The HCL Actian Data Platform (formerly Actian Avalanche) hybrid cloud data warehouse is a fully managed service that aims to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity – at a lower cost than alternative solutions. Avalanche has built-in self-service data integration that can be deployed on-premises as well as on multiple clouds, including AWS, Azure, and Google Cloud, enabling users to migrate or offload applications and data to…
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SAP Datasphere
Score 8.3 out of 10
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SAP Datasphere, the next generation of SAP Data Warehouse Cloud, is a comprehensive data service that enables data professionals to deliver seamless and scalable access to mission-critical business data. It provides a unified experience for data integration, data cataloging, semantic modeling, data warehousing, data federation, and data virtualization. SAP Datasphere enables users to distribute mission-critical business data — with business context and logic preserved — across the data…
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Pricing
HCL Actian Data Platform
SAP Datasphere
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
HCL Actian Data Platform
SAP Datasphere
Free Trial
Yes
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
Optional
No setup fee
Additional Details
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SAP Datasphere is available as a subscription or consumption-based model. The SAP Datasphere capacity unit (CU) offers an adaptable approach to pricing that enables any workload on any hyperscaler. The number of CUs required is determined by the unique workload, with the ability to tailor the combination of required services within SAP Datasphere utilizing a flexible tenant configuration. The services that contribute to CU consumption are the core application (compute and storage), data lake, BW bridge, data integration, and data catalog (crawling and storage).
We didn’t actually choose Actian, it arrived as part of an acquisition, and really served its purpose both when it was used by the smaller firm we acquired as well as afterwards when we were extracting data and folding the company into our own data and analytics culture. The …
Each of these listed software has its own unique strength and capacity that scales well. SAP Datasphere on its end up against them with more suitability for large establishments with complex data ecosystems with scalability support. Also, it avails a pay-as-you-go pricing for …
I am only going to talk about the visualizing and analytics part of SAP Datasphere because if I start listing what the whole SAP ecosystem provides, then it would not even be a comparison. In a summary, Google Analytics is high-level. Of course, the introduction of the new GA4 …
Scalability and effectiveness to handle large volumes of data while maintaining consistency with the applications. Excellent with data integrity by eliminating duplications. Magnificent with a collection of data from different sources to get trusted data for making decisions.
The Friendly interface that SAP offered was second to none. The Tracking and tracebility that was Integrated helped in configuring locations quite easily and while shifting of cargo it was never an issue. Distribution wise SKU segregation was never a problem and report …
Compared to the SAP Business Warehouse, the DWC can be operated with the SAC in a cloud environment on the Business Technology Platform. Thus, a single point of entry or single point of truth is given in the DWC, and reporting in the SAC is possible. The complete corporate …
It is very easy and quick to develop.it has good and excellent self-service. The use of state art data platform is very easy and simple. It has a high-performance and scalable data warehouse. It accelerates time to value real-time. It gives data a new level of consumption.
It is user-friendly and has self-service tools. It has built-in capabilities such as a data lake. It backed integration capabilities. It gives data on a new level of consumption. It is easy and very quick to develop. It has a high-performance and scalable data warehouse.
Both tools are fairly the same, but we mainly focused on SAP Data Warehouse Cloud since we had a lot of SAP services that were simple to integrate with each other.
VectorWise is suitable to be a departmental data mart database or an operational data store (ODS). It is not suitable for enterprise data warehouse database.
SAP is best suited for large global corporations. It is a very complex software and is difficult to come by individuals who have sufficient enough knowledge to master the product. Globally our company only has 1 person to support us in IT for SAP. It is great if you use a more complex web host such as SFCC as you can make connections to SAP for returns, inventory, price updates, etc.
The support community was not as robust as you would find in a Mulesoft or Informatica environment. Given time and growth, it’s possible it will blossom, but for now it is minimal.
Training is always a big thing for us, and the tool was not expansive enough for us to implement our own internal training program. There was some online training, and we acquired an expert when we brought on the new company, but some additional training tools would have helped the tool grown its user base internally.
Not a lot to set it apart from the competition. Most of the features are available with other more established tools, but for a small company that maybe grew too quickly and needs to get its arms around many different data sources, I can see the appeal. Not really geared for larger firms.
SAP Data Warehouse Cloud offers free trial for 90 days with free 128 GB of storage and 64 GB memory.
Availability of self-service data modeling and analytics on SAP Data Warehouse Cloud enables users to access and analyze data without getting support from the IT team.
Without zero coding while collecting, connecting, analyzing and modeling data, it saves us time and operational costs of partnering with external IT support experts.
As I said before, more training or greater visibility to training tools/options would be a plus. It’s easy to publish YouTube videos these days, I think they should make more of them.
Differentiation would help, there’s not a lot out there to drive you to buy the product if you are well informed in the market. If you know the market, you steer towards the large or trendy products. It’s a good product, but lost in the noise of the field I think.
Hitching the wagon to a major software brand (like Mule did to Salesforce) would help grow the user base, and thus increase the activity in the support community. More users also translates into product champions.
It is one of the best tools and a boon to Logistics teams across the globe. One tends to actually process warehousing data so smoothly and the way demonstration is made while in programs it makes it user friendly. The Inventory touch points that one identify is simply awesome and is best part.
I would greatly acknowledge the services of Sap Data [warehouse Cloud] because we were struggling before its arrival where we used to get manual data connections and this used to consume a lot of time but after its use, we now are able to connect data easily saving a lot of time and finances.
We didn’t actually choose Actian, it arrived as part of an acquisition, and really served its purpose both when it was used by the smaller firm we acquired as well as afterwards when we were extracting data and folding the company into our own data and analytics culture. The included hundreds of pre-built connectors gave us lots of options, but in the end, we were just too large of a company to rely on the product and needed a big-name player to address our wide-ranging needs. Powerful for its size, but not sized enough to address big businesses.
Each of these listed software has its own unique strength and capacity that scales well. SAP Datasphere on its end up against them with more suitability for large establishments with complex data ecosystems with scalability support. Also, it avails a pay-as-you-go pricing for users, and it is widely up for data quality, data governance, and data discovery.