After covering the high-level overview and key features, let’s look at recent innovation with SAP Datasphere:

1. New replication flows

SAP has introduced a new cloud-based replication technique called the “new replication flow”. It is designed to streamline data integration processes by removing the need for extra on-premises components. This means, no installation and maintenance activity are needed for DP-Server/DP-Agent. To establish connections with remote sources, the new replication flow functionality leverages the data intelligence embedded environment and the data intelligence connectors.

Furthermore, the new replication flow has been seamlessly integrated into the existing data builder, and it comes with built-in monitoring features.

The new replication flow is ideal for replicating multiple data assets from an only source to a single target in a straightforward and efficient manner, without the need for complex logic. It can load data from your source systems using both batch and delta functionalities, all with a single flow. Please refer to supported connection type available for ‘’New Replication Flow’’ (Integrating Data via Connections | SAP Help Portal).

2. Catalog

Data catalog enables self-service discovery of data and analytics assets, glossaries, terms, and key performance indicators. SAP Datasphere is also integrated with top third-party catalog vendors to drive the business data fabric.

Technically, data catalog is categorized into three areas:

• Data discovery and consumption
  Data discovery and consumption help to locate relevant data and provide insights about the data. This enables self-service for business stakeholders, which propels data democratization, and it supports data governance programs.
• Data enrichment and classification
  Data enrichment means adding more information to existing data to make it better. This can include things like a business glossary, tags, and key performance indicators (KPIs). Data enrichment and classification supports proactive data governance and collaboration by organizing data assets, clearly labeling data, and improving the data discovery process. It also helps to identify and manage sensitive data to support governance and compliance requirements. Teams align with the organization’s overall goals through KPIs, which can measure project success or employee performance. This keeps teams moving in the same direction and helps them in achieving the organization’s objectives.
• Admin and tracking
  Administration enables the data catalog to establish connections to data sources, whereas tracking (monitoring) provides valuable feedback and insights regarding the performance and interactions with the connected sources. The combined use of administration and monitoring features supports the following:
• Connect to and manage a wide range of source systems.
• Continuously monitor source activity and resource usage.
• Identify and troubleshoot any connection or extraction issues.
• Keep track of user activity and resource consumption levels.
• Making assets discoverable to end-users.
• Providing control and governance over the publication process.
• Allowing users to publish KPIs and terms.
• Encouraging data sharing and collaboration among users.

3. Analytic Model

The Analytic Model is a key component of SAP Datasphere that enables multi-dimensional and semantically rich analytical modeling. It plays a critical role in providing faster, more efficient, and easy-to-use solutions for complex business questions.

The analytical model represents the start schema with a fact dimension model and provides a variety of benefits:

• Advanced key figure modeling allows for complex key figure calculation, aggregation, and restriction.
• Developers can curate which measures, attributes, and associated dimensions to expose to users.
• User input via prompts in SAP Analytics Cloud can be used for subsequent calculations, filters, and time-dependency.
• Rich previewing possibility: The analytic model editor tightly integrates with the data analyzer of SAP Analytics Cloud, providing modelers with a rich previewing capability. Users can inspect the results of their modeling efforts in real-time, allowing them to slice, dice, pivot, filter, use hierarchies and access many other features. This functionality helps business users to gain a deeper understanding of the data and how it will be presented for consumption.
• Time-dependency support: Support the critical time-dependency feature, allowing users to move back and forth in time, even as lines of business, structures, and organizations continue to evolve. This capability is essential for ensuring the accuracy and relevance of analytics as business environments change over time.
• Dependency management and transport: The analytic model is seamlessly integrated into the SAP Datasphere repository, which enables it to benefit from features like impact and lineage analysis, transport management, and change management. These capabilities streamline the management of complex analytic projects by keeping track of all modeling artifacts throughout their lifecycle.

The analytic model is set to be the primary analytical consumption artifact across all channels in SAP Datasphere. While analytical datasets in the data layer and business layer perspectives will still be used for a while, the analytic model already offers superior features for most modeling scenarios.