ELT and data warehouse automation

ZAP Data Hub automates data collection, data modeling, data integration, and the creation of a data warehouse and semantic layer. Data governance is built-in and everything is delivered via a flexible platform and standards.

How ZAP Data Hub works

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ZAP Product – in depth – Mobile View
Hybrid Data Collection Intuitive Data Modeling Automated Semantic Layer Automated Warehouse Automated Data Integration Your BI tool

Hybrid Data Collection

Smart data connectors for ERP, CRM, finance systems, and databases

Intuitive Data Modeling

Pre-built, optimized, analytical data models for reporting

Automated Semantic Layer

Logical caption-based metadata approach

Automated Warehouse

Built-in data type detection and profiling

Automated Data Integration

Automatic detection of relationships between data

Your BI tool

Report from ZAP Data Hub with BI tools including Tableau and Power BI

Hybrid data collection

ZAP Data Hub automates data collection from ERP, CRM, finance systems, databases, files and more. It delivers high-speed, stable data migration for even the largest and most complex data sets.
The ZAP Data Gateway creates a persistent, two-way connection across networks, firewalls, and realms to ZAP Data Hub from each data source.

  • Connect to cloud, on-premise, and hybrid data sources
  • Handle multiple cloud and on-premise systems
  • Manage multiple instances of the same type of ERP or CRM system
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Intuitive data modeling

ZAP Data Hub has pre-built analytical data models for BI reporting. Its metadata-driven approach pairs familiar business terminology with an easy-to-use graphical interface.

  • Intuitive, metadata-driven
  • Familiar business terminology, graphical interface, drag-and-drop
  • One screen for both data warehouse and semantic layer
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Automated data warehouse

ZAP Data Hub automates ELT processes and data warehouse builds via an automated, step-by-step series of user prompts. ZAP Data Hub delivers a two-tier warehouse structure (staging, and modern data warehouse) with self-generated data migration routines and in-warehouse ELT transformations. Other features include data type detection, profiling, automated and rapid data migration, and incremental refreshes.

Automated semantic layer

With a caption-based metadata approach, ZAP Data Hub’s automated semantic layer features cross-ledger querying, hierarchies, and aggregations and materialized calculations (allowing you to deploy logical measures into the model).
Users can interrogate the data warehouse and work with complex data sets using familiar terminology from the data source.

Automated data integration

ZAP Data Hub’s automated data integration detects relationships between data sets and recommends integrations – which are either approved or refused via a graphical interface – before being processed.
Other automated data integration features include per-table and per-column mapping, data profiling and recommendations, automatic merging of tables, multi-threaded and/or parallel migrations, and incremental loading and updating.

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Built-in data governance

ZAP Data Hub’s built-in administration controls allow users to audit and log all aspects of data governance and security, from password and access control to reconfiguring mismatched data definitions.
Enterprise-class controls set security permissions, regulate access and protect data at any level of granularity, while data governance is controlled at four separate levels: user, resource, warehouse data, and cell data. Data lineage is governed separately, with history tracking, auditing and monitoring also included.

Flexible platform architecture

The platforms and standards on which ZAP Data Hub is based assure both its reliability and its feature-rich capabilities.
ZAP Data Hub’s resource-based approach is reusable and highly flexible. Pipelines, scripts, security roles – and even data sources and data models – can all be treated as resources that can be moved, copied, pasted, exported and imported. And multi-lingual functionality allows users to automatically translate pipelines, fields, and values from a data source augmented with their own specific integrations.


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