Databricks offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service provides a platform for data pipelines, data lakes, and data platforms.
$0.07
Per DBU
IBM Cognos Analytics
Score 7.4 out of 10
N/A
IBM Cognos is a full-featured business intelligence suite by IBM, designed for larger deployments. It comprises Query Studio, Reporting Studio, Analysis Studio and Event Studio, and Cognos Administration along with tools for Microsoft Office integration, full-text search, and dashboards.
$10
per month per user
Teradata Vantage
Score 8.1 out of 10
N/A
Teradata Vantage is presented as a modern analytics cloud platform that unifies everything—data lakes, data warehouses, analytics, and new data sources and types. Supports hybrid multi-cloud environments and priced for flexibility, Vantage delivers unlimited intelligence to build the future of business.
Users can deploy Vantage on public clouds (such as AWS, Azure, and GCP), hybrid multi-cloud environments, on-premises with Teradata IntelliFlex, or on commodity hardware with VMware.
Because our Datawarehouse born with Teradata and we are happy with the vendor support & product benefits
Verified User
Executive
Chose Teradata Vantage
Performance and capacilities in order to manage high volumes of data, multiples joins and complex queries
Verified User
Consultant
Chose Teradata Vantage
The Teradata is leader and reference in the market. We had a project to migrate from Teradata on premise to Teradata Cloud, bring advantages por example: we can inprovement our worklouds with low impacts for our infra solution e bring better experience to work in the cloud tools …
Oracle Exadata is an excellent product. Performs mass data processing with similar capability compared to Teradata. Some features Exadata has lack for Teradata Vantage, such as archive generation, consistent reading and writing (simultaneously), RMAN backing up online …
Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.
Well suited: Financial reporting - It can handle complex, pixel perfect, muti-page reports with scheduled delivery to stakeholders (like sales report by region on quarterly periodicity) Operational dashboard across departments - It can combine multiple data sources (ERP, CRM, excels etc) with filters, and embedded AI insights Less appropriate: Live dashboards - As stated earlier as well, IBM Cognos Analytics doesn't suit well for live dashboards or event driven data. For ex: live web traffic data or IOT device data, etc Data science - Although IBM Cognos Analytics is great tool for data exploration but it should not be used as a substitute for Python or R, which has edge over advanced modelling and stats based workflows like predictive modelling or clustering
Teradata Vantage is well suited for large scale ETL pipelines like the ones we developed for anti money laundering risk matrices. It handles heavy joins, aggregations, and transformations on transactional data efficiently. We generate alert variables, adjust for inflation, and monitor establishments monthly with it, all integrated with Python and Control-M for a centralised automation across the company. For less appropriate, I would say that heavy resource demands might slow down experimentation for iterative work.
IBM Cognos Analytics enables customer data segmentation, which is essential for marketing, improving and streamlining purchasing behavior and preferences. This helps companies create more targeted and effective marketing campaigns.
Our clients Through data analysis, we can identify and observe trends in the behavior of other clients, allowing us to anticipate needs and adjust strategies to avoid consequences.
Teradata is an excellent option but only for a massive amount of data warehousing or analysis. If your data is not that big then it could be a misfit for your company and cost you a lot. The cost associated is quite extensive as compared to some other alternative RDBMS systems available in the market.
Migration of data from Teradata to some other RDBMS systems is quite painful as the transition is not that smooth and you need to follow many steps and even if one of them fails. You need to start from the beginning almost.
Last but not least the UI is pretty outdated and needs a revamp. Though it is simple, it needs to be presented in a much better way and more advanced options need to bee presented on the front page itself.
For an existing solution, renewing licenses does provide a good return on investment. Additionally, while rolling out scorecards and dashboards with little adhoc capabilities, to end users, cognos is very easily scalable. It also allows to create a solution that has a mix of OLAP and relational data-sources, which is a limitation with other tools. Synchronizing with existing security setup is easy too.
Teradata is a mature RDBMS system that expands its functionality towards the current cloud capabilities like object storage and flexible compute scale.
