap and the daily work. A partner adds depth where it is rare or slow to hire, such as migrations, streaming, and platform security, and carries the run work when you would rather not staff it.
Which platforms do you build on?
Cloudera, Databricks, and Snowflake for the data platform, on premises or on AWS, Azure, or Google Cloud. Kafka, NiFi, Apache Iceberg, Airflow, and dbt, within Cloudera or as open source.
Do you handle data platform migrations?
Yes. We move legacy CDH and HDP estates to current Cloudera releases, and move workloads from on premises to cloud, in stages with a rollback plan and with the security and lineage controls rebuilt.
Do you work with Informatica?
Yes. We build pipelines that work alongside Informatica, including in Cloudera environments.
Engagements
What does data engineering consulting cost?
It depends on scope. Engagements start with an AI Readiness Assessment, a fixed-scope review that takes 2 to 4 weeks. Build work is sized after the assessment, against facts rather than assumptions.
Who runs the pipelines after go-live?
Your team, us, or both. We hand over runbooks and monitoring either way, and we can carry support and managed services or provide an outsourced data engineering team if you would rather not staff it.
Part of our data and AI consulting practice. Practitioner writing on the data layer under AI is collected under Private AI & Data Architecture.