Layer2 Cloud Connector vs. SAP Data Intelligence

We had a scenario where our clients have thousands of external party vendors/committees who regularly sync SharePoint data. Managing thousands of external parties can never be possible without using a Layer2 cloud connector. We managed the local network sharing and SharePoint integration by using the Layer2 connector. Layer2 is convenient with a large amount of data as the per-user license is high.

It is good at bringing together data from diverse sources, including databases and cloud services, streamlining the integration process seamlessly. Environments where maintaining a clear understanding of metadata is important, such as those with stringent compliance requirements, it is effective. But ,it might be considered less appropriate for small-scale operations with limited data needs. In such cases, the extensive capabilities of the platform could be perceived as overkill, and simpler tools might be more cost-effective and easier to manage. A more simpler tool might be more suitable for teams that are less tech-savvy, ensuring smoother adoption and utilization.

Incentivized

• Layer2 provides best connectivity with SharePoint
• It syncs local network shared files to the SharePoint
• Provides details file history

• It integrates well with our current ecosystem of SAP products, like HANA.
• It provides end-to-end machine learning operations, with tools for the complete model life cycle.
• It has a simple user interface for novice users, with complex tools also available for power users.
• It builds on SAP Data Hub, providing all the ETL functions of that tool with additional machine learning functionality.
• It can run in the cloud, no on-premise software management needed.
• Many programming languages are supported, it provides a sandbox environment for the user to develop in whichever style they prefer.
• SAP is very actively developing and improving it.

Incentivized

• Per User cost is expensive
• A lot of pre-requisites
• UI needs to be more user-friendly

• Data transfer speed tends to be slow when there is poor internet connection since SAP Data Intelligence don’t synchronize data while offline. However, this is not vendor fault, that’s why we have implemented robust wireless technology internet connection in our organization.

Incentivized

Allow collaborations among various personas
with insights as ratings and comments on the
datasets Reuse knowledges on the datasets for new users Next-Gen Data Management and Artificial Intelligence

Incentivized

I think the troubleshooting process might be streamlined with improved error recording and tracing. A lot of information about issues and how to fix them is hidden away in the Kubernetes pods themselves. I'm not sure whether SAP Data Intelligence can fix this problem it may be connected to Kubernetes's design, in which case fixing it could need modifications inside Kubernetes itself.

Incentivized

Initially we struggle to get help from SAP but then dedicated Dev angel was assigned to us and that simplify the overall support scenario. There is still room of improvement in documentation around SAP Data intelligence. We struggle a lot to initially understand the feature and required help around performance improvement area,

Incentivized

We had also used Microsoft One Drive as a syncing tool but it requires Office365 for managing and syncing data across thousands of vendors which was not practically possible to manage users on Office 365. Layer2 resolved this issue by having 3 user licenses and they are enough to periodically sync all external parties' data with our SharePoint online.

One of the reasons to pick SAP Data Intelligence is the speed and security it provides, in addition to the excellent support it provides. It is also compatible with all popular databases, which is another reason to choose it.

Incentivized

• Layer2 resolves the time management issue
• Resolved data syncing issues across different external vendors
• LOE optimisation with using Layer2 connector.

• Automation of data management slashed tasks by over 60% in most departments for the first 8 months.
• Metadata catalogs have enabled us to categorize data from disjointed sources in one place.
• It runs multiple ML models which enhances flexibility when managing data.

Incentivized