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.
in terms of graph generation and interaction it could improve their UI and UX
We have a strong user base (3500 users) that are highly utilizing this tool. Basic users are able to consume content within the applied security model. We have a set of advanced users that really push the limits of Cognos with Report and Query Studio. These users have created a lot of personal content and stored it in 'My Reports'. Users enjoy this flexibility.
Teradata Vantage allows us to create a scalable infrastructure to support our strategic initiatives. The dedicated compute power ensures reliable performance with isolated workloads and dedicated resources, optimizing workflows for faster, more efficient data transfers. The compute clusters support ETL processes and OSF’s developers and data science team with the flexibility to create self-service analytics, to spin up/down at any time, driving better performance and minimizing costs.
Reports can typically be viewed through any browser that can access the server, so the availability is ultimately up to what the company utilizing it is comfortable with allowing, though report development tends to be more picky about browsers and settings as mentioned above. It also has an optional iPad app and general mobile browsing support, but dashboards lack the mobile compatibility. What keeps it from getting a higher score is the desktop tools that are vital to the development process. The compatibility with only Windows when the server has a wide range of compatibility can be a real sore point for a company that outfits its employees exclusively with Mac or Linux machines. Of course, if they are planning on outsourcing the development anyways, it's a rather moot point
Overall no major complaints but it doesn't handle DMR (Dimensionally Modeled for Relational) very well. DMR modelling is a capability that IBM Cognos Framework Manager provides allowing you to specify dimensional information for relational metadata and allows for OLAP-style queries. However, the capability is not very efficient and, for example, if I'm using only 2 columns on a 20-column model, the software is not smart enough to exclude 18 columns and the query side gets progressively larger and larger until it's effectively unusable.
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
Why is their web application not working as fast as you think it should? They never know, and it is always a a bunch of shots in the dark to find out. Trying to download software from them is like trying to find a book at the library before computers were invented.
We have meetings at the beginning with the technical team to explain our requirements to them and they were really putting in a lot of effort to come up with a solution which will address all our needs. They implemented the software and also trained a few of our resources on the same too. We can get in touch with them now as well whenever we run into a roadblock but it's very less now.
Onsite training provided by IBM Cognos was effective and as expected. They did not perform training with our data which was a bit difficult for our end-users.
The online courses they offer are thorough and presented in such a way that someone who isn't already familiar with the general design methodologies used in this field will be capable of making a good design. The training environments are provided as a fully self contained virtual machine with everything needed already to create the environments. We've had some persisting issues with the environments becoming unavailable, but support has been responsive when these issues arise and straightening them out for us
Make sure that any custom tables that you have, are built into your metadata packages. You can still access them via SQL queries in Cognos, but it is much easier to have them as a part of the available metadata packages.
The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.
Power BI is stronger for quick ad-hoc analysis and dashboards, but IBM Cognos Analytics is better when consistency, precision, and mass distribution matter. Tableau is best for interactive analysis, while IBM Cognos Analytics is better for standardized, repeatable enterprise reporting. Sigma shines for customizable dashboards and drill-down analysis while IBM Cognos Analytics holds an edge in data discovery and visualization.
Teradata is way ahead of its competitor because of its unique features of ensuring data privacy and data never gets corrupted even in worst case scenario. In most cases, the data corruption is a major issue if left unused and it leads to important data being wiped off which in ideal case should be stored for 3 years
The Cognos architecture is well suited for scalability. However, the architecture must be designed with scalability in mind from day one of the implementation. We recently upgraded from 10.1 to 10.2.1 and took the opportunity to revamp our architecture. It is now poised for future growth and scalability.
Moving to Teradata in the Cloud-enabled a level of agility that previously didn't exist in the organization. It also enabled a level of analytic competency that was not achievable using other options on the aggressive timeline that was required. We didn't want to settle for reinventing a wheel when we had a super tuned performance capable beast readily available in Teradata. Teradata lets us focus on our business rather than spending money and effort trying to design software or database foundations features on an open source or lower performance platform.
