DataOps
Reliable data products, delivered continuously, on any cloud
At a glance
DataOps brings DevOps discipline to the data lifecycle: pipelines as code, tests in every run, governed and monitored delivery. NGT builds and runs the data platform as platform engineering, so your data engineers ship data products faster.
Not another data-engineering team: we build and run the platform your data teams deliver on.
Tests in every pipeline; access, lineage and audit come with the platform.
Six maturity dimensions, a scorecard, a ranked backlog and one pilot tied to a business pain.
One governed platform, from source to AI
Data lands once, is tested at every hop and is served governed to reports, ML and agents.
Ingest onceBatch and streaming connectors land every source raw and versioned.- Tested at every hopContracts and data tests gate bronze → silver → gold; a failure stops the run.
Served to BI, ML and agentsWarehouse, feature store and vector index read the same governed data.- Governed from day oneCatalog, lineage, access and keys come with the platform.
Point at a component to light its links; select it for what it does here and the practice that makes it right on AWS.
Problems we solve
Five data problems customers bring us, each with the architecture we use. Select one.
Errors found by the business, not the pipeline
Pipeline changes go straight to production and the data is never tested on its way. Broken reports are found by the people who read them.
- Pull requests run code tests and a contract check
- A promotion gate from Dev and QA to Prod
- Failing runs stop, quarantine rows and alert the owner
Problems caught in the pipeline, with test evidence from every run.
Point at a component to light its links; select it for what it does here and the practice that makes it right.
What NGT delivers in DataOps
Six service areas from NGT's DataOps catalogue, on AWS, Azure or Google Cloud, with Databricks, Snowflake, BigQuery or Synapse.
It is platform engineering for the data platform: we design, automate, secure and operate it, so data engineers deliver data products faster and more reliably.
Explore DataOps
6 pages, each a few minutes to read.
Frequently asked questions
What customers ask first.
Data engineering builds the data products: pipelines, models and datasets. DataOps builds and runs the platform under them, and the CI/CD, tests, access and monitoring around them.
With a fixed-scope Data Maturity Assessment across six dimensions. You get a scorecard, a ranked backlog, quick wins and one pilot use case tied to a business pain point.
AWS, Azure and Google Cloud, with Databricks, Snowflake, BigQuery or Synapse. The design is cloud-agnostic; we start from what you run today.
No. Your data engineers keep the data products; NGT builds and runs the platform and automation. NGT engineers can also join your platform team.
ML models, RAG and agents need current, tested, access-controlled data. DataOps serves it from the governed platform, so AI pilots reach production without a separate copy.
Targets agreed at the start, such as infrastructure as code, automated data deployments, pipeline success, availability and time to restore, reported from the pilot onwards.
