Azure Synapse Analytics is described as the former Azure SQL Data Warehouse, evolved, and as a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives users the freedom to query data using either serverless or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
$4,700
per month 5000 Synapse Commit Units (SCUs)
SAP Business Data Cloud
Score 8.4 out of 10
N/A
SAP Business Data Cloud is a fully managed SaaS solution that unifies and governs all SAP data and seamlessly connects with third-party data—giving line-of-business leaders context to make even more impactful decisions.
It's well suited for large, fastly growing, and frequently changing data warehouses (e.g., in startups). It's also suited for companies that want a single, relatively easy-to-use, centralized cloud service for all their data needs. Larger, more structured organizations could still benefit from this service by using Synapse Dedicated SQL Pools, knowing that costs will be much higher than other solutions. I think this product is not suited for smaller, simpler workloads (where an Azure SQL Database and a Data Factory could be enough) or very large scenarios, where it may be better to build custom infrastructure.
Ease of data modelling and SAC dashboards make it easier for super users to present their data. SAP Business Data Cloud is very strong tool which is helping the organization to arrange meaningful and important data at once single platform. This data can be used in Data bricks for any AI needs and in SAC for analytical reporting.
Quick to return data. Queries in a SQL data warehouse architecture tend to return data much more quickly than a OLTP setup. Especially with columnar indexes.
Ability to manage extremely large SQL tables. Our databases contain billions of records. This would be unwieldy without a proper SQL datawarehouse
Backup and replication. Because we're already using SQL, moving the data to a datawarehouse makes it easier to manage as our users are already familiar with SQL.
SAP Business Data Cloud help in arranging data at one single place reducing cost of maintenance for multiple platforms and governance.
Once data is arranged , it can be modelled as needed using Datasphere and in data bricks for AI needs
Data fabric provided in SAP Business Data Cloud helps in reducing man efforts to design models needed for PNL reporting as data fabric helps in quickly designing the solution with great accuracy.
With Azure, it's always the same issue, too many moving parts doing similar things with no specialisation. ADF, Fabric Data Factory and Synapse pipeline serve the same purpose. Same goes for Fabric Warehouse and Synapse SQL pools.
Could do better with serverless workloads considering the competition from databricks and its own fabric warehouse
Synapse pipelines is a replica of Azure Data Factory with no tight integration with Synapse and to a surprise, with missing features from ADF. Integration of warehouse can be improved with in environment ETl tools
In the new analytics world, BDC has been a game changer for SAP Analytics. Extending the SAP data for the usage in Databricks, snow flake, GCP has opened new doors for Analytics . Shift from traditional data warehousing to Business Data fabric adapting to the change in the analytics world is the need of the hour and Sap has managed to pulled it off with BDC
The data warehouse portion is very much like old style on-prem SQL server, so most SQL skills one has mastered carry over easily. Azure Data Factory has an easy drag and drop system which allows quick building of pipelines with minimal coding. The Spark portion is the only really complex portion, but if there's an in-house python expert, then the Spark portion is also quiet useable.
1. Easy to understand and use for developers 2. Detailed training, including the SAP Partner get-certified academy and developer documentation, to upskill and learn more about SAP BDC 3. A bundled offering of SAP Datasphere, SAC, and Databricks also helps. BDC supporting Snowflakes is another game-changer for SAP BDC in the long run.
Microsoft does its best to support Synapse. More and more articles are being added to the documentation, providing more useful information on best utilizing its features. The examples provided work well for basic knowledge, but more complex examples should be added to further assist in discovering the vast abilities that the system has.
support team is generally responsive and knowledgeable, and most issues are addressed within acceptable timelines. Documentation and standard guidance are helpful for common scenarios.
One of the best training session I attended and they covered most of the topics and answered all our questions. participants joined from different regions, infact they all had a different questions and it was different thoughts from all of then and helped to learn better. Though I was on travel, I could able yo attend the session.
I have done implementation of models in traditional bw and Using BDC. The integration of BDC with S4 hana for creating sap data products is seamless and reduces lot of implementation effort. The intelligent app feature is BDC also eases the implementation effort. If i have to compare the previous world with new BDC, implementation effort is largely saved
In comparing Azure Synapse to the Google BigQuery - the biggest highlight that I'd like to bring forward is Azure Synapse SQL leverages a scale-out architecture in order to distribute computational processing of data across multiple nodes whereas Google BigQuery only takes into account computation and storage.
With a S4 backend a lot of core functionality is made simpler - authorization, data types, currency conversion. In particular if the front end choice is SAP Analytics Cloud. The lack of a good connection from Power BI to the datasphere application (instead of the underlying HANA cloud) is a major drawback in that scenario.
Licensing fees is replaced with Azure subscription fee. No big saving there
More visibility into the Azure usage and cost
It can be used a hot storage and old data can be archived to data lake. Real time data integration is possible via external tables and Microsoft Power BI
