Oracle TimesTen In-Memory Database (TimesTen) delivers real time application performance by changing the assumptions around where data resides at runtime. By managing data in memory, and optimizing data structures and access algorithms, database operations execute achieve gains in responsiveness and throughput. With TimesTen Scaleout, a shared nothing scale-out architecture based on the existing in-memory technology, TimesTen allows databases to scale across hosts, reach hundreds of terabytes in…
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SAP HANA Cloud
Score 8.9 out of 10
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SAP HANA is an application that uses in-memory database technology to process very large amounts of real-time data from relational databases, both SAP and non-SAP, in a very short time. The in-memory computing engine allows HANA to process data stored in RAM as opposed to reading it from a disk which means that the data can be accessed in real time by the applications using HANA. The product is sold both as an appliance and as a cloud-based software solution.
TimesTen is well suited for applications using smaller data or smaller data stores and where transaction response times are not as business critical. TimesTen is good for applications already accessing Oracle and need to cache data for quick read/write operations. TimesTen is not appropriate for large data dependent applications or applications requiring fast response times. In these cases, using Oracle database or Exadata is better
It is well organized. One can use it for the company's portfolio management. Various tasks can be done for managerial purposes. One can track the material from start to end product: for example, raw material, packing material & consumable material to formulated bulk and formulated drug product. This can help to manage spending as well as finding costing of the product.
Real-time reporting and analytics on data: because of its in-memory architecture, it is perfect for businesses that need to make quick decisions based on current information.
Managing workload with complex data: it can handle a vast range of data types, including relational, documental, geospatial, graph, vector, and time series data.
Developing and deploying intelligent data applications: it provides various tools for such applications and can be used for machine learning and artificial intelligence to automate tasks, gain insights from data, and make predictions.
Requires higher processing power, otherwise it won't fly. How ever computing costs are lower. Incase you are migrating to cloud please do not select the highest config available in that series . Upgrading it later against a reserved instance can cost you dearly with a series change
Lack of clarity on licensing is one major challenge
Unless S/4 with additional features are enabled mere migration HANA DB is not a rewarding journey. Power is in S/4
At this moment we are not focusing on SAP, however would love to in the future. This is primarily because of our limited ability to generate more revenue to fund for SAP partnerships and products. Our initial tryst with SAP Partneredge open ecosystem didn't go as planned and we have shelved that for now. Hope we can revive in the future
SAP HANA Cloud requires significant expertise on technical side to admin and manage it.It surely is lot of improvement over previous versions of SAPThe modern, role-based SAP Fiori interface has enhanced the user experience for applications like S/4HANA, though the complexity of the underlying database remains. For the average business user, the usability of SAP solutions running on the HANA database has seen a major transformation, largely thanks to the Fiori user interface.
One specific example of how the support for SAP HANA Cloud impacted us is in our efforts to troubleshoot and resolve technical issues. Whenever we encountered an issue or had a question, the support team was quick to respond and provided us with clear and actionable guidance. This helped us avoid downtime and keep our analytics operations running smoothly.
Professional GIS people are some of the most risk-averse there are, and it's difficult to get them to move to HANA in one step. Start with small projects building to 80% use of HANA spatial over time.
Sybase does not have an in-memory database until version 15 so TimesTen was ideal for caching data. TimesTen has reliable replication and backing up mechanisms. Oracle takes longer to set up and use for most applications where as TimesTen is a smaller DBMS that is quick and easy to set up and use. TimesTen can connect to Oracle for caching data so using Oracle as a backend makes sense
I have deep knowledge of other disk based DBMSs. They are venerable technology, but the attempts to extend them to current architectures belie the fact they are built on 40 year old technology. There are some good columnar in-memory databases but they lack the completeness of capability present in the HANA platform.
TimesTen has had a positive impact from a developer's perspective because implementing TimesTen is quick and easy. The benefits of TimesTen can be seen almost instantly. For instance, the application start up time is faster, the data is easy to maintain and the performance is fast for TimesTen clients.
TimesTen has had a positive impact for the business because it can be made accessible to users via a GUI. This gives users transparency to the data at any time.
The negative impact is that once the TimesTen database has grown too large, the application should move to using Oracle database or else it suffers from performance degradation and stability issues.
