Skip to main content

About data engineering

Data engineering

Model, integrate, transform, and consolidate data into structures that are suitable for building analytics solutions.

About data engineering

Use the information in this section to design and support high-performing, efficient, and reliable data pipelines. Learn how to model, integrate, transform, and consolidate data into structures that are suitable for building analytics solutions.

Data governance

Data governance is a set of principles and practices that ensure high quality through the complete lifecycle of your data and is a must for scalable digitalization in all industries.

Learn about the Cognite Data Fusion (CDF) tools and features that help make sure your data conforms to the user and organizational expectations.

Learn more

Data modeling

Data modeling makes it easier for developers, data architects, business analysts, and other stakeholders to view and understand relationships between data objects.

A data model organizes data objects and standardizes the properties of real-world entities and how they relate to one another. Data models are the core of an ontology, a knowledge graph, or an industry standard and are crucial in building solutions like data science models, mobile apps, and web apps.

Learn more

Integrate data

To analyze and contextualize your data in CDF, you need to establish efficient data integration pipelines between your existing data infrastructure and CDF.

Learn more

Managed staged data

You can stream or batch-extract data into the CDF staging area, called CDF RAW.

Learn more

Transform data

Data transformation is the process of changing your data set from one state into another and is a core part of a data integration workflow.

CDF ships with a built-in data transformation tool, CDF Transformations, and provides integrations to many other data transformation technologies. Which tool to use depends on your transformation requirements and your technology preferences.

Learn more

Contextualize data

The interactive contextualization tools in CDF let you combine machine learning, a powerful rules engine, and domain expertise to map resources from different source systems to each other in the CDF data model.

This way of connecting information allows you to build applications where you, for example, can click a component in a 3D model to see all the connected time series data or ask for all the pressure readings along a flow line.

Learn more

Build and deploy

Learn about the features you can use to build and deploy your CDF solutions.

Learn more