Qrvey vs. Teradata Vantage

For our use case, its hands down the ability to embed visualizations onto our platform. We have an intelligence platform that provides different types of insights that follow a specific user flow. We need a platform that could not only visualize our data but help automate our data processing workflows. Other analytics platforms that we have used in the past make it extremely difficult to do the same thing.

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.

• The platform deploys to our AWS instance which gave us the ability to manage ourselves
• The feature of easily embedding visualizations into other applications
• The powerful features like workflow automation

• ETL (Extract - Transfor - Load)
• NOS to send data from Teradata Vantage to S3 and from S3 to Teradata Vantage
• Teradata GeoSpacial feature
• Bulk reading and writing in huge tables
• MPP capacity already mature
• Temporal Capacity more mature that other solutions
• TASM

Incentivized

• Merges between heterogenous datasets can occasionally be problematic
• The dashboards/pages are already very powerful, but more interactive elements would always be welcome.
• The administration of a Qrvey instance and underlying datastore can sometimes be complex, but the Admin app helps make this a smooth process.

Incentivized

• Teradata can improve by supporting more native AWS cloud features. Currently if a node goes down the EC2 instance must be restarted. It isn't something that happens frequently but more tight integration with cloud providers like AWS and Azure will allow Teradata to offer truly dynamic scaling.
• Some Teradata features are oversold before they are ready for prime-time. Teradata is not unique in this but if something is sold as an integrated product stack it should really be integrated not something that requires an extensive development cycle to be integrated at a customer's expense. If something is supported it should've really be tested and QAed thoroughly before a customer touches it.

Incentivized

Teradata is a mature RDBMS system that expands its functionality towards the current cloud capabilities like object storage and flexible compute scale.

Incentivized

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.

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.

Incentivized

Qrvey isn't just an analytics tool or a survey tool. It's a one-stop-shop for surveys, analytics, and automation. If you need all or most of this functionality, Qrvey can be a better solution than standing up three tools and getting them to work well together. Looking at just analytics, Qrvey is excellent for embedded analytics on AWS. If you need to put a chart or a chart/dashboard builder in your website or web app, Qrvey makes this process very straightforward.

Incentivized

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

Incentivized

• In one implementation, Qrvey helped our customer quickly achieve feature parity with competitors, reducing churn.

Incentivized

• Teradata is been absolutely phenomenal for our project because we feed huge chunks of data to it and get back the desired results in no time which earlier used to take hours to process and then also sometimes timeout.
• We don't have to do any manual intervention for resource or task allocation, it is all taken care by Teradata internally and all the AMP's are given equal amount of work and have their own resources to complete them with no sharing with another.

Incentivized