Matillion Data Integration
Visual pipelines with business logic your team can follow.
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When Matillion fits
We use Matillion for visual data integration and transformation workflows. Cloud Data Consulting configures the environment, builds and reconciles pipelines, and prepares your team to operate them. Our Jumpstart package specifically deploys AWS-hosted Matillion ETL.
A useful starting point: You need visual orchestration and transformation, or want to replace existing integration jobs with a workflow the team can inspect and maintain.
What Cloud Data implements
Environment and access
Configure the agreed Matillion product and hosting model, source credentials, Snowflake connections and environment separation.
Pipelines and orchestration
Build ingestion and transformation jobs, dependencies and schedules, including SQL or Python where the workload calls for it.
Migration and reconciliation
Translate existing jobs, compare outputs, tune incremental processing and agree the cutover sequence.
Operation and enablement
Set up monitoring, deployment practices and documentation, with training and ongoing support scoped around your operating model.
Decisions to make together
- Product and hosting model
- Matillion ETL and Data Productivity Cloud have different deployment and operating models. Confirm the edition before applying an infrastructure package or migration plan.
- Visual design still needs engineering
- Reusable components help, but source rules, incremental logic and failure recovery still need review and testing.
- Pipeline and warehouse costs
- Evaluate licensing, runtime infrastructure and Snowflake processing together. The right approach depends on data volume and the jobs being replaced.
In practice
Up to 10× faster development
We've seen ETL development move up to 10× faster with Matillion's visual approach. Results depend on pipeline complexity, the starting workflow and how much existing work we can reuse.
Automotus: changing the ingestion approach
As integration costs grew, Cloud Data helped Automotus move ingestion to AWS-hosted Matillion ETL and orchestrate dbt, with incremental processing in Snowflake.
Read the Automotus case study →What does your next pipeline need to do?
Start with a complimentary discovery conversation and initial fit review.
Assessments, architecture, proofs of concept, implementation and training are scoped separately.
Schedule a discovery call