Modern data platforms, especially data lakes, are complex. Many projects fail due to wild west practices of “first dump and then figure out”. Storage costs trending downward has only exacerbated the problem. Established businesses that are subject to regulation usually rely upon best practices honed over years of managing data, but these are not available easily in these data platforms. Many sources of data are designed for legacy applications in ways that are not easily compatible with today’s technologies. The explosion in digitization has meant that traditional data ingestion approaches are not cutting it.

The need of the hour is an efficient and scalable mechanism for ingesting and managing massive data with key emphasis on operational ease. Cadenz Diffen uses configurations to drive normal patterns of data ingestion and management and extends out-of-the-box functionality using custom scripts that are modular and plug-and-play. Cadenz Diffen relies upon best practices from data warehousing to get usable business data.

Salient Solution Features
Ease of use

Data ops enabled via dashboard and alerts
Supports schema changes and data drift
Built using Spark to run on modern platforms
Decoupled compute from storage for horizontal scalability

Fast and Flexible

Handles data at scale
Technical metadata quality checks
Standard processing patterns for dealing with all paradigms of extraction
Guarantees valid snapshots of source systems as of defined periodicity
Stands up a usable data lake in as little as 6 weeks for all downstream purposes.

Governance and Security

Exception logging and alerting
Encrypted, access on data
Supports business closure processing for critical fields
Handling rejects and default substitution
Native support for standard column transformations, and extensible

Cadenz Diffen: Governed Ingestion for Data LakesTheDataTeam

Cadenz Diffen: Governed Ingestion for Data Lakes

TheDataTeam

Cadenz Diffen: Governed Ingestion for Data Lakes

TheDataTeam

Cadenz Diffen: Governed Ingestion for Data Lakes
TheDataTeam