DataHub Highlights
Data Discovery
Explore your entire data ecosystem, including dashboards, datasets, machine learning models and raw data, to gain a comprehensive understanding of your data landscape.
Data Governance & Management
Define and track clear ownership of your data. Manage personally identifiable information (PII), maintain column descriptions and centralize your documentation to efficiently comply with governance standards.
Data Quality Control
Improve data quality by testing to your expectations. Use data contracts to check for a standardized format between data providers and data consumers.
Data Lineage
The data lineage allows you to trace the data flow – from the dashboard to transformations and the origin. This transparency makes it possible to identify data problems, make their processing traceable and ensure compliance requirements.
APIs and SDKs
For programmatic control, comprehensive APIs and SDKs are available to flexibly integrate DataHub into your existing infrastructure.
Our professional services for your DataHub Project
Tailor DataHub precisely to business requirements
- Analysis of the data landscape and existing systems.
- Identification of use cases (e.g. data lineage, impact analysis, governance).
- Recommendation of an optimal DataHub architecture.
Provision of a functional DataHub instance on Kubernetes
- Provision of preconfigured deployments and automated installation scripts.
- Setting up connectors to relevant data sources (e.g. databases, BI tools, data lakes).
- Support with the adaptation of open source connectors or the development of new integrations.
- Configuration of security and user administration (e.g. LDAP/SSO).
Integrate data sources, metadata and lineage in DataHub
- Development of a central data model (e.g. custom schemas, entity modeling).
- Automation of metadata capture and lineage updates with ETL pipelines.
- Optimization of the display of data flows and lineage
Training and enablement for high user acceptance
- For administrators: Workshops on the operation, maintenance and scaling of DataHub.
- For users: training on metadata search, lineage analysis, dashboards.
- Provision of best practices for the use of DataHub.
Continuous optimization and expansion
- Analysis of the data landscape and existing systems.
- Identification of use cases (e.g. data lineage, impact analysis, governance).
- Carrying out a metadata audit and recommending standards for improvement.
- Recommendation of an optimal DataHub architecture.
Support packages for fast problem solving
Media library
Video
TDWI: The role of the data catalog in an effective data ecosystem
A data catalog is the foundation of successful data management. Watch the video to find out how it supports the development and operation of data products in classic architectures and modern data mesh structures.
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Video
TDWI: Manage data effectively. Getting started with data catalogs
Data catalogs bundle data centrally, improve data quality and simplify access. The following points are covered in the video:
- Connection of data sources and automation of processes
- Improving collaboration and data quality through metadata management
- Steps towards the introduction of a data catalog
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Video
Aspects of the introduction of data catalogs in municipal utilities
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Whitepaper
Six key criteria for an optimal data catalog
Choosing a suitable data catalog is crucial for the effective management and use of company data. A data catalog provides a comprehensive overview of the data landscape, facilitates access to various data sources and improves data governance.
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